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Thematic content in the form of geodata is becoming increasingly available on the web. However, adequate base map information to support appropriate communication of the thematic content is not available in many cases. This base map information might be served through topographic databases available on the web and optimized by means of automated generalization. This article describes an approach based on user profiles, which formally captures the user requirements (preferences) towards the base map and deploys those profiles in a web-based architecture to generate on-demand maps. Those user profiles, which may differ per user, drive the generalization process and produce base maps on demand by taking into account the user requirements as well as the thematic content. The research in this article specifically focuses on the technological environment that generates on-demand base maps incorporating four basic concepts in the user profile: (1) topology awareness between the base map and the thematic content, (2) user-specific base map information at a non-uniform scale to improve the communication of the thematic content, (3) the map specification and (4) inheritance of user profiles. Both existing and new techniques are explored to realize the environment. The approach is evaluated by applying it to a project in the Netherlands dedicated to disseminating physical planning maps through the web. [ABSTRACT FROM AUTHOR]

Recommender systems are software frameworks that employ a specific type of information filtering technique, aiming at recommending information items or social elements that are likely to be of interest to the user. Herein, we present the evaluation results of the recently prototyped tourism recommender system (TRS). We followed a formal evaluation process to validate the usability of two versions of the TRS by users in realistic environments; the first is a typical web recommender system (wTRS), while the second is a system that enables tourist content recommendations addressed to mobile users (mTRS). The usability evaluation tests have been undertaken at the municipality of Mytilene, Greece. [ABSTRACT FROM AUTHOR]

The measures taken to adapt the ContentDm application to users' validation requirements are introduced, along with the collections in the Instituto Cervantes image bank creation project. The project's general characteristics are then laid out in a presentation. This is followed by a summary of the objectives and strategies inherent within the complex task of implementing a document manager which is suitable for both internal use to meet the needs of an institution's personnel and for the publishing of images on the internet. Finally, there is an account of the suggestions which have been put into practice to further tailor users' needs to the digital collection manager's characteristics. [ABSTRACT FROM AUTHOR]

Abstract: The difference between the actual and predicted energy consumption for heating in housing is thought to be partly attributable to the use of HVAC systems. More reliable data on energy consumption could help in determining the actual energy performance of dwellings and in the search for the most adequate design for housing and home amenities. Further reductions on energy consumption might also be achieved if energy-saving policy programmes were geared to different household groups. The aim of this paper is to statistically determine Behavioural Patterns associated with the energy spent on heating and to identify household and building characteristics that could contribute to the development of energy-User Profiles. This study had two outcomes: it identified Behavioural Patterns to be used in energy calculations and it discerned User Profiles with different behaviours. Five underlying groups of behavioural variables were found, which were used to define the Behavioural Patterns and User Profiles. The groups showed statistically significant differences in the scores for most of the behavioural factors. This study established clear relationships between occupant behaviour and household characteristics. However, it seems difficult to establish relationships between energy consumption and Behavioural Patterns and household groups. [Copyright &y& Elsevier]

With the dramatically quick and explosive growth of information available over the internet, World Wide Web has become a powerful platform to store, disseminate and retrieve information as well as mine useful knowledge. Web usage mining addresses the application of data mining techniques over web data in order to identify and characterise users' navigation behaviour patterns. The wide spectrum of uncertainties involved in the web navigation process can be modelled and handled using fuzzy set theory. Fuzzy web intelligent techniques are used in this paper to deal with uncertainty in web navigation patterns and for uncovering web user communities. Intelligent fuzzy clustering algorithm (IFC) is proposed in this work and the performance of IFC is compared with popular fuzzy C-means algorithm (FCM) and functional fuzzy C-means (FFCM) algorithm. [ABSTRACT FROM AUTHOR]

In recent days the amount of Information Available on the internet is huge when compared to past few decades. How to gather useful information from the web has become a challenging issue for users. Current web information gathering systems trying to satisfy user requirements by looking at their information needs. For this purpose, user profiles are created for user background knowledge description. User profiles represent the concept models possessed by users when gathering web information. A concept model is implicitly possessed by users and is generated from their background knowledge. While this concept model cannot be proven in laboratories, many web ontologists have observed it in user behaviour. When users read through a document, they can easily determine whether or not it is of their interest or relevance to them, a judgment that arises from their implicit concept models. If a user's concept model can be simulated, then a superior representation of user profiles can be built. To simulate user concept models, ontology's--a knowledge description and formalization model--are utilized in personalized web information gathering. Such ontology's are called ontological user profiles or personalized ontology's. To represent user profiles, many researchers have attempted to discover user background knowledge through global or local analysis. [ABSTRACT FROM AUTHOR]

