Pub Date : 2023-10-24DOI: 10.1007/s11257-023-09377-8
Alain D. Starke, Cataldo Musto, Amon Rapp, Giovanni Semeraro, Christoph Trattner
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 ( $$N=502$$ N=502 ), 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 ( $$N=504$$ N=504 ), 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.
在线食谱网站的用户倾向于选择不健康的食品。它们的流行破坏了传统食物推荐系统的健康，因为许多用户缺乏营养知识，无法做出明智的食物决定。此外，所提供的信息往往与营养无关或难以理解。为了减轻这种情况，我们提出了一种方法来生成自然语言的理由，强调营养成分，健康风险，或推荐食谱的好处。我们的框架采用一个用户和两个食谱作为输入，并根据用户的特征和食谱的特征，遵循基于知识的推荐方法，自动生成自然语言证明作为输出。我们在两个众包研究中评估了我们的方法。在研究1 ($$N=502$$ N = 502)中，我们比较了两种个性化推荐方法的用户食物选择，基于(1)单一风格的论证或(2)使用无论证基线的比较论证。这些建议要么基于受欢迎程度，要么基于健康意识，后者基于用户的健康和营养需求。我们发现，比较论证风格在支持我们的健康意识建议的选择方面是有效的，证实了我们的方法对食物选择的影响。在研究2 ($$N=504$$ N = 504)中，我们使用相同的方法来比较八种不同的比较论证策略的有效性。我们向用户展示了两次成对的食谱:一次没有，一次有成对的证明。结果表明，理由导致第一道菜的选择明显更健康，而比较食物特征并强调健康风险、益处和用户的生活方式的策略最有效，迎合了与健康相关的选择动机。
{"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}
Pub Date : 2023-10-06DOI: 10.1007/s11257-023-09379-6
Josef Bauer, Dietmar Jannach
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.
{"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}
Pub Date : 2023-10-04DOI: 10.1007/s11257-023-09378-7
Gabrielle Alves, Dietmar Jannach, Rodrigo Ferrari de Souza, Daniela Damian, Marcelo Garcia Manzato
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 ( $$N=1064$$ N=1064 ) 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.
在推荐系统的许多应用领域中，例如在流媒体网站上，推荐服务提供商的一个主要目标是通过帮助用户发现他们喜欢的新类型的内容来增加用户的参与度。设计的标准协同过滤算法通常会导致一定程度的发现。尽管如此，在某些领域，更积极地向用户推广超越他们过去偏好的内容(“off-profile”)可能会有所帮助，从而帮助用户探索新内容。但是，当将这些非配置文件内容与更熟悉的内容结合在一起显示给用户时，新的内容项可能会被忽略。在这项研究中，我们探讨了数字推动，即在特定方向上巧妙地引导用户选择，可以在多大程度上帮助提高用户对非个人资料内容的关注和兴趣。我们在一个真实世界的社交图书推荐应用程序上进行了一项用户研究($$N=1064$$ N = 1064)。我们发现，那些被推荐他们不喜欢的类型的书的用户更经常把这些不喜欢的书放在他们的阅读清单上，从而证实了数字助推在这个应用程序中的有效性。然而，我们也发现，数字推动可能会对用户对系统的信念和态度产生负面影响，并在未来使用系统的意愿更有限。因此，我们发现，虽然推荐中的数字推动在短期内是有效的，但必须谨慎行事，密切关注用户对整体质量的看法和潜在的有害长期影响。
{"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}
Pub Date : 2023-09-25DOI: 10.1007/s11257-023-09382-x
Cataldo Musto, Giuseppe Spillo, Giovanni Semeraro
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.
{"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}
Pub Date : 2023-09-22DOI: 10.1007/s11257-023-09380-z
Thi Ngoc Trang Tran, Alexander Felfernig, Viet Man Le
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.
{"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}
Pub Date : 2023-09-19DOI: 10.1007/s11257-023-09383-w
Dennis Paulino, António Correia, João Barroso, Hugo Paredes
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.
{"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}
Pub Date : 2023-08-12DOI: 10.1007/s11257-023-09374-x
A. Cristea, Ahmed Alamri, Mohammed Alshehri, F. D. Pereira, A. Toda, E. H. T. de Oliveira, Craig Stewart
{"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}
Pub Date : 2023-07-31DOI: 10.1007/s11257-023-09373-y
D. Castilla, O. Del Tejo Catalá, Patricia Pons, F. Signol, Beatriz Rey, C. Suso‐Ribera, J. Pérez-Cortes
{"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}
Pub Date : 2023-07-12DOI: 10.1007/s11257-023-09375-w
Shabnam Najafian, Geoff Musick, Bart P. Knijnenburg, N. Tintarev
{"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}
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