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Background: Real‐world problems are important in math instruction, but they do not necessarily trigger students' task motivation. Personalizing real‐world problems by (1) matching problems to students' shared living environment (context personalization) and (2) asking students to pose their own problems (active personalization) might be two interventions to increase students' task motivation. Aim: In the current study, we investigated the effects of context personalization and active personalization on students' self‐efficacy expectations, intrinsic value, attainment value, utility value, and cost. Sample: The participants were 28 fifth‐ and sixth‐grade students who voluntarily took part in a six‐month afterschool program in which they posed problems with the aim of creating a math walk in their hometown. Method: Using a within‐subjects design, at the end of the afterschool program, the students rated their self‐efficacy expectations and task values for four self‐developed problems associated with their hometown, four peer‐developed problems associated with their hometown, and four instructor‐provided problems associated with unfamiliar locations. Results: Students reported higher self‐efficacy expectations, intrinsic value, attainment value, and utility value for active‐personalized than non‐personalized problems. To a lesser extent, context personalization promoted intrinsic value and attainment value. No effect was found for cost. Conclusions: Active personalization (i.e. asking students to pose their own real‐world problems) is suited to enhance students' task motivation, specifically their self‐efficacy expectations, intrinsic value, attainment value, and utility value. Context personalization still boosts students' intrinsic value and attainment value. Implementation in classroom instruction is discussed. [ABSTRACT FROM AUTHOR]

This study proposed a three-tier test to help students learn English grammar. To reduce students' anxiety, game-based learning was incorporated into the three-tier test, where personalization was also implemented to accommodate students' different needs. More specifically, we developed a Personalized Entertaining Three-Tier Test (PET3), which consisted of the three-tier test, game-based learning and personalization. On the other hand, diversities exist among learners, especially prior knowledge. To this end, this study aimed to provide the complete understandings of the effects of prior knowledge in the PET3 context, in terms of test performance, task performance, learning behavior and gaming behavior. Regarding test performance, high prior knowledge learners (HPK) performed better than low prior knowledge learners (LPK). Regarding task performance, HPK was superior to LPK in two aspects, i.e., the reasons of the main questions and advanced questions, while LPK performed better than HPK in the aspect of the review questions. Regarding learning behavior, HPK and LPK favored to use different hints. Regarding gaming behavior, HPK tended to be a winner while LPK had a tendency to be looser. Based on these findings, we developed a framework, which could help designers and instructors to know the preferences of diverse learners. [ABSTRACT FROM AUTHOR]

With the rapid advancement of artificial intelligence (AI) technology, the advertising industry is at a crossroads of new opportunities and challenges. This pioneering study provides an in-depth review of AI's application in advertising, focusing especially on four key elements: Targeting, Personalization, Content Creation, and Ad Optimization. By delving deep into these areas, we uncover the potential of AI in revolutionizing the advertising sector. At the same time, we discuss the pressing ethical issues arising from the current applications of AI in advertising-related fields. Using the VOSviewer software, this study conducts an in-depth analysis of the literature, revealing the intrinsic connections of these four key elements in AI advertising based on computational advertising: Targeting and Personalization are closely linked, jointly determining who gets shown which advertisements. Content Creation generates appealing advertising content through AI during the Personalization process, while Ad Optimization relies on the outcomes of the first three elements, adjusting ad displays to achieve the highest return on investment. This research offers a fresh perspective on understanding AI's application in advertising, aiding in the responsible and effective use of AI technology for superior ad delivery. [ABSTRACT FROM AUTHOR]

Personalization on digital platforms drives a broad range of harms, including misinformation, manipulation, social polarization, subversion of autonomy, and discrimination. In recent years, policy makers, civil society advocates, and researchers have proposed a wide range of interventions to address these challenges. This Article argues that the emerging toolkit reflects an individualistic view of both personal data and data-driven harms that will likely be inadequate to address growing harms in the global data ecosystem. It maintains that interventions must be grounded in an understanding of the fundamentally collective nature of data, wherein platforms leverage complex patterns of behaviors and characteristics observed across a large population to draw inferences and make predictions about individuals. Using the lens of the collective nature of data, this Article evaluates various approaches to addressing personalization-driven harms under current consideration. It also frames concrete guidance for future legislation in this space and for meaningful transparency that goes far beyond current transparency proposals. It offers a roadmap for what meaningful transparency must constitute: a collective perspective providing a third party with ongoing insight into the information gathered and observed about individuals and how it correlates with any personalized content they receive across a large, representative population. These insights would enable the third party to understand, identify, quantify, and address cases of personalization-driven harms. This Article discusses how such transparency can be achieved without sacrificing privacy and provides guidelines for legislation to support the development of such transparency. [ABSTRACT FROM AUTHOR]

With the increasing implementation of algorithms across various news platforms, understanding news consumers' subjective perceptions of algorithmic-based news recommender systems has become critical. A between-subjects experiment (News Recommender System type: content-based filtering vs. collaborative filtering vs. human editorial choice-based recommender system) with 161 participants revealed that participants tended to trust the collaborative filtering system and perceive news recommended by the system to be more credible and less biased compared to editorial choices-based or content-based recommender systems – due to the triggering of the homophily heuristic – even though the three systems recommended the same set of news. Implications were discussed. [ABSTRACT FROM AUTHOR]

