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Causality is gaining more and more attention in the machine learning community and consequently also in recommender systems research. The limitations of learning offline from observed data are widely recognized, however, applying debiasing strategies like Inverse Propensity Weighting does not always solve the problem of making wrong estimates. This concept paper contributes a summary of debiasing strategies in recommender systems and the design of several toy examples demonstrating the limits of these commonly applied approaches. Therefore, we propose to map the causality frameworks of potential outcomes and structural causal models onto the recommender systems domain in order to foster future research and development. For instance, applying causal discovery strategies on offline data to learn the causal graph in order to compute counterfactuals or improve debiasing strategies.

Recommender systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by many factors when making decisions, and the recommender must account for these to be effective. In this work, we use a causal graph to investigate the influence of different factors on the user's decision to click or not on the recommended accommodations. To learn the causal graph, we combine data provided by a meta-search booking platform for online hotel searches with prior knowledge made available by domain experts. The analysis confirms that the learnt causal model correctly models the well-known effect of the ranking position and price on user decision-making. Furthermore, we discover some interactions between the considered factors. For example, the country of the user market influences the user's decisions for different values of the price.

Recommender Systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by several factors in their decision-making. We analysed a historical dataset from a meta-search booking platform with the aim of exploring how these factors influence user choices in the context of online hotel search and booking. Specifically, we focused our study on the influence of (i) ranking position, (ii) number of reviews, (iii) average ratings and (iv) price when analysing users’ click behaviour. Our results confirmed conventional wisdom that position and price were the “two elephants in the room” heavily influencing user decision-making. Thus, they need to be taken into account when, for instance, trying to learn user preferences from clickstream data. Using the results coming from this analysis, we performed an online A/B test on this meta-search booking platform comparing the current policy with a price-based re-rank policy. Our online experiments suggested that, although in offline experiments items with lower prices tend to have a higher Click-Through Rate, in an online context a price-based re-rank was only capable to improve the Click-Through Rate metric for the first positions of the recommended lists.

Recommender Systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by many factors in their decision making. Hence, we aim at exploring how these factors influence user choices in the context of online hotel search and booking. Specifically, we focused our study on (i) ranking position, (ii) price, (iii) rating, and (iv) number of reviews when analyzing users’ click behavior. The results showed that there were “two elephants in the room”: position and price, that heavily influenced the user decision-making and need to be taken into account when, for instance, trying to learn user preferences from offline data in order to bootstrap a recommender system.

This article presents the development of an expert system to support the diagnosis of post-harvest diseases of stored apples. We propose a picture-based and conversational interaction with users, where sampled images depicting symptoms of apples with known diseases are presented to users to elicit their feedback on perceived similarities in order to determine the most likely diagnosis of a diseased target apple. This article makes, besides the description of the industrial application scenario, multiple contributions circled around three rounds of user studies: (i) an usability and effectiveness assessment of the approach, where three user interface configurations are put to a test and the effectiveness of different types of user feedback mechanisms is assessed; (ii) contextual multi-armed bandit approaches for dynamic selection of displayed images with symptoms of diseased apples, that clearly outperform random and greedy sampling baseline strategies; (iii) a comparison of two different strategies for determining the context representation of a contextual multi-armed bandit approach, namely based on PCA of image features and a gamified large-scale user study. We therefore provide design insights for the development of such diagnosis applications on diseases that manifest themselves through visual symptoms in general and, hence, the findings can be also valid for domains other than post-harvest fruit diseases.

The article “Agent-based models of human response to natural hazards: systematic review of tsunami evacuation”, written by Karel Mls · Milan Kořínek · Kamila Štekerová · Petr Tučník ·Vladimír Bureš · Pavel Čech · Martina Husáková · Peter Mikulecký ·Tomáš Nacházel · Daniela Ponce · Marek Zanker · František Babič ·Ioanna Triantafyllou., was originally published Online First without Open Access. After publication in volume 115, issue 3, page 1887–1908 the authors decided to opt for Open Choice and to make the article an Open Access publication. Therefore, the copyright of the article has been changed to © The Author(s) 2023 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit. The original article has been corrected. © 2023, Springer Nature B.V.