Online social networks have attracted millions of users, who have integrated social network web sites into their daily life. Users participate to the changes and to the evolution of these sites because they are producers and reviewers of contents that help them to maintain the existing social relationships, make new friends, collaborate and enrich experiences. This paper presents a study of the characteristics of the users of MySpace web site, with the objective of studying relationships and interactions among users and deriving hints about their behavior. The analysis relies on data collected by monitoring the web site for 12 weeks. Typical user behaviors have been derived and classes of users characterized by different levels of participation to the social network have been identified. In particular, the analysis reveals that most of the users actively participate to the social network and specify many personal details. Social networks web sites allow access to such details; the sharing of information about users and their relationships can lead to non-ethic online activities, which threat the privacy and the security of users themselves. [ABSTRACT FROM AUTHOR]

Abstract: There are few formal studies on the contribution of botanical gardens as urban green spaces, particularly within developing countries. Therefore, this paper reports on an assessment of the use and appreciation of botanical gardens as urban green spaces in South Africa. Users and staff were surveyed in six national botanical gardens. The gardens provided numerous benefits in terms of conservation, education and recreation. However, the people using the gardens were not demographically representative of the general population of the surrounding city or town. Generally, most of the visitors were middle- to old-aged, well-educated professionals with medium to high incomes. Most were white and English was their home language. There was an even gender representation. Most visited only a few times per year. The majority of users visited the gardens for recreation and psychological reasons rather than educational ones. However, the staff of each garden placed emphasis on education in the gardens and amongst surrounding schools. Most visitors appreciated the conservation dimensions of botanical gardens, and felt that there was insufficient public green space in their town or city. Understanding how people perceive and use the botanical gardens of South Africa is important to inform future research and strategies regarding the conservation of urban green space within a developing country. [Copyright &y& Elsevier]

For projects in knowledge-intensive domains, it is crucially important that knowledge management systems are able to track and infer workers' up-to-date information needs so that task-relevant information can be delivered in a timely manner. To put a worker's dynamic information needs into perspective, we propose a topic variation inspection model to facilitate the application of an implicit relevance feedback (IRF) algorithm and collaborative filtering in user modeling. The model analyzes variations in a worker's task-needs for a topic (i.e., personal topic needs) over time, monitors changes in the topics of collaborative actors, and then adjusts the worker's profile accordingly. We conducted a number of experiments to evaluate the efficacy of the model in terms of precision, recall, and F-measure. The results suggest that the proposed collaborative topic variation inspection approach can substantially improve the performance of a basic profiling method adapted from the classical RF algorithm. It can also improve the accuracy of other methods when a worker's information needs are vague or evolving, i.e., when there is a high degree of variation in the worker's topic-needs. Our findings have implications for the design of an effective collaborative information filtering and retrieval model, which is crucial for reusing an organization's knowledge assets effectively. [ABSTRACT FROM AUTHOR]

Advanced tourist information systems should offer more than relatively static information about sights and places. Instead, semantically rich information about sights should be delivered to the mobile users. Furthermore, tourists should not be overwhelmed by a stream of superfluous data that are unrelated to their interest, location, and knowledge of a place. Personalization of the information delivery to each traveler, together with their travel history, is therefore crucial. This article presents the major design issues of the personalized Tourist Information Provider (TIP). TIP is a combination of an event-based system (EBS) and a location-based service (LBS) applied to a mobile environment. We discuss the lessons learned from developing its kernel using a semantic network of sight-related information and considering the travelers' interest and travel route, with emphasis on modeling decisions and their impact on the final system. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we study the recommendation problem as formed by two tasks: (i) to filter useful/interesting items, (ii) to guide the user to good recommendations. The first task has been widely studied in the field of recommender systems. In fact, the most common characterization of these systems is based on the algorithms that select (filter) the items to be recommended (e.g. collaborative filtering, content-based, etc.). Through this paper, we will focus on the second task: the task of guiding the user. We claim that this task needs a closer attention. In this paper, we report an experiment to provide evidence for this fact. Actually, the experiment shows that machine learning algorithms commonly applied to the first task become useless when applied to the task of guiding. [Copyright &y& Elsevier]