Background: The use of serious games in health care is on the rise, as these games motivate treatment adherence, reduce treatment costs, and educate patients and families. However, current serious games fail to offer personalized interventions, ignoring the need to abandon the one-size-fits-all approach. Moreover, these games, with a primary objective other than pure entertainment, are costly and complex to develop and require the constant involvement of a multidisciplinary team. No standardized approach exists on how serious games can be personalized, as existing literature focuses on specific use cases and scenarios. The serious game development domain fails to consider any transfer of domain knowledge, which means this labor-intensive process must be repeated for each serious game. Objective: We proposed a software engineering framework that aims to streamline the multidisciplinary design process of personalized serious games in health care and facilitates the reuse of domain knowledge and personalization algorithms. By focusing on the transfer of knowledge to new serious games by reusing components and personalization algorithms, the comparison and evaluation of different personalization strategies can be simplified and expedited. In doing so, the first steps are taken in advancing the state of the art of knowledge regarding personalized serious games in health care. Methods: The proposed framework aimed to answer 3 questions that need to be asked when designing personalized serious games: Why is the game personalized? What parameters can be used for personalization? and How is the personalization achieved? The 3 involved stakeholders, namely, the domain expert, the (game) developer, and the software engineer, were each assigned a question and then assigned responsibilities regarding the design of the personalized serious game. The (game) developer was responsible for all the game-related components; the domain expert was in charge of the modeling of the domain knowledge using simple or complex concepts (eg, ontologies); and the software engineer managed the personalization algorithms or models integrated into the system. The framework acted as an intermediate step between game conceptualization and implementation; it was illustrated by developing and evaluating a proof of concept. Results: The proof of concept, a serious game for shoulder rehabilitation, was evaluated using simulations of heart rate and game scores to assess how personalization was achieved and whether the framework responded as expected. The simulations indicated the value of both real-time and offline personalization. The proof of concept illustrated how the interaction between different components worked and how the framework was used to simplify the design process. Conclusions: The proposed framework for personalized serious games in health care identifies the responsibilities of the involved stakeholders in the design process, using 3 key questions for personalization. The framework focuses on the transferability of knowledge and reusability of personalization algorithms to simplify the design process of personalized serious games. [ABSTRACT FROM AUTHOR]

Current definitions of friction in user experience generally advocate for reducing or eliminating friction in the pursuit of efficiency and ease of use. As the field of user experience design increasingly aims to develop highly personalized experiences for unique individuals, this narrow view of friction risks the vital role that friction plays in human life. This paper compares definitions of friction found in other fields and examines ways in which friction shows up in our day-to-day lives to suggest a working definition of friction that clarifies how the concept can be applied to the experience design challenges of tomorrow. To meet these challenges, researchers and designers need to shift from reflexively eliminating friction to intentionally designing friction, which requires asking deeper questions about human needs and goals in addition to examining how we might help people accomplish tasks quickly and easily. [ABSTRACT FROM AUTHOR]

In this article, we propose a Latent Dirichlet Allocation-(LDA) based topic-graph probabilistic personalization model for Web search. This model represents a user graph in a latent topic graph and simultaneously estimates the probabilities that the user is interested in the topics, as well as the probabilities that the user is not interested in the topics. For a given query issued by the user, the webpages that have higher relevancy to the interested topics are promoted, and the webpages more relevant to the non-interesting topics are penalized. In particular, we simulate a user's search intent by building two profiles: A positive user profile for the probabilities of the user is interested in the topics and a corresponding negative user profile for the probabilities of being not interested in the the topics. The profiles are estimated based on the user's search logs. A clicked webpage is assumed to include interesting topics. A skipped (viewed but not clicked) webpage is assumed to cover some non-interesting topics to the user. Such estimations are performed in the latent topic space generated by LDA. Moreover, a new approach is proposed to estimate the correlation between a given query and the user's search history so as to determine how much personalization should be considered for the query. We compare our proposed models with several strong baselines including state-of-the-art personalization approaches. Experiments conducted on a large-scale real user search log collection illustrate the effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

Today, e-commerce has become a competitive space for online retailers. Therefore, personalization has become a vital part of e-commerce websites' success, challenging marketers and researchers. This study aims to provide a model for web personalization and mining user interests using a hybrid web usage and web content mining approach. The navigational patterns of web users and the interests of each user on web pages of a Persian website were extracted through web usage mining and topic modeling. Users were then clustered using the dependency distribution algorithm, and 25 categories were extracted. To better understand the behavioral patterns of web users they were categorized using the Support Vector Machine algorithm based on the users' interests and navigational behaviors. The most important result of the proposed system is that the patterns of users' navigation are understandable, and the subsequent analyses will be much simpler. [ABSTRACT FROM AUTHOR]

Diversity is a crucial precondition for a democratic public discourse. In today's high-choice media environments, exposure to diverse news is largely determined by individuals' personal selection. Yet these decisions are increasingly shaped by online platforms, whose curation mechanisms may serve to expand or contract the diversity of encountered content. In a major extension of existing research, we show that positive short-term effects of platforms mask detrimental long-term effects. Drawing on a four-month tracking dataset and a comprehensive content analysis covering the online news consumption of over 10,000 German citizens, we demonstrate that even though short-term usage of platforms uniformly increases exposure diversity, long-term reliance can lead to decreases. In addition, platforms vary in their influences: News aggregators are beneficial to exposure diversity, while Twitter and search engines have a limiting effect; Facebook offers no significant influence. [ABSTRACT FROM AUTHOR]