Tsunamis are a perilous natural phenomenon endangering growing coastal populations and tourists in many seaside resorts. Failures in responding to recent tsunami events stresses the importance of further research in building a robust tsunami warning system, especially in the “last mile” component. The lack of detail, unification and standardisation in information processing and decision support hampers wider implementation of reusable information technology solutions among local authorities and officials. In this paper, the architecture of a tsunami emergency solution is introduced. The aim of the research is to present a tsunami emergency solution for local authorities and officials responsible for preparing tsunami response and evacuation plans. The solution is based on a combination of machine learning techniques and agent-based modelling, enabling analysis of both real and simulated datasets. The solution is designed and developed based on the principles of enterprise architecture development. The data exploration follows the practices for data mining and big data analyses. The architecture of the solution is depicted using the standardised notation and includes components that can be exploited by responsible local authorities to test various tsunami impact scenarios and prepare plans for appropriate response measures. © 2023 by the authors.

In areas often decimated by natural disasters, indigenous peoples have developed specific knowledge through generations of stories based on their past experiences. This indigenous knowledge has enabled them to reduce the risk associated with natural phenomena, including tsunamis, disasters with highly destructive potential that occurrence is complicated to predict. At the beginning of the 21st century alone, tsunamis claimed hundreds of thousands of lives and caused enormous material damage, but many indigenous communities survived them with minimal human casualties. This study aims to identify, describe and analyze the indigenous knowledge that indigenous peoples use to reduce the risks resulting from the possible effects of tsunamis and earthquakes and reduce their negative impacts. This area is mapped in detail based on the available relevant literature and its analysis. Here, individual findings are linked to specific localities threatened by tsunamis or earthquakes, which are primarily situated in the Ring of Fire. The search results revealed various forms of indigenous knowledge ranging from intangible cultural values and stories to tangible stones or musical instruments. There is an intention to generalize the view of traditional knowledge, especially by analyzing the similarities and dependencies found between the identified knowledge. Some examples of failure to use indigenous knowledge, or ignorance of it on the part of municipalities, are also presented. Acquired results demonstrate that procedures and methods based on indigenous knowledge serve as effective tools in risk management when dealing with the danger of tsunamis or earthquakes and reducing their negative impacts. © 2023 The Authors

We approached the causal discovery task in the recommender system domain to learn a causal graph by combining observational data provided by a meta-search booking platform for online hotel search with prior knowledge made available by domain experts. The results show that it is possible to learn a causal graph coherent with previous findings in the recommender systems literature about the relations between different factors. Furthermore, we also discovered new insights that could help in the recommendation process.

Post-harvest diseases of apple can cause considerable economic losses. Thus, we developed DSSApple, an interactive web-based decision support system, that helps users to diagnose post-harvest diseases of domesticated apple based on observed macroscopic symptoms on fruit. Specifically, DSSApple is designed as a two-stream hybrid diagnostic tool, that can be effectively used by both expert and non-expert users to diagnose diseased instances of apple. The image-based stream allows the user to interact simply by selecting pictures, representing the variety of symptoms of diseases at different stages of the infection and on different cultivars. Instead, the expert-based stream of the system incrementally collects user feedback about the target disease by asking questions related to the macroscopic characteristics of the observed symptoms on a target apple. The expert-based reasoning mechanism of DSSApple is developed by leveraging the framework of Bayesian Networks (BNs). We detail the process of building this knowledge base with the support of a domain expert. We further exploit the BN to process incomplete or conflicting user feedback within the inference mechanism as well as to provide human-understandable explanations on the suggested diagnoses. The proposed hybrid approach has been thoroughly evaluated in two studies, involving simulated (by photos) as well as real infected apples. Thus, the proposed hybrid version of DSSApple is able to outperform both the single streams and the user intuition in terms of diagnostic accuracy.