We propose an information filtering system with extracted index words using Independent Component Analysis (ICA). Elements of a document vector are established as the weights of index words and their dimensions become larger as the number of documents is increased. Therefore, from the viewpoint of processing time and memory space, the dimension must be decreased. The proposed method decreases the dimension by selecting the index words based on the topics included in the corpus. We have applied ICA to the documents to obtain the topics. Then filtering by the relevance feedback with the document vectors reconstructed by the selected index words, was carried out to confirm the effectiveness of the proposed method. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(2): 21–27, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10018 [ABSTRACT FROM AUTHOR]

Recently, wireless sensor networks (WSNs) have been widely discussed in many applications. In this paper, we propose a WSN-based intelligent light control system for indoor environments. Wireless sensors are responsible for measuring current illuminations. Two kinds of lighting devices, namely, whole lighting and local lighting devices, are used to provide background and concentrated illuminations, respectively. Users may have various illumination requirements according to their activities and profiles. An illumination requirement is as the combination of background and concentrated illumination demands and users' locations. We consider two requirement models, namely, binary satisfaction and continuous satisfaction models, and propose two decision algorithms to determine the proper illuminations of devices and to achieve the desired optimization goals. Then, a closed-loop device control algorithm is applied to adjust the illumination levels of lighting devices. The prototyping results verify that our ideas are practical and feasible. [ABSTRACT FROM PUBLISHER]

The objective of this study was to characterize the types of tissue-centric users based on tissue use, requirements, and their job or work-related variables at the University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA. A self-reporting questionnaire was distributed to biomedical researchers at the UPMC. Descriptive and cluster analyses were performed to identify and characterize the complex types of tissue-based researchers. A total of 62 respondents completed the survey, and two clusters were identified based on all variables. Two distinct groups of tissue-centric users made direct use of tissue samples for their research as well as associated information, while a third group of indirect users required only the associated information. The study shows that tissue-centric users were composed of various types. These types were distinguished in terms of tissue use and data requirements, as well as by their work or research-related activities. [ABSTRACT FROM AUTHOR]

In 2007, the social networking Web site MySpace apparently overthrew Google as the most visited Web site for U.S. Web users. If this heralds a new era of widespread online social networking, then it is important to investigate user behaviour and attributes. Although there has been some research into social networking already, basic demographic data is essential to set previous results in a wider context and to give insights to researchers, marketers and developers. In this article, the demographics of MySpace members are explored through data extracted from two samples of 15,043 and 7,627 member profiles. The median declared age of users was surprisingly high at 21, with a small majority of females. The analysis confirmed some previously reported findings and conjectures about social networking, for example, that female members tend to be more interested in friendship and males more interested in dating. In addition, there was some evidence of three different friending dynamics, oriented towards close friends, acquaintances, or strangers. Perhaps unsurprisingly, female and younger members had more friends than others, and females were more likely to maintain private profiles, but both males and females seemed to prefer female friends, with this tendency more marked in females for their closest friend. The typical MySpace user is apparently female, 21, single, with a public profile, interested in online friendship and logging on weekly to engage with a mixed list of mainly female “friends” who are predominantly acquaintances. [ABSTRACT FROM AUTHOR]

In this paper, we present a complete framework and findings in mining Web usage patterns from Web log files of a real Web site that has all the challenging aspects of real-life Web usage mining, including evolving user profiles and external data describing an ontology of the Web content. Even though the Web site under study is part of a nonprofit organization that does not ‘sell’ any products, it was crucial to understand ‘who’ the users were, ‘what’ they looked at, and ‘how their interests changed with time,’ all of which are important questions in Customer Relationship Management (CRM). Hence, we present an approach for discovering and tracking evolving user profiles. We also describe how the discovered user profiles can be enriched with explicit information need that is inferred from search queries extracted from Web log data. Profiles are also enriched with other domain-specific information facets that give a panoramic view of the discovered mass usage modes. An objective validation strategy is also used to assess the quality of the mined profiles, in particular their adaptability in the face of evolving user behavior. [ABSTRACT FROM AUTHOR]

Recently, there has been a dramatic increase in the use of XML data to deliver information over the Web. Personal Weblogs, news Web sites, and discussion forums are now publishing RSS feeds for their subscribers to retrieve new postings. As the popularity of personal Weblogs and RSS feeds grows rapidly, RSS aggregation services and blog search engines have appeared, which try to provide a central access point for simpler access and discovery of new content from a large number of diverse RSS sources. In this paper, we study how the RSS aggregation services should monitor the data sources to retrieve new content quickly using minimal resources and to provide its subscribers with fast news alerts. We believe that the change characteristics of RSS sources and the general user access behavior pose distinct requirements that make this task significantly different from the traditional index refresh problem for Web search engines. Our studies on a collection of 10,000 RSS feeds reveal some general characteristics of the RSS feeds and show that, with proper resource allocation and scheduling, the RSS aggregator provides news alerts significantly faster than the best existing approach. [ABSTRACT FROM AUTHOR]

