Novelty enhancement of recommendations is typically achieved through a post-filtering process applied on a candidate set of items. While it is an effective method, its performance heavily depends on the quality of a baseline algorithm, and many of the state-of-the-art algorithms generate recommendations that are relatively similar to what the user has interacted with in the past. In this paper we explore the use of sampling as a means of novelty enhancement in the Bayesian Personalized Ranking objective. We evaluate the proposed extensions on the MovieLens 20M dataset, and show that the proposed method can be successfully used instead of two-step reranking, as it offers comparable and better accuracy/novelty tradeoffs, and more unique recommendations.
{"title":"Bayesian Personalized Ranking for Novelty Enhancement","authors":"Jacek Wasilewski, N. Hurley","doi":"10.1145/3320435.3320468","DOIUrl":"https://doi.org/10.1145/3320435.3320468","url":null,"abstract":"Novelty enhancement of recommendations is typically achieved through a post-filtering process applied on a candidate set of items. While it is an effective method, its performance heavily depends on the quality of a baseline algorithm, and many of the state-of-the-art algorithms generate recommendations that are relatively similar to what the user has interacted with in the past. In this paper we explore the use of sampling as a means of novelty enhancement in the Bayesian Personalized Ranking objective. We evaluate the proposed extensions on the MovieLens 20M dataset, and show that the proposed method can be successfully used instead of two-step reranking, as it offers comparable and better accuracy/novelty tradeoffs, and more unique recommendations.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133632470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Badges are endemic to online interaction sites, from Question and Answer (Q&A) websites to ride sharing, as systems for rewarding participants for their contributions. This paper studies how badge design affects people's contributions and behavior over time. Past work has shown that badges "steer'' people's behavior toward substantially increasing the amount of contributions before obtaining the badge, and immediately decreasing their contributions thereafter, returning to their baseline contribution levels. In contrast, we find that the steering effect depends on the type of user, as modeled by the rate and intensity of the user's contributions. We use these measures to distinguish between different groups of user activity, including users who are not affected by the badge system despite being significant contributors to the site. We provide a predictive model of how users change their activity group over the course of their lifetime in the system. We demonstrate our approach empirically in three different Q&A sites on Stack Exchange with hundreds of thousands of users, and we discuss the implications for system designers.
{"title":"One Size Does Not Fit All: Badge Behavior in Q&A Sites","authors":"Stav Yanovsky, Nicholas Hoernle, Omer Lev, Y. Gal","doi":"10.1145/3320435.3320438","DOIUrl":"https://doi.org/10.1145/3320435.3320438","url":null,"abstract":"Badges are endemic to online interaction sites, from Question and Answer (Q&A) websites to ride sharing, as systems for rewarding participants for their contributions. This paper studies how badge design affects people's contributions and behavior over time. Past work has shown that badges \"steer'' people's behavior toward substantially increasing the amount of contributions before obtaining the badge, and immediately decreasing their contributions thereafter, returning to their baseline contribution levels. In contrast, we find that the steering effect depends on the type of user, as modeled by the rate and intensity of the user's contributions. We use these measures to distinguish between different groups of user activity, including users who are not affected by the badge system despite being significant contributors to the site. We provide a predictive model of how users change their activity group over the course of their lifetime in the system. We demonstrate our approach empirically in three different Q&A sites on Stack Exchange with hundreds of thousands of users, and we discuss the implications for system designers.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116043442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Hossein Rimaz, Mehdi Elahi, Farshad Bakhshandegan Moghaddam, C. Trattner, Reza Hosseini, M. Tkalcic
In this paper, we explore the potential of using visual features in movie Recommender Systems. This type of content features can be extracted automatically without any human involvement and have been shown to be very effective in representing the visual content of movies. We have performed the following experiments, using a large dataset of movie trailers: (i) Experiment A: an exploratory analysis as an initial investigation on the data, and (ii) Experiment B: building a movie recommender based on the visual features and evaluating the performance. The observed results have shown promising potential of visual features in representing the movies and the excellency of recommendation based on these features.