Post-harvest diseases are one of the main causes of economical losses in the apple fruit production sector. Therefore, this paper presents an application of a knowledge-based expert system to diagnose post-harvest diseases of apple. Specifically, we detail the process of domain knowledge elicitation for constructing a Bayesian network reasoning system. We describe the developed expert system, dubbed BN-DSSApple, and the diagnostic mechanism given the evidence provided by the user, as well as a likelihood evidence method, learned from the estimated consensus of users’ and expert’s interactions, to effectively transfer the performance of the model to different cohorts of users. Finally, we detail a novel technique for explaining the provided diagnosis, thus increasing the trust in the system. We evaluate BN-DSSApple with three different types of user studies, involving real diseased apples, where the ground truth of the target instances was established by microbiological and DNA analysis. The experiments demonstrate the performance differences in the knowledge-based reasoning mechanism due to heterogeneous users interacting with the system under various conditions and the capability of the likelihood-based method to improve the diagnostic performance in different environments.

The Multi-Armed Bandit (MAB) problem has been extensively studied in order to address real-world challenges related to sequential decision making. In this setting, an agent selects the best action to be performed at time-step t, based on the past rewards received by the environment. This formulation implicitly assumes that the expected payoff for each action is kept stationary by the environment through time. Nevertheless, in many real-world applications this assumption does not hold and the agent has to face a non-stationary environment, that is, with a changing reward distribution. Thus, we present a new MAB algorithm, named f-Discounted-Sliding-Window Thompson Sampling (f-dsw TS), for non-stationary environments, that is, when the data streaming is affected by concept drift. The f-dsw TS algorithm is based on Thompson Sampling (TS) and exploits a discount factor on the reward history and an arm-related sliding window to contrast concept drift in non-stationary environments. We investigate how to combine these two sources of information, namely the discount factor and the sliding window, by means of an aggregation function f (.). In particular, we proposed a pessimistic (f = min), an optimistic (f = max), as well as an averaged (f = mean) version of the f-dsw TS algorithm. A rich set of numerical experiments is performed to evaluate the f-dsw TS algorithm compared to both stationary and non-stationary state-of-the-art TS baselines. We exploited synthetic environments (both randomly-generated and controlled) to test the MAB algorithms under different types of drift, that is, sudden/abrupt, incremental, gradual and increasing/decreasing drift. Furthermore, we adapt four real-world active learning tasks to our framework—a prediction task on crimes in the city of Baltimore, a classification task on insects species, a recommendation task on local web-news, and a time-series analysis on microbial organisms in the tropical air ecosystem. The f-dsw TS approach emerges as

South Tyrol (northern Italy) produces around one million metric tons of apple fruit annually and contributes to almost 10% of the European apple harvest. Despite the availability of advanced storage technologies, postharvest diseases of apple may lead to the deterioration of quality and losses of fruit not only during the storage process but also in the course of packing, shipment and shelf-life. In order to decide on a strategy for damage control or to implement plant protection programs, it is crucially important to reliably determine the nature of plant diseases. Apart from the observation of macroscopic symptoms and the application of laboratory-dependent microscopic, microbiological and molecular methods, a variety of novel approaches for the detection of plant diseases and pathogens is being developed. The present study focuses on the potential of information technology to bring the diagnosis process closer to practitioners. Particular attention is paid to the application of computer vision techniques and use of decision support systems. We provide a review of existing implementations of computer-based tools to diagnose plant diseases and present an outline of the “DSSApple” research project, which focuses on the development of a decision support system for the determination of postharvest diseases of apples.

This paper presents a case-study of a knowledge-based recommender system capable to diagnose post-harvest diseases of apples. It describes the process of knowledge elicitation and construction of a Bayesian Network reasoning system as well as its evaluation with three different types of studies involving diseased apples. The ground truth of diseased instances has been established by genome sequencing in a lab. The paper demonstrates the performance differences of knowledge-based reasoning mechanisms due to different users interacting with the system under different conditions and proposes methods for boosting the performance by likelihood evidence learned from the estimated consensus of users' and expert's interactions.