An online survey revealing significant dependencies between tourist types and the preferences for particular tourism-related photographs has been conducted. We take advantage of these findings for a playful user preference elicitation approach and propose a profiling technique based on photograph selection, Furthermore. significant relationships between tourist types and their willingness to keeping travel diaries and compile photo albums were observed. Especially tourist types with travel motives of knowledge acquisition and exploration were identified to be most willing to share their travel experiences. These insights will be used to advance the design and development of an integrated, game-like 3D e-tourism environment fostering a lively and sustainable online community. [ABSTRACT FROM AUTHOR]

A replication scheme determines the number and location of replicas in a distributed system. Traditional static replication schemes do not perform well in mobile environments due to the assumptions of fixed hosts, relatively static access patterns, and the lack of considerations on users' behalf. For effective data management in mobile environments, we define the notion of activity-data dependency for inferring the information requirement of mobile users based on their scheduled activities. We then propose a dynamic replication scheme which employs user profiles for recording users' mobility schedules, accesses behaviors and read/write patterns, and actively reconfigures the replicas to adapt to changes in user locations, data requests, and system status. Simulation results demonstrate that the scheme can accurately predict the data requirement to facilitate effective replication, reduce response time, and increase data availability. [ABSTRACT FROM AUTHOR]

As more information becomes available electronically, information retrieval or filtering tools for finding information of interest to users become increasingly important. Building tools for assisting users in finding relevant information is often complicated by the difficulty in articulating user interest in a form that can be used for searching. The aim of the approach described here is to build instantaneous user profiles in a single episode information-seeking environment that instantaneously and accurately captures user interests with minimum user interaction. The research work described here focuses on the importance of accuracy and preciseness of user profiles that could be represented in terms of concepts, i.e., keywords or subject descriptors assigned to technical documents, Studies conducted here are in the context of understanding and building content-based profiling system for technical documents, specially in the field of aerospace science and technology. Sample data of aerospace grey literature in the form of technical papers with title and associated descriptors have been considered for the design and development of database and web-based profile builder for generating user profiles. The descriptors assigned to technical papers with the help of technical thesaurus were considered. Equal importance or weightage was given to broader and narrower terms, because there is no pre-defined mechanism to assign different weightage to different subject descriptors. Generated user profiles in terms of weighted vector of descriptors could directly be used for recommending technical documents with relevance ranking for latest addition of documents. [ABSTRACT FROM AUTHOR]

In this paper, we present a music recommendation system, which provides a personalized service of music recommendation. The polyphonic music objects of MIDI format are first analyzed for deriving information for music grouping. For this purpose, the representative track of each polyphonic music object is first determined, and then six features are extracted from this track for proper music grouping. Moreover, the user access histories are analyzed to derive the profiles of user interests and behaviors for user grouping. The content-based, collaborative, and statistics-based recommendation methods are proposed based on the favorite degrees of the users to the music groups, and the user groups they belong to. A series of experiments are carried out to show that our approach performs well. [ABSTRACT FROM AUTHOR]

Web applications are designed to communicate for a diversity of purposes to a multiplicity of users with a variety of goals. Analysts have to manage this complexity by modelling user requirements and provide proper guidance to the web design activity. This paper introduces a framework based on user profiles, roles and goals for describing and analysing web user requirements. Project teams can use these conceptual tools for systematically envisioning salient user scenarios at a proper conceptual level. The information gathered through the analysis feeds into a goal-oriented requirements engineering method, which facilitates the transition from requirements to web design. Excerpts from the design of a real museum web site are discussed as a case study. [ABSTRACT FROM AUTHOR]

A good shopping recommender system can boost sales in a retailer store. To provide accurate recommendation, the recommender needs to accurately predict a customer's preference, an ability difficult to acquire. Conventional data mining techniques, such as association rule mining and collaborative filtering, can generally be applied to this problem, but rarely produce satisfying results due to the skewness and sparsity of transaction data. In this paper, we report the lessons that we learned in two real-world data mining applications for personalized shopping recommendation. We learned that extending a collaborative filtering method based on ratings (e.g., GroupLens) to perform personalized shopping recommendation is not trivial and that it is not appropriate to apply association-rule based methods (e.g., the IBM SmartPad system) for large scale prediction of customers' shopping preferences. Instead, a probabilistic graphical model can be more effective in handling skewed and sparse data. By casting collaborative filtering algorithms in a probabilistic framework, we derived HyPAM (Hybrid Poisson Aspect Modelling), a novel probabilistic graphical model for personalized shopping recommendation. Experimental results show that HyPAM outperforms GroupLens and the IBM method by generating much more accurate predictions of what items a customer will actually purchase in the unseen test data. The data sets and the results are made available for download at http://chunnan.iis.sinica.edu.tw/hypam/HyPAM.html. [ABSTRACT FROM AUTHOR]