Web personalization, e.g., recommendation or relevance search, tailoring a service/product to accommodate specific online users, is becoming increasingly important. Inductive personalization aims to infer the relations between existing entities and unseen new ones, e.g., searching relevant authors for new papers or recommending new items to users. This problem, however, is challenging since most of recent studies focus on transductive problem for existing entities. In addition, despite some inductive learning approaches have been introduced recently, their performance is sub-optimal due to relatively simple and inflexible architectures for aggregating entity's content. To this end, we propose the inductive contextual personalization (ICP) framework through contextual relation learning. Specifically, we first formulate the pairwise relations between entities with a ranking optimization scheme that employs neural aggregator to fuse entity's heterogeneous contents. Next, we introduce a node embedding term to capture entity's contextual relations, as a smoothness constraint over the prior ranking objective. Finally, the gradient descent procedure with adaptive negative sampling is employed to learn the model parameters. The learned model is capable of inferring the relations between existing entities and inductive ones. Thorough experiments demonstrate that ICP outperforms numerous baseline methods for two different applications, i.e., relevant author search and new item recommendation. [ABSTRACT FROM AUTHOR]

The tremendous growth of services available on the Internet makes user profiling an increasing important topic in web personalization. While profiling a user, one of the main challenges is that user preferences and interests usually change over time. It would be much appreciated, if the user profile could describe these changes. Such user profile is defined as time-aware user profile (TUP) in this paper. The TUP can delineate both dynamic user preferences and interests and evolutions of them, which illustrates that it can represent users more clearly and accurately. A TUP consists of a set of time slot-specific user profiles, which is defined as sub-time-aware user profile (sTUP). And a TUP is special for a user; then, its sTUPs separately represent preferences and interests of this user for different time slots. Constructing a TUP needs the constructions of its sTUPs. While constructing a sTUP from the user behavior logs for a time slot, in this paper, we present a novel learning model called personal service ecosystem (PSE) to delineate user preferences and interests naturally. Different from PSE, sTUP is the representation model. For a time slot, the sTUP is constructed based on the PSE recovered from the user behavior logs of this time slot. In terms of a TUP, two main approaches to analyze the evolutions of sTUPs in this TUP are further discussed. They are the evolution analysis of various margins between every two neighboring sTUPs called EA-VM and the evolution analysis of different sTUPs on raw data defined as EA-RD. EA-VM mainly focuses on the fine-grained evolution between two neighboring time slots by analyzing these various margins. It can be further divided into the clustering method and the distribution analysis approach. EA-RD chiefly pays attentions to the overall evolution of such sTUPs. By a comprehensive survey on the raw data of sTUPs, we summarize six interesting overall evolution patterns, including refugee pattern, periodic pattern, stable pattern, fluctuant pattern, emergency pattern and zombie pattern. Eventually, a systematic empirical study based on our collection data has been conducted, where smartphone users are taken as examples to illustrate our proposed models and approaches. And experimental results highlight the superiority of these approaches. [ABSTRACT FROM AUTHOR]

To compete for attention in today's online environment, webtexts must appeal not only to human audiences, but also to the search engines that rank them via a results page, driving traffic their way (or not). This article investigates the rhetorical dynamics of this fundamental dimension of contemporary online discourse, viewing search engines as a pivotal nexus between scholarly conversations regarding algorithmic rhetoric and attention management. Specifically, it positions the algorithm as an audience, one webtexts invoke via subtextual signals meant to elude human notice. It is animated by Charles E. Morris's formulation of the fourth persona, drawing on his framework to delineate a technologically oriented persona 4.0. In doing so, this paper broadens rhetoric's long-standing repertoire of constructs pertaining to persona to include algorithmic address. It then takes web searches for "public speaking," and the top-ranking page by the motivational speaker Brian Tracy, as a site through which to explore the dynamics of persona 4.0, thereby also highlighting some of the disciplinary stakes. [ABSTRACT FROM AUTHOR]

Many online information systems are delivering personalized news to users today. The essence of this personalization process is to match a news article to the reader's self-identity. However, prior studies mostly focus on matching a positive news story to a person's desired identity. No known research has discussed the possibility of matching a negative news story to a person's undesired identity. This study aims to fill this theoretical gap by testing a three-way interaction effect among news valence, identity desirability, and uniqueness on attitude toward the news story. Through a 2 × 2 × 2 between-subjects experiment, it is shown that a positive news story tends to generate a more favorable attitude when matched to the reader's desired self-identity, whereas a negative news story will generate a more favorable attitude when matched to the person's undesired self-identity, and such an effect is especially pronounced when the identity is unique. Perceived news credibility is found to partially mediate these effects. [ABSTRACT FROM AUTHOR]

The article is devoted to the problem of formation of a new type of cognitive practices in mixed reality, where the process of cognition is mediated by digital artifacts. Changes, which have to do with the interaction of the cognizing subject with smartphones and artificial intelligence at the level of daily life are actualized through various manifestations of 'gnoseological omniscience', absorption in 'garbage content' and the phenomenon of constant connection to the Web. The article demonstrates the heuristic nature of the question of personalization as a necessary factor in the formation of the web-mind. [ABSTRACT FROM AUTHOR]