Recommender Systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by many factors in their decision making. In this paper, we focused our attention on the influence of price in user decision-making in the context of online hotel search and booking. First, we analyzed a historical dataset from a meta-search booking platform to evaluate the influence of different factors on user click behavior. Then, we performed an online A/B test on the same meta-search booking platform, in which we compared the current policy with a price-based re-rank policy. Our experiments suggested that, although in offline observations properties with lower prices tended to have a higher Click-Through Rate, in an online context a price-based re-rank was only sufficient to achieve an improvement in Click-Through Rate for the first position on the recommended list.

This manuscript discusses our ongoing work on ranx, a Python evaluation library for Information Retrieval. First, we introduce our work, summarize the already available functionalities, show the user-friendly nature of our tool through code snippets, and briefly discuss the technologies we relied on for the implementation and their advantages. Then, we present the upcoming features, such as several Metasearch algorithms, and introduce the long-term goals of our project.

Post-harvest diseases of apple are one of the major issues in the economical sector of apple production, causing severe economical losses to producers. Thus, we developed DSSApple, a picture-based decision support system able to help users in the diagnosis of apple diseases. Specifically, this paper addresses the problem of sequentially optimizing for the best diagnosis, exploiting past interactions with the system and their contextual information (i.e., the evidence provided by the users), while exploring the set of candidate diseases. This online learning problem is commonly addressed in the literature through a stochastic active learning paradigm - i.e., Contextual Multi-Armed Bandit (CMAB). The methodology interactively updates the decision model considering the success of each past interaction with respect to the context provided in each round. However, contextual information is very often partial and inadequate to handle such a complex decision making problem. On the other hand, human-made decisions implicitly include unobserved factors (referred to as unobserved confounders) that significantly influence their choices. In this paper, we take advantage of the information embedded in the observed human decisions to marginalize confounders and improve the capability of the CMAB model to identify the correct diagnosis. Specifically, we propose a Counterfactual Thompson Sampling (CF-TS), a CMAB model based on the causal concept of counterfactual. The proposed model is validated with offline experiments based on data collected through a large user study on DSSApple application. The results prove that CF-TS is able to significantly outperform both traditional CMAB algorithms and observed user decisions, in the real-world task of predicting the correct apple disease.

This paper describes a decision support system to fulfill the diagnosis of post-harvest diseases in apple fruit. The diagnostic system builds on user feedback elicitation via multiple conversational rounds. The interaction with the user is conducted by selecting and displaying images depicting symptoms of diseased apples. The system is able to adapt the image selection mechanism by exploiting previous user feedback through a contextual multi-armed bandit approach. We performed a large scale user experiment, where different strategies for image selection have been compared in order to identify which reloading strategy makes users more effective in the diagnosis task. Concretely, we compared contextual multi-armed bandit methods with two baseline strategies and identified that the exploration-exploitation principle significantly paid off in comparison to a greedy and to a random stratified selection strategy. Although the application context is very domain specific, we believe that the general methodology of conversational item selection for eliciting user preferences applies to other scenarios in decision support and recommendation systems.

The deep learning wave is propagating through many research areas and communities. In the last years it quickly propagated to Recommendation Systems, a research area which aims to recommend items to users. Indeed, many deep learning models and architectures have been proposed for Recommendation Systems to improve collaborative €ltering and content based algorithms. In this paper we propose a hybrid recommendation system combining user ratings and natural language text processing to solve the 0/1 recommendation problem. In particular, we describe a deep learning architecture combining two information sources, namely natural language text and user rating. Natural language text is used to learn a user-specific content-based classifier, while user ratings are used to develop user-Adaptive collaborative filtering recommendations. We perform numerical experiments on MovieLens 1M and reach first preliminary, but promising results, showing the proposed architecture has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervisor.