{"title":"Exploring the Power of Visual Features for the Recommendation of Movies","authors":"Mohammad Hossein Rimaz, Mehdi Elahi, Farshad Bakhshandegan Moghaddam, C. Trattner, Reza Hosseini, M. Tkalcic","doi":"10.1145/3320435.3320470","DOIUrl":"https://doi.org/10.1145/3320435.3320470","url":null,"abstract":"In this paper, we explore the potential of using visual features in movie Recommender Systems. This type of content features can be extracted automatically without any human involvement and have been shown to be very effective in representing the visual content of movies. We have performed the following experiments, using a large dataset of movie trailers: (i) Experiment A: an exploratory analysis as an initial investigation on the data, and (ii) Experiment B: building a movie recommender based on the visual features and evaluating the performance. The observed results have shown promising potential of visual features in representing the movies and the excellency of recommendation based on these features.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"307 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116405016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dereck Toker, Róbert Móro, Jakub Simko, M. Bieliková, C. Conati
In this paper, we present research to uncover how the level of reading comprehension abilities impacts how users process textual documents in English with embedded visualizations (i.e., Magazine Style Narrative Visualizations or MSNVs). We analyze performance and gaze data of users processing MSNVs from two user studies, one run in Canada and one in a non-English speaking European country. Our findings provide important insights toward developing automatic, real-time support to MSNV processing personalized according to users' English reading comprehension abilities.
{"title":"Impact of English Reading Comprehension Abilities on Processing Magazine Style Narrative Visualizations and Implications for Personalization","authors":"Dereck Toker, Róbert Móro, Jakub Simko, M. Bieliková, C. Conati","doi":"10.1145/3320435.3320447","DOIUrl":"https://doi.org/10.1145/3320435.3320447","url":null,"abstract":"In this paper, we present research to uncover how the level of reading comprehension abilities impacts how users process textual documents in English with embedded visualizations (i.e., Magazine Style Narrative Visualizations or MSNVs). We analyze performance and gaze data of users processing MSNVs from two user studies, one run in Canada and one in a non-English speaking European country. Our findings provide important insights toward developing automatic, real-time support to MSNV processing personalized according to users' English reading comprehension abilities.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128578925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Identifying instances when a user will not able to attend to an incoming message and constructing an auto-response with relevant contextual information may help reduce social pressures to immediately respond that many users face. Mobile messaging behavior often varies from one person to another. As a result, compared to a generic model considering profiles of several users, a personalized model can capture a user's messaging behavior more accurately to predict their inattentive states. However, creating accurate personalized models requires a non-trivial amount of individual data, which is often not available for new users. In this work, we investigate a weighted hybrid approach to model users' attention to messaging. Through dynamic performance-based weighting, we combine the predictions of three types of models, a general model, a group model and a personalized model to create an approach which can work through the lack of initial data while adapting to the user's behavior. We present the details of our modeling approach and the evaluation of the model with over three weeks of data from 274 users. Our results highlight the value of hybrid weighted modeling to predict when a user cannot attend to their messages.
{"title":"Adaptive Modelling of Attentiveness to Messaging: A Hybrid Approach","authors":"Pranut Jain, Rosta Farzan, Adam J. Lee","doi":"10.1145/3320435.3320461","DOIUrl":"https://doi.org/10.1145/3320435.3320461","url":null,"abstract":"Identifying instances when a user will not able to attend to an incoming message and constructing an auto-response with relevant contextual information may help reduce social pressures to immediately respond that many users face. Mobile messaging behavior often varies from one person to another. As a result, compared to a generic model considering profiles of several users, a personalized model can capture a user's messaging behavior more accurately to predict their inattentive states. However, creating accurate personalized models requires a non-trivial amount of individual data, which is often not available for new users. In this work, we investigate a weighted hybrid approach to model users' attention to messaging. Through dynamic performance-based weighting, we combine the predictions of three types of models, a general model, a group model and a personalized model to create an approach which can work through the lack of initial data while adapting to the user's behavior. We present the details of our modeling approach and the evaluation of the model with over three weeks of data from 274 users. Our results highlight the value of hybrid weighted modeling to predict when a user cannot attend to their messages.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"8 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114025211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Guchev, F. Cena, Fabiana Vernero, Cristina Gena
Structured user model data not only allow system personalization, but also may be of interest as a source for analysis: in particular, for the study of general trends and for the detection of anomalies in preferences and mutually-referenced features among different user models. Such sources are multidimensional and interrelated, and recently started to be represented as graph-based datasets. Among the most effective ways of studying such data is visual exploration based on data-driven graph drawing approaches: in particular, node-link and node-link-group diagrams. The paper provides an overview of advanced approaches to the graphical representation of multidimensional data derived from user modeling and presents a proposal for developing flexible and scalable user interfaces for the hypergraph-based visual exploration of relations within a user model (UM). Then, we propose these principles in the visualization of an existing adaptive system.
