Pub Date : 2023-06-24DOI: 10.1007/s11257-023-09368-9
Ine Coppens, Toon De Pessemier, Luc Martens
{"title":"Connecting physical activity with context and motivation: a user study to define variables to integrate into mobile health recommenders","authors":"Ine Coppens, Toon De Pessemier, Luc Martens","doi":"10.1007/s11257-023-09368-9","DOIUrl":"https://doi.org/10.1007/s11257-023-09368-9","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48279451","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 : 2023-06-21DOI: 10.1007/s11257-023-09363-0
Francesco Barile, Tim Draws, Oana Inel, Alisa Rieger, Shabnam Najafian, Amir Ebrahimi Fard, Rishav Hada, Nava Tintarev
Abstract Social choice aggregation strategies have been proposed as an explainable way to generate recommendations to groups of users. However, it is not trivial to determine the best strategy to apply for a specific group. Previous work highlighted that the performance of a group recommender system is affected by the internal diversity of the group members’ preferences. However, few of them have empirically evaluated how the specific distribution of preferences in a group determines which strategy is the most effective. Furthermore, only a few studies evaluated the impact of providing explanations for the recommendations generated with social choice aggregation strategies, by evaluating explanations and aggregation strategies in a coupled way. To fill these gaps, we present two user studies ( N =399 and N =288) examining the effectiveness of social choice aggregation strategies in terms of users’ fairness perception, consensus perception, and satisfaction. We study the impact of the level of (dis-)agreement within the group on the performance of these strategies. Furthermore, we investigate the added value of textual explanations of the underlying social choice aggregation strategy used to generate the recommendation. The results of both user studies show no benefits in using social choice-based explanations for group recommendations. However, we find significant differences in the effectiveness of the social choice-based aggregation strategies in both studies. Furthermore, the specific group configuration (i.e., various scenarios of internal diversity) seems to determine the most effective aggregation strategy. These results provide useful insights on how to select the appropriate aggregation strategy for a specific group based on the level of (dis-)agreement within the group members’ preferences.
{"title":"Evaluating explainable social choice-based aggregation strategies for group recommendation","authors":"Francesco Barile, Tim Draws, Oana Inel, Alisa Rieger, Shabnam Najafian, Amir Ebrahimi Fard, Rishav Hada, Nava Tintarev","doi":"10.1007/s11257-023-09363-0","DOIUrl":"https://doi.org/10.1007/s11257-023-09363-0","url":null,"abstract":"Abstract Social choice aggregation strategies have been proposed as an explainable way to generate recommendations to groups of users. However, it is not trivial to determine the best strategy to apply for a specific group. Previous work highlighted that the performance of a group recommender system is affected by the internal diversity of the group members’ preferences. However, few of them have empirically evaluated how the specific distribution of preferences in a group determines which strategy is the most effective. Furthermore, only a few studies evaluated the impact of providing explanations for the recommendations generated with social choice aggregation strategies, by evaluating explanations and aggregation strategies in a coupled way. To fill these gaps, we present two user studies ( N =399 and N =288) examining the effectiveness of social choice aggregation strategies in terms of users’ fairness perception, consensus perception, and satisfaction. We study the impact of the level of (dis-)agreement within the group on the performance of these strategies. Furthermore, we investigate the added value of textual explanations of the underlying social choice aggregation strategy used to generate the recommendation. The results of both user studies show no benefits in using social choice-based explanations for group recommendations. However, we find significant differences in the effectiveness of the social choice-based aggregation strategies in both studies. Furthermore, the specific group configuration (i.e., various scenarios of internal diversity) seems to determine the most effective aggregation strategy. These results provide useful insights on how to select the appropriate aggregation strategy for a specific group based on the level of (dis-)agreement within the group members’ preferences.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136355750","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 : 2023-06-19DOI: 10.1007/s11257-023-09372-z
H. Al-Samarraie, Samer Muthana Sarsam, A. Alzahrani
{"title":"Emotional intelligence and individuals’ viewing behaviour of human faces: a predictive approach","authors":"H. Al-Samarraie, Samer Muthana Sarsam, A. Alzahrani","doi":"10.1007/s11257-023-09372-z","DOIUrl":"https://doi.org/10.1007/s11257-023-09372-z","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47325688","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 : 2023-06-17DOI: 10.1007/s11257-023-09371-0
Bilikis Banire, Dena Al Thani, M. Qaraqe
{"title":"One size does not fit all: detecting attention in children with autism using machine learning","authors":"Bilikis Banire, Dena Al Thani, M. Qaraqe","doi":"10.1007/s11257-023-09371-0","DOIUrl":"https://doi.org/10.1007/s11257-023-09371-0","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46261223","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 : 2023-05-18DOI: 10.1007/s11257-023-09367-w