In order to meet user expectations and to optimize user experience with a higher degree of flexibility and sustainability, the Smart product-service system (Smart PSS), as a novel value proposition paradigm considering both online and offline smartness, was proposed. However, conventional manners for developing PSS require many professional consultations and still cannot meet with the new features of Smart PSS, such as user context-awareness and ever-evolving knowledge management. Therefore, aiming to assist Smart PSS development cost-effectively, this paper adopted the knowledge graph (KG) technique and concept-knowledge (C-K) model to propose an evolutionary design approach. Two knowledge graphs are firstly established with open-source knowledge, prototype specifications, and user-generated textual data. Then, triggered by personalized requirements, four KG-aided C-K operators are conducted based on graph-based query patterns and computational linguistics algorithms, thus generating innovative solutions for evolving Smart PSS. To validate the performance of the proposed approach, a case study of a smart nursing bed fulfilling multiple personalized requirements is conducted, and the evaluation result of its knowledge evolution is acceptable. It hopes that this work can offer insightful guidance to industrial organizations in their development of Smart PSS. [ABSTRACT FROM AUTHOR]

The introduction of artificial intelligence in education (AIED) is likely to have a profound impact on the lives of children and young people. This article explores the different types of artificial intelligence (AI) systems in common use in education, their social context and their relationship with the growth of commercial knowledge monopolies. This in turn is used to highlight data privacy rights issues for children and young people, as defined by the 2018 General Data Protection Regulations (GDPR). The article concludes that achieving a balance between fairness, individual pedagogic rights (Bernstein, 2000), data privacy rights and effective use of data is a difficult challenge, and one not easily supported by current regulation. The article proposes an alternative, more democratically aware basis for artificial intelligence use in schools. [ABSTRACT FROM AUTHOR]

This conceptual article discusses the role of digital literacies in personalized books, in relation to children's developing sense of self, and in terms of assessing the potential impact of artificial intelligence (AI). Personalized books contain children's data, such as their name, gender or image, and they can be created by readers or automatically by the publisher. Some personalized books are e-books enhanced with artificial intelligence, and some can be ordered as paperbacks. We discuss this use of children's personal data in terms of the social location of the self with regard to subjective and objective dimensions. We draw on a map metaphor, in which objective space requires readers to locate themselves in an unknown 'A-to-B' space and subjective space provides an individually oriented world of 'me-to-B'. By drawing on examples of personalized books and their use by parents and young children, we discuss how personalization troubles the borders between readers' me-to-B and A-to-B space experiences, leading to possible confusion in the sense of self. We conclude by noting that AI-enhanced personalized texts can reduce personal agency with respect to formulating a sense of identity as a child. [ABSTRACT FROM AUTHOR]

Social media platforms allow people to exert some control over their news feeds, even though not everyone utilizes the relevant features. This article conceptualizes consumptive news feed curation on social media as a special type of proactive personalization in news consumption. Survey data were analyzed to examine the extent and correlates of the practice in six East Asian societies. The findings indicated that East Asian news consumers do not frequently engage in consumptive news feed curation on social media. Engagement in relevant practices was consistently related to age, interest in the news, news participation, and news avoidance. Overall, the findings suggest that consumptive news feed curation is employed primarily by young people to navigate the sea of information of uneven quality. [ABSTRACT FROM AUTHOR]

Purpose: Many online merchants today have adopted web personalization in the form of personalized product recommendations (PPRs) to improve consumer's decision quality. The purpose of this paper is to reveal the roles of PPRs on consumer decision quality in online shopping from the theoretical perspective of information load. Design/methodology/approach: To explore the dual roles of PPRs on consumer decision quality, this paper develops a research model for it. A 2 (information load: high vs low) × 2 (web personalization: PPRs vs non-PPRs) between-subjects design is conducted to empirically test the model. Findings: The results indicate that: first, information load can increase perceived information overload and decrease perceived information underload; second, PPRs can weaken (enhance) the positive (negative) effect of information load on perceived information overload (perceived information underload); third, both perceived information overload and perceived information underload are negatively associated with consumer's decision quality. Originality/value: This paper originally develops a research model that explains the roles of PPRs on consumer decision quality from the theoretical perspective of information load in the online shopping context, which could add new insights to the field of web personalization, especially the impact of web personalization on consumer decision making. [ABSTRACT FROM AUTHOR]

The aim of this study was to consider the activities and impact of personalisation in a UK higher education setting. It considered how personalisation has affected outputs in terms of progression, achievement, internship, enterprise project and international placement uptake within the Nottingham Business School at Nottingham Trent University. Qualitative and quantitative data were collected. Semi-structured interviews were carried out with members of the personalisation team and those delivering personalisation. The quantitative data pertains to outputs in terms of success rates, particularly with regard to when the personalisation project began, in 2015/16. The findings suggest that the personalisation process involved engagement, interaction and reflection to meet the needs and requirements of the learner; it also provided them with options. This contributes to improving success rates, particularly when the Personalisation project began. [ABSTRACT FROM AUTHOR]

