User Modeling and User-Adapted Interaction最新文献

{"title":"“Tell Me Why”: using natural language justifications in a recipe recommender system to support healthier food choices","authors":"Alain D. Starke, Cataldo Musto, Amon Rapp, Giovanni Semeraro, Christoph Trattner","doi":"10.1007/s11257-023-09377-8","DOIUrl":"https://doi.org/10.1007/s11257-023-09377-8","url":null,"abstract":"Abstract Users of online recipe websites tend to prefer unhealthy foods. Their popularity undermines the healthiness of traditional food recommender systems, as many users lack nutritional knowledge to make informed food decisions. Moreover, the presented information is often unrelated to nutrition or difficult to understand. To alleviate this, we present a methodology to generate natural language justifications that emphasize the nutritional content, health risks, or benefits of recommended recipes. Our framework takes a user and two recipes as input and produces an automatically generated natural language justification as output, based on the user’s characteristics and the recipes’ features, following a knowledge-based recommendation approach. We evaluated our methodology in two crowdsourcing studies. In Study 1 ( $$N=502$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>502</mml:mn> </mml:mrow> </mml:math> ), we compared user food choices for two personalized recommendation approaches, based on either a (1) single-style justification or (2) comparative justification was shown, using a no justification baseline. The recommendations were either popularity-based or health-aware, the latter based on the health and nutritional needs of the user. We found that comparative justification styles were effective in supporting choices for our health-aware recommendations, confirming the impact of our methodology on food choices. In Study 2 ( $$N=504$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>504</mml:mn> </mml:mrow> </mml:math> ), we used the same methodology to compare the effectiveness of eight different comparative justification strategies. We presented pairs of recipes twice to users: once without and once with a pairwise justification. Results indicated that justifications led to significantly healthier choices for first course meals, while strategies that compared food features and emphasized health risks, benefits, and a user’s lifestyle were most effective, catering to health-related choice motivations.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135265919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid session-aware recommendation with feature-based models","authors":"Josef Bauer, Dietmar Jannach","doi":"10.1007/s11257-023-09379-6","DOIUrl":"https://doi.org/10.1007/s11257-023-09379-6","url":null,"abstract":"Abstract Session-based recommender systems model the interests of users based on their browsing behavior with the goal of making suitable item suggestions in an ongoing usage session. Most existing work in this growing research area make only use of the most recent observed interactions for each user, and they typically solely rely on user–item interaction data (e.g., click events) for interest modeling. Thus, they do not leverage important forms of other information which are commonly available in practical settings. In this work, we therefore propose a hybrid approach for personalized session-based ( “session-aware” ) recommendation, which (i) is able to take into account various types of side information as model features and which (ii) can be combined with existing session-based (or session-aware) recommendation models. Technically, our approach is based on stacking several session-based modeling approaches with efficient machine learning methods for tabular data, in our case using Gradient Boosting Machines (GBMs). We successfully evaluated our approach (named HySAR ) on two public e-commerce datasets. Specifically, we also demonstrate the effectiveness of a number of novel model features that we engineered in the course of this research. These features, which were mostly unexplored in previous works, relate to various types of information related to the users, their actions, the items, as well as contextual session characteristics. Different existing recommendation approaches and further problem specific features can be easily added in our generic method to improve recommendations.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135350689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digitally nudging users to explore off-profile recommendations: here be dragons","authors":"Gabrielle Alves, Dietmar Jannach, Rodrigo Ferrari de Souza, Daniela Damian, Marcelo Garcia Manzato","doi":"10.1007/s11257-023-09378-7","DOIUrl":"https://doi.org/10.1007/s11257-023-09378-7","url":null,"abstract":"Abstract In many application domains of recommender systems, e.g., on media streaming sites, one main goal of the provider of the recommendation service is to increase the engagement of users by helping them discover new types of content they like. Standard collaborative filtering algorithms by design often lead to a certain level of discovery. Nonetheless, in certain domains, it may be helpful to more actively promote content to users beyond their past preference profile (“off-profile”) and thereby help users explore new content. However, when showing such off-profile content to users in combination with more familiar content, the new content items may be overlooked. In this research, we explore to what extent digital nudging , i.e., subtly directing user choices in a specific direction, can help to raise the attention and interest of users for off-profile content. We conducted a user study ( $$N=1064$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1064</mml:mn> </mml:mrow> </mml:math> ) on a real-world social book recommendation app. We find that users who are nudged towards recommended books of their non-preferred genres significantly more often put these off-profile books on their reading lists, thus confirming the effectiveness of digital nudging in this application. However, we also found that digital nudges may negatively impact the users’ beliefs and attitudes towards the system and a more limited intention to use the system in the future. As a result, we find that digital nudging in recommendations, while effective in the short run, must be done with due care, keeping an eye on the overall quality perceptions by users and potentially harmful long-term effects.