Jianwen Sun, Shangheng Du, Ruxia Liang, Xiaoxuan Shen, Qing Li, Sannyuya Liu, Zongkai Yang
{"title":"Deep adversarial group recommendation with user feature space separation","authors":"Jianwen Sun, Shangheng Du, Ruxia Liang, Xiaoxuan Shen, Qing Li, Sannyuya Liu, Zongkai Yang","doi":"10.1007/s11257-023-09367-w","DOIUrl":"https://doi.org/10.1007/s11257-023-09367-w","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42193859","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 : 2023-05-18DOI: 10.1007/s11257-023-09366-x
Naieme Hazrati, F. Ricci
{"title":"Choice models and recommender systems effects on users’ choices","authors":"Naieme Hazrati, F. Ricci","doi":"10.1007/s11257-023-09366-x","DOIUrl":"https://doi.org/10.1007/s11257-023-09366-x","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44162953","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 : 2023-05-15DOI: 10.1007/s11257-023-09361-2
Patrícia Alves, Helena Martins, Pedro Saraiva, João Carneiro, Paulo Novais, Goreti Marreiros
To travel in leisure is an emotional experience, and therefore, the more the information about the tourist is known, the more the personalized recommendations of places and attractions can be made. But if to provide recommendations to a tourist is complex, to provide them to a group is even more. The emergence of personality computing and personality-aware recommender systems (RS) brought a new solution for the cold-start problem inherent to the conventional RS and can be the leverage needed to solve conflicting preferences in heterogenous groups and to make more precise and personalized recommendations to tourists, as it has been evidenced that personality is strongly related to preferences in many domains, including tourism. Although many studies on psychology of tourism can be found, not many predict the tourists' preferences based on the Big Five personality dimensions. This work aims to find how personality relates to the choice of a wide range of tourist attractions, traveling motivations, and travel-related preferences and concerns, hoping to provide a solid base for researchers in the tourism RS area to automatically model tourists in the system without the need for tedious configurations, and solve the cold-start problem and conflicting preferences. By performing Exploratory and Confirmatory Factor Analysis on the data gathered from an online questionnaire, sent to Portuguese individuals from different areas of formation and age groups (n = 1035), we show all five personality dimensions can help predict the choice of tourist attractions and travel-related preferences and concerns, and that only neuroticism and openness predict traveling motivations.
{"title":"Group recommender systems for tourism: how does personality predict preferences for attractions, travel motivations, preferences and concerns?","authors":"Patrícia Alves, Helena Martins, Pedro Saraiva, João Carneiro, Paulo Novais, Goreti Marreiros","doi":"10.1007/s11257-023-09361-2","DOIUrl":"10.1007/s11257-023-09361-2","url":null,"abstract":"<p><p>To travel in leisure is an emotional experience, and therefore, the more the information about the tourist is known, the more the personalized recommendations of places and attractions can be made. But if to provide recommendations to a tourist is complex, to provide them to a group is even more. The emergence of personality computing and personality-aware recommender systems (RS) brought a new solution for the cold-start problem inherent to the conventional RS and can be the leverage needed to solve conflicting preferences in heterogenous groups and to make more precise and personalized recommendations to tourists, as it has been evidenced that personality is strongly related to preferences in many domains, including tourism. Although many studies on psychology of tourism can be found, not many predict the tourists' preferences based on the Big Five personality dimensions. This work aims to find how personality relates to the choice of a wide range of tourist attractions, traveling motivations, and travel-related preferences and concerns, hoping to provide a solid base for researchers in the tourism RS area to automatically model tourists in the system without the need for tedious configurations, and solve the cold-start problem and conflicting preferences. By performing Exploratory and Confirmatory Factor Analysis on the data gathered from an online questionnaire, sent to Portuguese individuals from different areas of formation and age groups (<i>n</i> = 1035), we show all five personality dimensions can help predict the choice of tourist attractions and travel-related preferences and concerns, and that only neuroticism and openness predict traveling motivations.</p>","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9706138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-19DOI: 10.1007/s11257-023-09360-3
Akrivi Krouska, C. Troussas, C. Sgouropoulou
{"title":"A novel group recommender system for domain-independent decision support customizing a grouping genetic algorithm","authors":"Akrivi Krouska, C. Troussas, C. Sgouropoulou","doi":"10.1007/s11257-023-09360-3","DOIUrl":"https://doi.org/10.1007/s11257-023-09360-3","url":null,"abstract":"","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43636908","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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