Background: Connected health devices and applications (referred to hereafter as "SDApps" - Smart devices and applications) are being portrayed as a new way for prevention, with the promise of accessibility, effectiveness and personalization. Many effectiveness evaluations (experimental designs) with strong internal validity exist. While effectiveness does appear to vary, the mechanisms used by these devices have not yet been thoroughly investigated. This article seeks to unpack this black box, and describes the process of elaboration of an intervention theory for healthy eating and physical activity SDApps. It includes a set of requirements relative to their impact on social health inequalities.Methods: To build this theory, we drew on theory-driven approaches and in particular on the theory of change (ToC) method. To this end, we developed a cumulative and iterative process combining scientific data from the literature with knowledge from experts (researchers and practitioners) and from patients or users. It was a 3-step process, as follows: 1 - identifying the evidence base; 2 - developing the theory through design intervention and creating realistic expectations, including in our case specific work on social health inequalities (SHIs); 3 - modeling process and outcome.Results: We produced an evidence-based theory according to the ToC model, based on scientific evidence and knowledge from experts and users. It sets out a causal pathway leveraging 11 key mechanisms - theoretical domains - with which 50 behavior change techniques can be used towards 3 ultimate goals: Capacity, Opportunity, Motivation - Behavior (COM-B). Furthermore, the theory specifically integrates requirements relative to the impact on SHIs.Conclusions: This theory is an aid to SDAapp design and evaluation and it can be used to consider the question of the possible impact of SDApps on the increase in inequalities. Firstly, it enables developers to adopt a more overarching and thorough approach to supporting behavior change, and secondly it encourages comprehensive and contributive evaluations of existing SDApps. Lastly, it allows health inequalities to be fully considered. [ABSTRACT FROM AUTHOR]

Mobile networks transformation into the main support for Internet access since last decade made Apple and Alphabet the most lucrative companies in a market where they were not present until then. The reinterpretation of platform model as multilateral markets where the value of service providers tends to increase, largely relinquishing control over supply, relegated telecommunications operators to access providers role in a market built on their own networks. The evolution towards the 5G could reverse this tendency to concentration by opening new markets for operators in the industrial sector within the Internet of Things (IoT), but also deepening the personalization of the offer to the user. This article analyzes the main milestones in the evolution of mobile networks, the strategies employed by Apple and Alphabet to position themselves as leaders in the most profitable niches, the basic characteristics of 5G and why this system can be an opportunity for operators to overcome the current duopoly. [ABSTRACT FROM AUTHOR]

Web personalization is a process that utilizes a set of methods, techniques, and actions for adapting the linking structure of an information space or its content or both to user interaction preferences. The aim of personalization is to enhance the user experience by retrieving relevant resources and presenting them in a meaningful fashion. The advent of big data introduced new challenges that locate user modeling and personalization community in a new research setting. In this paper, we introduce the research challenges related to Web personalization analyzed in the context of big data and the Semantic Web. This paper also introduces some models and approaches that can bridge the gap between the two. Future challenges and opportunities related to Web personalization, analyzed from the big data and Semantic Web perspective, are also presented. The research challenges outlined in this paper involve the scrutability of user models in personalization, generic personalization, meta-personalization, open corpus personalization, and semantic data modeling. [ABSTRACT FROM AUTHOR]

Today it is practically difficult to recover data with a catchphrase pursuit when the data is spread more than a few pages. Semantic Web is a growth of the present web in which data is given well characterized meaning and web personalization is the one of the utilization of the semantic web. In this we have investigated correlation of different community oriented sifting procedures and filtering methodologies were induced to get the exact result for the given queries. In our work we have proposed an image based content retrieval for the given search query from the enduser. The user identification are taken in to consideration to frame the search using the community oriented sifting mining for their desires and the same will be mapped with the image to give out the exact content in terms of text and images. Those systems are memory based, model based and cross breed communitarian sifting. Our review demonstrates that the execution of half and half communitarian separating method is superior to anything memory based and show based collective sifting strategy. We have introduced standardization step, which will enhance precision of conventional community oriented sifting procedures. One of the intense personalization advances controlling the versatile web is synergistic separating. Collaborative community oriented sifting (CCOS) is the way toward separating or assessing things through the sentiments of other individuals. CCOS innovation unites the suppositions of huge interconnected groups on the web, supporting separating of significant amounts of information. [ABSTRACT FROM AUTHOR]