This chapter builds on a dataset where online users of a spa platform participated in an online idea contest providing free-text descriptions of their proposals for spa services. A panel of domain experts annotated these idea descriptions with a score for their innovativeness that serves as ground truth for machine learning experiments. Thus, the contribution lies in the application of topic modeling techniques to free-text idea descriptions in order to automatically identify innovative proposals based on advanced text processing and machine learning. Results of this case study indicate that topic modeling can outperform the ZeroR baseline as well as traditional survey scales for lead user identification and therefore constitute a first step towards exploring this technique for innovation research.

Most evaluations of novel algorithmic contributions assess their accuracy in predicting what was withheld in an of- ine evaluation scenario. However, several doubts have been raised that standard offline evaluation practices are not ap- propriate to select the best algorithm for field deployment. The goal of this work is therefore to compare the offline and the online evaluation methodology with the same study participants, i.e. a within users experimental design. This paper presents empirical evidence that the ranking of algo- rithms based on offline accuracy measurements clearly con- tradicts the results from the online study with the same set of users. Thus the external validity of the most commonly applied evaluation methodology is not guaranteed.

This demo presents a conversational navigation approach for a diagnostic application of postharvest diseases of apple with the goal to educate users on the diagnosed diseases as well as to recommend consequences for the storage facility and what action to take for the next growing period. It thus builds on earlier works on picture-based navigation for conversational recommender systems and provides evidence for its usability based on a first small-scale comparative usability study. © 2018 Copyright held by the owner/author(s)

Latent factor models have been proved to be the state of the art for the Collaborative Filtering approach in a Recommender System. However, latent factors obtained with mathematical methods applied to the user-item matrix can be hardly interpreted by humans. In this paper we exploit Topic Models applied to textual data associated with items to find explanations for latent factors. Based on the MovieLens dataset and textual data about movies collected from Freebase we run a user study with over hundred participants to develop a reference dataset for evaluating different strategies towards more interpretable and portable latent factor models.

Travel websites and online booking platforms represent today's major sources for customers when gathering information before a trip. In particular, community-provided customer reviews and ratings of various tourism services represent a valuable source of information for trip planning. With respect to customer ratings, many modern travel and tourism platforms-in contrast to several other e-commerce domains-allow customers to rate objects along multiple dimensions and thus to provide more fine-granular post-trip feedback on the booked accommodation or travel package. In this paper, we first show how this multi-criteria rating information can help to obtain a better understanding of factors driving customer satisfaction for different segments. For this purpose, we performed a Penalty-Reward contrast analysis on a data set from a major tourism platform, which reveals that customer segments significantly differ in the way the formation of overall satisfaction can be explained. Beyond the pure identification of segment-specific satisfaction factors, we furthermore show how this fine-granular rating information can be exploited to improve the accuracy of rating-based recommender systems. In particular, we propose to utilize user- and object-specific factor relevance weights which can be learned through linear regression. An empirical evaluation on datasets from different domains finally shows that our method helps us to predict the customer preferences more accurately and thus to develop better online recommendation services. © 2014 Springer-Verlag Berlin Heidelberg., Correspondence Address: Zanker, M.; Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria; email: markus.zanker@aau.at

Most recommendation systems require some form of user feedback such as ratings in order to make personalized propositions of items. Typically ratings are unidimensional in the sense of consisting of a scalar value that represents the user's appreciation for the rated item. Multi-criteria ratings allow users to express more differentiated opinions by allowing separate ratings for different aspects or dimensions of an item. Recent approaches of multi-criteria recommender systems are able to exploit this multifaceted user feedback and make personalized propositions that are more accurate than recommendations based on unidimensional rating data. However, most proposed multi-criteria recommendation algorithms simply exploit the fact that a richer feature space allows building more accurate predictive models without considering the semantics and available domain expertise. This paper contributes on the latter aspects by analyzing multi-criteria ratings from the major etourism platform, TripAdvisor, and structuring raters' overall satisfaction with the help of a Penalty-Reward Contrast analysis. We identify that several a-priori user segments significantly differ in the way overall satisfaction can be explained by multi-criteria rating dimensions. This finding has implications for practical algorithm development that needs to consider different user segments. © 2012 Springer-Verlag., Source: Scopus