The authors propose improved ways of analysing web server log files. Traditionally web site statistics focus on giving a big (and shallow) picture analysis based on all transaction log entries. The pictures are, however, distorted because of the problems associated with resolving Internet protocol (IP) numbers to a single user and cross-border IP registration. The authors argue that analysing extracted sub-groups and categories presents a more accurate picture of the data and that the analysis of the online behaviour of selected individuals (rather than of very large groups) can add much to our understanding of how people use web sites and, indeed, any digital information source. The analysis is labelled `micro' to distinguish it from traditional macro, big picture transactional log analysis. The methods are illustrated with recourse to the logs of the Surgery Door (www.surgerydoor.co.uk) consumer health web site. It was found that use attributed to academic users gave a better approximation of the sites' geographical distribution of users than an analysis based on all users. This occurs as academic institutions, unlike other user types, register in their host country. Selecting log entries where each user is allocated a unique IP number can be particularly beneficial, especially to analyses of returnees. Finally the paper tracks the online behaviour of a small number of IP numbers, in an example of the application of microanalysis, [ABSTRACT FROM PUBLISHER]

We investigate the application of multi-agent systems to develop intelligent information interfaces for connected communities, a class of computer applications aimed at enhancing the way people interact and socialise in geographically co-located communities such as neighbourhoods. In this context, we study the problem of providing effective information management in support of social interaction when a diverse range of computing devices is employed. The novelty of our approach is based on combining innovative interactive devices with a framework based on agent roles in order to support the effective flow of community-related content for the people of a given locality. In particular, we have integrated existing techniques for information retrieval and filtering with measures of content popularity, to ensure that documents in the community system are optimally available. After reporting on the potential presence of the system in the community, we report on the development of a framework for multi-agent systems in which agents provide a number of services aimed at facilitating personalised and location-dependent information access to members of the community. We also present a summary of the results of an expert evaluation of the information flow resulting from the communication between agents, and a user-evaluation of the information dissemination facilities provided by the system. [Copyright &y& Elsevier]

Mobile devices need two basic renewable resources - power and data. Power recharging is easy; data recharging is a much more problematic activity. It requires complex interaction between a user and a collection of data sources. We provide an automatic data recharging capability based on user profiles written in an expressive profile language. A profile identifies relevant information and orders it by its usefulness. We discuss the issues involved in designing a profile language for data recharging [ABSTRACT FROM PUBLISHER]

We discuss algorithms for learning and revising user profiles that can determine which World Wide Web sites on a given topic would be interesting to a user. We describe the use of a naive Bayesian classifier for this task, and demonstrate that it can incrementally learn profiles from user feedback on the interestingness of Web sites. Furthermore, the Bayesian classifier may easily be extended to revise user provided profiles. In an experimental evaluation we compare the Bayesian classifier to computationally more intensive alternatives, and show that it performs at least as well as these approaches throughout a range of different domains. In addition, we empirically analyze the effects of providing the classifier with background knowledge in form of user defined profiles and examine the use of lexical knowledge for feature selection. We find that both approaches can substantially increase the prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we present a complete framework and findings in mining Web usage patterns from Web log files of a real Website that has all the challenging aspects of real-life Web usage mining, including evolving user profiles and external data describing an ontology of the Web content. Even though the Web site under study is part of a nonprofit organization that does not "sell" any products, it was crucial to understand "who" the users were, "what" they looked at, and "how their interests changed with time," all of which are important questions in Customer Relationship Management (CRM). Hence, we present an approach for discovering and tracking evolving user profiles. We also describe how the discovered user profiles can be enriched with explicit information need that is inferred from search queries extracted from Web log data. Profiles are also enriched with other domain-specific information facets that give a panoramic view of the discovered mass usage modes. An objective validation strategy is also used to assess the quality of the mined profiles, in particular their adaptability in the face of evolving user behavior. [ABSTRACT FROM AUTHOR]