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing distributional semantics to build context-aware justifications for recommender systems","authors":"Cataldo Musto, Giuseppe Spillo, Giovanni Semeraro","doi":"10.1007/s11257-023-09382-x","DOIUrl":"https://doi.org/10.1007/s11257-023-09382-x","url":null,"abstract":"Abstract This paper introduces a methodology to generate review-based natural language justifications supporting personalized suggestions returned by a recommender system. The hallmark of our strategy lies in the fact that natural language justifications are adapted to the different contextual situations in which the items will be consumed. In particular, our strategy relies on the following intuition: Just like the selection of the most suitable item is influenced by the contexts of usage, a justification that supports a recommendation should vary as well. As an example, depending on whether a person is going out with her friends or her family, a justification that supports a restaurant recommendation should include different concepts and aspects . Accordingly, we designed a pipeline based on distributional semantics models to generate a vector space representation of each context. Such a representation, which relies on a term-context matrix, is used to identify the most suitable review excerpts that discuss aspects that are particularly relevant for a certain context. The methodology was validated by means of two user studies, carried out in two different domains (i.e., movies and restaurants). Moreover, we also analyzed whether and how our justifications impact on the perceived transparency of the recommendation process and allow the user to make more informed choices. As shown by the results, our intuitions were supported by the user studies.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135815746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An overview of consensus models for group decision-making and group recommender systems","authors":"Thi Ngoc Trang Tran, Alexander Felfernig, Viet Man Le","doi":"10.1007/s11257-023-09380-z","DOIUrl":"https://doi.org/10.1007/s11257-023-09380-z","url":null,"abstract":"Abstract Group decision-making processes can be supported by group recommender systems that help groups of users obtain satisfying decision outcomes. These systems integrate a consensus-achieving process, allowing group members to discuss with each other on the potential items, adapt their opinions accordingly, and achieve an agreement on a selected item. Such a process, therefore, helps to generate group recommendations with a high satisfaction level of group members. Our article provides a rigorous review of the existing consensus approaches to group decision-making. These approaches are classified depending on the applied consensus models such as reference domain where a set of group members or items is selected for calculating consensus measures, coincidence method that calculates the consensus degree between group members depending on the coincidence concept, operators that aggregate user preferences, guidance measures where the consensus-achieving process is guided by different consensus measures, and recommendation generation and individual centrality that enhance the role of a moderator or a leader in the consensus-achieving process. Further consensus techniques for group decision-making in heterogeneous and large-scale groups are also discussed in this article. Besides, to provide an overall landscape of consensus approaches, we also discuss new consensus models in group recommender systems. These models attempt to improve basic aggregation strategies, further consider social relationship interactions, and provide group members with intuitive descriptions regarding the current consensus state of the group. Finally, we point out challenges and discuss open topics for future work.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive personalization for online microtask labor platforms: A systematic literature review","authors":"Dennis Paulino, António Correia, João Barroso, Hugo Paredes","doi":"10.1007/s11257-023-09383-w","DOIUrl":"https://doi.org/10.1007/s11257-023-09383-w","url":null,"abstract":"Abstract Online microtask labor has increased its role in the last few years and has provided the possibility of people who were usually excluded from the labor market to work anytime and without geographical barriers. While this brings new opportunities for people to work remotely, it can also pose challenges regarding the difficulty of assigning tasks to workers according to their abilities. To this end, cognitive personalization can be used to assess the cognitive profile of each worker and subsequently match those workers to the most appropriate type of work that is available on the digital labor market. In this regard, we believe that the time is ripe for a review of the current state of research on cognitive personalization for digital labor. The present study was conducted by following the recommended guidelines for the software engineering domain through a systematic literature review that led to the analysis of 20 primary studies published from 2010 to 2020. The results report the application of several cognition theories derived from the field of psychology, which in turn revealed an apparent presence of studies indicating accurate levels of cognitive personalization in digital labor in addition to a potential increase in the worker’s performance, most frequently investigated in crowdsourcing settings. In view of this, the present essay seeks to contribute to the identification of several gaps and opportunities for future research in order to enhance the personalization of online labor, which has the potential of increasing both worker motivation and the quality of digital work.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135060766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation","authors":"A. Cristea, Ahmed Alamri, Mohammed Alshehri, F. D. Pereira, A. Toda, E. H. T. de Oliveira, Craig Stewart","doi":"10.1007/s11257-023-09374-x","DOIUrl":"https://doi.org/10.1007/s11257-023-09374-x","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43485299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling users’ heterogeneous taste with diversified attentive user profiles","authors":"Oren Barkan, T. Shaked, Yonatan Fuchs, Noam Koenigstein","doi":"10.1007/s11257-023-09376-9","DOIUrl":"https://doi.org/10.1007/s11257-023-09376-9","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42604331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the understanding of web user behaviors through machine learning analysis of eye-tracking data","authors":"D. Castilla, O. Del Tejo Catalá, Patricia Pons, F. Signol, Beatriz Rey, C. Suso‐Ribera, J. Pérez-Cortes","doi":"10.1007/s11257-023-09373-y","DOIUrl":"https://doi.org/10.1007/s11257-023-09373-y","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45310109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How do people make decisions in disclosing personal information in tourism group recommendations in competitive versus cooperative conditions?","authors":"Shabnam Najafian, Geoff Musick, Bart P. Knijnenburg, N. Tintarev","doi":"10.1007/s11257-023-09375-w","DOIUrl":"https://doi.org/10.1007/s11257-023-09375-w","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49540865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}