This article discusses the mediatization of politics and its theorization as a process of transformation in the making of (political) meaning through three different theses, presented as evolutionist, intended, and imagined transformation. These theses differ from each other not as much on what they describe as meaning-making transformation – the personalization, conversationalization, and dramatization of politics – as on what they consider to be the causes, extent, and consequences of this transformation. By examining their differences, the article argues that mediatization cannot be fully explained with reference either to a single-universal media logic (as in the thesis of evolutionist transformation) or actor-perceived media logics (as in the thesis of intended transformation). It is seen (in the thesis of imagined transformation), instead, as being catalyzed by the imaginary of media omnipresence, the overwhelming sense that media are everywhere, and therefore potential media effects must be anticipated, which intensifies the fusion of public with private spheres of political life. At the same time, this private-public fusion takes place through existing, institutionalized practices of media performativity, such as the performativity of charisma (personalization), ordinariness (conversationalization) and spectacle (dramatization), bearing implications for the exercise of power and democratic practice in our societies. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop a conceptual framework for assessing what are the possibilities and pitfalls of using algorithmic systems of news personalization – i.e. the tailoring of individualized news feeds based on users' information preferences – for constructive conflict coverage in the context of peace journalism, a journalistic paradigm calling for more diversified and creative war reporting. Design/methodology/approach: The paper provides a critical review of existing research on peace journalism and algorithmic news personalization, and analyzes the intersections between the two concepts. Specifically, it identifies recurring pitfalls of peace journalism based on empirical research on constructive conflict coverage and then introduces a conceptual framework for analyzing to what degree these pitfalls can be mediated – or worsened – through algorithmic system design. Findings: The findings suggest that AI-driven distribution technologies can facilitate constructive war reporting, in particular by countering the effects of journalists' self-censorship and by diversifying conflict coverage. The implementation of these goals, however, depends on multiple system design solutions, thus resonating with current calls for more responsible and value-sensitive algorithmic design in the domain of news media. Additionally, our observations emphasize the importance of developing new algorithmic literacies among journalists both to realize the positive potential of AI for promoting peace and to increase the awareness of possible negative impacts of new systems of content distribution. Originality/value: The article particle is the first to provide a comprehensive conceptualization of the impact of new content distribution techniques on constructive conflict coverage in the context of peace journalism. It also offers a novel conceptual framing for assessing the impact of algorithmic news personalization on reporting traumatic and polarizing events, such as wars and violence. [ABSTRACT FROM AUTHOR]

In recent times, Recommender Systems (RSs) are gaining immense popularity with the wider adaptation to deal information overload problem in various application domains such as e-commerce, entertainment, e-tourism, etc. RSs are developed as information filtering systems to make personalized predictions based on the priorities and preferences for the suggestion of relevant items to users. Travel Recommender Systems (TRSs) generates a list of best matching locations or Point of Interests (POIs) to the users based their preferences. Predicting interesting locations for the generation recommendations from Location Based Social Network (LBSN) is crucial due to variety, size, and dimensions of data. The growing demand for effective TRS extends the scope for the development of user behavior based recommendation approach. In the literature, several research works are conducted to generate location recommendations by focusing on location attributes and failed to incorporate user behavior. As a significant solution to the existing limitations of TRSs, we propose Activity and Behavior induced Personalized Recommender System (ABiPRS) as a hybrid approach to predict persuasive POI recommendations. The proposed ABiPRS is designed to support travelling user by providing effective list of POIs as recommendations. As an extension, we have designed a new group recommendation model to meet the requirements of the group of users by exploiting relationships between them. Further, we have developed a novel hybridization approach for aggregating recommendations from multiple RSs to improve the effectiveness of recommendations. The proposed approaches are evaluated on the real-time large-scale datasets of Yelp and TripAdvisor. The experimental results depict the improved performance of the proposed hybrid recommendation approach over standalone and baseline hybrid approaches. [ABSTRACT FROM AUTHOR]

Recent advancements in web personalization techniques facilitate enhanced web-based services that allow recommender systems (RSs) to incorporate contextual knowledge about users and items as an additional dimension into recommendation process. Context-awareness is one of the important aspects of ubiquitous computing to support cognitive environment and provide services in various e-commerce recommendation applications. Tracking each user's preferences over various contextual dimensions from their past transactions and providing personalized recommendations to them are the essence of context-aware recommender systems (CARSs). Conventional paradigms for incorporating context in recommendation process cannot fully cover the challenges on several levels of a context-aware system. Our proposed scheme is based on the hybridization of two complementary techniques, collaborative filtering (CF) and reclusive method (RM) to make context valuable at each level of users' preferences and improve predictive capability of CARSs. Further, a fuzzy real-coded genetic algorithm (Fuzzy-RCGA) approach is incorporated for identifying the influential contextual situations and handling the uncertainty of users' preferences under various contextual situations. Furthermore, users' demographic features are utilized for alleviating the problem of data sparsity. The empirical results on two real-world benchmark datasets clearly demonstrate the effectiveness of our proposed schemes for CARS framework. [ABSTRACT FROM AUTHOR]

Best practices in mail survey design advise personalizing correspondence, but most research predates the use of address-based sampling (ABS) in which a householder's name either cannot be matched to an address or may be matched incorrectly. Further, recent evidence casts doubt on the effectiveness of personalization. The current study examines the impact of using a personalized versus a generic salutation on response rates and data quality in an ABS mail survey of the general population. A sample of 2,000 household addresses in Wisconsin was randomly selected from the U.S. Postal Service Delivery Sequence File. For households in the personalized salutation group, all materials used the surname of the household; cases in the generic salutation group referred to the state conducting the survey. While personalization was not related to response rates, it was associated with higher levels of item nonresponse. Findings support current recommendations not to use names in ABS mail surveys. [ABSTRACT FROM AUTHOR]

Our many online routines leave behind trails of data about our identities, habits, preferences and connections. These data serve as filters when we seek out information, yielding relevant results and content of interest. However, commercial and political parties can use the same data to personalize persuasive messages, and some even use psychological profiles to target individuals. With this revelation come concerns that news can be framed to appeal to individual personalities. This study investigates the relationship between personality and news engagement among predominantly young Norwegian adults across different news angles. It addresses the Big Five personality traits as well as rational and experiential information-processing styles. The results provide support for our hypothesis on the relation between neuroticism and lowered news engagement, although the effect sizes are small. When exploring isolated news stories, we find greater differentiation among the participants, suggesting that individuals' news interest really does start at the headline. [ABSTRACT FROM AUTHOR]

Abstract Elites in the U.S. have become increasingly polarized over the past several decades. More recently, the degree to which partisans view the opposing party more negatively than their own — a phenomenon called affective, or social, polarization — has increased. How does elite polarization inform affective polarization? We argue that partisans respond to increasing levels of elite polarization by expressing higher levels of affective polarization, i.e. more negative evaluations of the opposing party relative to their own. Motivated reasoning further encourages partisans to blame the opposing party more than their own. Results from surveys collected from 1978 through 2016 provide strong support for our theory. We further find that increasing levels of political interest magnify the relationship between elite and affective polarization. These results produce important implications for the health of democratic systems experiencing high levels of elite polarization. [ABSTRACT FROM AUTHOR]

Abstract Although recent research on the personalization of politics has provided empirical evidence of leadership effects on voting behaviour, previous studies have dealt primarily with an impact on vote choice, neglecting the primary step of the voting decision process: turnout. Exploring the relationship between leader effects and turnout gains relevance considering the generalized decline in voter turnout rates across Western democracies as a symptom of the dealignment process, pointed as a key cause of the personalization of politics. Despite this theoretical linkage, it is yet unstudied whether voters' evaluations of leaders have an effect on turnout. This study attempts at assessing the impact of voters' evaluations of political leaders on turnout decisions using data from 25 countries, derived from the Comparative Study of Electoral Systems. Results show a positive significant effect of voters' evaluations of candidates on turnout. Moreover, this effect was especially strong amongst dealigned voters. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we investigate the effect of incumbency in three different electoral settings in South Korea using a regression discontinuity (RD) design. We claim that incumbency provides advantages in local elections, but its positive effects diminish in national legislative elections. Local politicians have an opportunity to develop close ties with their constituents, insulating themselves from national politics; but, due to the centralized nature of national politics, members of the National Assembly do not. Consistent with our expectation, incumbency effects are generally positive in local elections, but insignificant or even negative in national legislative elections. We also find that politicians who previously held local elected office enjoy an incumbency advantage in national legislative elections. [ABSTRACT FROM AUTHOR]

Abstract: This article analyses what makes political candidates run a party‐focused or personalised election campaign. Prior work shows that candidates face incentives from voters and the media to personalise their campaign rhetoric and promises at the expense of party policy. This has raised concerns about the capacity of parties to govern effectively and voters’ ability to hold individual politicians accountable. This article builds on the literature on party organisation and considers the possible constraints candidates face from their party in personalising their election campaigns. Specifically, it is argued that party control over the candidate nomination process and campaign financing constrains most political candidates in following electoral incentives for campaign personalisation. Using candidate survey data from the 2009 EP election campaign in 27 countries, the article shows how candidates from parties in which party officials exerted greater control over the nomination process and campaign finances were less likely to engage in personalised campaigning at the expense of the party programme. The findings imply that most parties, as central gatekeepers and resource suppliers, hold important control mechanisms for countering the electoral pressure for personalisation and advance our understanding of the incentives and constraints candidates face when communicating with voters. The article discusses how recent democratic reforms, paradoxically, might induce candidate personalisation with potential negative democratic consequences. [ABSTRACT FROM AUTHOR]

Web personalization has been widely adopted in the mobile shopping context to improve shopping experience and consumer loyalty. Timing is a crucial issue of the implementation of web personalization, a topic not yet adequately investigated in the extant literature. Web personalization could classify into two forms, namely Before-search Web Personalization (BSWP) and After-search Web Personalization (ASWP), in the mobile shopping context. Besides, motivations are the critical factors in mobile shopping, and seldom research has studied the effects of motivation on consumer's use of web personalization in the mobile shopping context. To advance understanding of the timing issue of web personalization, this paper employs the Uses and Gratifications method to investigate how mobile shopping motivations influence consumers' use of BSWP and ASWP, respectively. Furthermore, this paper also investigates the role of an important personal disposition (i.e. need for cognition). Empirical results indicate that shopping motivations could have significant effects on web personalization use; furthermore, BSWP use and ASWP use are predicted by different shopping motivations. Besides, need for cognition is found to positively moderate some effects of mobile shopping motivations on web personalization use. Theoretical and practical implications are also discussed. [ABSTRACT FROM AUTHOR]

By tracking consumers’ browsing and purchase history, web personalization generates taste-matched recommendations for each consumer to stimulate purchases. In addition to taste-matching, mobile personalization matches recommendations to a consumer’s physiological need and current location. These two additional features, referred to as need-matching and location-matching, are believed to be enablers of unplanned purchases. However, mobile advertisers may not be able to generate recommendations that meet all personalization criteria. Hence, mobile recommendations may be imperfect. We examine two questions in relation to imperfect recommendations. First, how do we use a descriptor to promote such recommendations? Second, what personalization criterion should be downplayed to induce unplanned purchases? Drawing upon the theory of mood congruence, we theorize that the effect of imperfect recommendation on consumers’ unplanned purchases depends on their mood. We conducted three field experiments to test our hypotheses. Our findings indicate that (1) consumers in positive moods are more likely to form an urge to buy than those in negative moods, and this difference is larger when the descriptor is partial than when it is complete (Experiment 1); (2) need-matching is more influential on urge to buy for consumers in negative moods than for those in positive moods (Experiment 2); and (3) for taste-and-need-matched recommendations, location-matching exerts a stronger effect on the urge to buy for consumers in negative moods than for those in positive moods (Experiment 3). We validated the relevance of our research findings to practice through interviews with senior executives in personalization solution providers. Pathways for enhancing practical impacts of this line of research are recommended. [ABSTRACT FROM AUTHOR]

Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) represent a major challenge for health systems within the aging population. New and better instruments will be crucial to assess the disease severity and progression, as well as to improve its treatment, stimulation, and rehabilitation. With the purpose of detecting, assessing and quantifying cognitive impairments like MCI or AD, several methods are employed by clinical experts. Syndrom Kurztest neuropsychological battery (SKT) is a simple and short test to measure cognitive decline as it assesses memory, attention, and related cognitive functions, taking into account the speed of information processing. In this paper, we present a decision system to embed in a robot that can set up a productive interaction with a patient, and can be employed by the caregiver to motivate and support him while performing cognitive exercises as SKT. We propose two different interaction loops. First, the robot interacts with the caregiver in order to set up the mental and physical impairments of the patient and indicate a goal of the exercise. This is used to determine the desired robot behavior (human-centric or robot-centric, and preferred interaction modalities). Second, the robot interacts with the patient and adapts its actions to engage and assist him to complete the exercise. Two batches of experiments were conducted, and the results indicated that the robot can take profit of the initial interaction with the caregiver to provide a quicker personalization, and also it can adapt to different user responses and provide support and assistance at different levels of interaction. [ABSTRACT FROM AUTHOR]

Personalization in e-commerce increases sales by improving customer perception of site quality. However, some demographic data about customers (crucial for the success of the personalization process) not always can be obtained explicitly, as is the case of anonymous web site visitors. The paper describes a user study focused on determining whether it would be possible to categorize the age and gender of individual visitors of a web site through the automatic analysis of their behavior. Three tasks commonly found in e-commerce sites ( Point & Click, Drag & Drop and Item Selection ) were tested by 592 volunteers and their performance was analyzed using several different statistical methods. The study found consistencies in the execution times of individuals across the different tasks and revealed that age and gender are sufficiently determining factors to support an automatic profiling. Results also showed that relevant information about gender and age can be extracted separately through the individual analysis of each one of the mentioned interaction tasks. [ABSTRACT FROM AUTHOR]

Neighbor-based collaborative ranking algorithms exploit users’ pairwise preferences to predict how they will rank items. Current neighbor-based algorithms lie in the category of user-based recommendation methods: they calculate users’ similarities over pairwise preferences, estimate the unknown pairwise preferences, and finally, infer the ranking of items for the target user. However, it still is an open question how to adapt item-based recommendation for neighbor-based collaborative ranking. The more specific question is how to calculate items’ similarities in a pairwise preference dataset, and how these similarities can be employed to infer the total ranking of items for the target user. This paper presents a novel recommendation approach, PreNIt, that exploits preference networks for item-based collaborative ranking. PreNIt models the users’ pairwise preferences as two novel bipartite networks with labeled edges. These labeled edges enable us to model the choice context in which items are preferred/not preferred by the user. Once the networks are constructed, PreNIt finds the transitive similarities of items using a new personalized ranking algorithm in graphs with labeled edges. Experimental results shows the significant outperformance of PreNIt over the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The global approach in clinical situation requires an open listening on the complexity of the patient, its antecedents most distant and the most striking in order to approach more closely the specificity of its ground. Taking into account the theory of «constituent phases of the individual» or Ben Shen makes it possible to better situate the present clinical symptomatology and to associate very specific acupuncture points of the patient's history which can favor the personalization of the patient. treatment. [ABSTRACT FROM AUTHOR]

Purpose Many off-line retailers have experienced a slump in sales and have the potential risk of overstock or understock. To overcome these problems, retailers have applied data mining techniques, such as association rule mining or sequential association rule mining, to increase sales and predict product demand. However, because these techniques cannot generate shopper-centric rules, many off-line shoppers are often inconvenienced after writing their shopping lists carefully and comprehensively. Therefore, the purpose of this paper is to propose a personalized recommendation methodology for off-line grocery shoppers.Design/methodology/approach This paper employs a Markov chain model to generate recommendations for the shopper’s next shopping basket. The proposed methodology is based on the knowledge of both purchased products and purchase sequences. This paper compares the proposed methodology with a traditional collaborative filtering (CF)-based system, a bestseller-based system and a Markov-chain-based system as benchmark systems.Findings The proposed methodology achieves improvements of 15.87, 14.06 and 37.74 percent with respect to the CF-, Markov chain-, and best-seller-based benchmark systems, respectively, meaning that not only the purchased products but also the purchase sequences are important elements in the personalization of grocery recommendations.Originality/value Most of the previous studies on this topic have proposed on-line recommendation methodologies. However, because off-line stores collect transaction data from point-of-sale devices, this research proposes a methodology based on purchased products and purchase patterns for off-line grocery recommendations. In practice, this study implies that both purchased products and purchase sequences are viable elements in off-line grocery recommendations. [ABSTRACT FROM AUTHOR]