{"title":"Visual Annotations for Hybrid Graph-based User Model","authors":"V. Guchev, F. Cena, Fabiana Vernero, Cristina Gena","doi":"10.1145/3320435.3320472","DOIUrl":"https://doi.org/10.1145/3320435.3320472","url":null,"abstract":"Structured user model data not only allow system personalization, but also may be of interest as a source for analysis: in particular, for the study of general trends and for the detection of anomalies in preferences and mutually-referenced features among different user models. Such sources are multidimensional and interrelated, and recently started to be represented as graph-based datasets. Among the most effective ways of studying such data is visual exploration based on data-driven graph drawing approaches: in particular, node-link and node-link-group diagrams. The paper provides an overview of advanced approaches to the graphical representation of multidimensional data derived from user modeling and presents a proposal for developing flexible and scalable user interfaces for the hypergraph-based visual exploration of relations within a user model (UM). Then, we propose these principles in the visualization of an existing adaptive system.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"442 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121238536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Adamu Sidi-Ali, J. Masthoff, Matt Dennis, J. Kopecký, N. Beacham
This paper investigates adaptation of feedback to learners' cultural backgrounds. First, we investigate how to portray the cultural background of a learner. Second, we present a qualitative focus-group study, investigating how participants from different cultures believe culture affects the kind of feedback given to a learner. Finally, we present an empirical study on how humans adapt feedback based on the cultural background of learners to inspire an algorithm. Our investigations resulted in a set of stories which can be used to reliably portray a person's culture when investigating cultural adaptation in indirect experiments and user as wizard studies. They also provided insights into the adaptations people make to cultural differences.
{"title":"Adapting Performance And Emotional Support Feedback To Cultural Differences","authors":"Muhammad Adamu Sidi-Ali, J. Masthoff, Matt Dennis, J. Kopecký, N. Beacham","doi":"10.1145/3320435.3320444","DOIUrl":"https://doi.org/10.1145/3320435.3320444","url":null,"abstract":"This paper investigates adaptation of feedback to learners' cultural backgrounds. First, we investigate how to portray the cultural background of a learner. Second, we present a qualitative focus-group study, investigating how participants from different cultures believe culture affects the kind of feedback given to a learner. Finally, we present an empirical study on how humans adapt feedback based on the cultural background of learners to inspire an algorithm. Our investigations resulted in a set of stories which can be used to reliably portray a person's culture when investigating cultural adaptation in indirect experiments and user as wizard studies. They also provided insights into the adaptations people make to cultural differences.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116165442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Researchers claim that we are facing a global loneliness epidemic, and that mental illness, anxiety disorders, stress and burnout are on the rise. Technology, such as social media, is often found to have a detrimental effect on mental health, self-esteem and sleep, and to cause anxiety and feelings of loneliness. This talk is about how adaptive systems can actively improve well-being, instead of contributing to making it worse. We will discuss different ways of doing so, the work already done, the challenges faced, and our vision of a new kind of personalized systems that act as guardian angels. First, systems can provide emotional support, adapted to the recipient's characteristics such as their personality, affective state, cultural background, and stressors experienced. Second, systems can aid humans to provide emotional support. People often struggle to support others, and may say something that is counter productive or nothing at all. Systems can train people on how to provide support. They can also mediate emotional support, adapting support messages to both the support giver and recipient, taking into account for example the closeness of relationships and people's personality. Third, systems can support and motivate people to adopt behaviours that improve their well-being and that of others, and to better regulate their emotions. There has been much research on persuasive technology to support people in changing behaviours, and it has been shown that both the behaviour change techniques used, and attributes of techniques need adapting. Whilst much persuasive technology research has focused on physical well-being and sustainability, the emphasis in this presentation will be on mental well-being and encouraging people to help each other. Fourth, systems can team people up. Systems can decide who are best placed to provide support and motivation, encouraging particular people to support (or ask help from) particular other people. Additionally, adaptive group formation (or peer-to-peer recommendations) can be used for joint problem solving scenarios, with a system deciding or recommending who should work with whom. There are many benefits to group work, but it is also often a source of negative emotions. Adaptive group formation can consider affect and personality in addition to expertise, to minimize such negative emotions. Finally, systems can improve the well-being of groups and not just individuals. People's well-being is influenced by the well-being of others in their surroundings, and people's actions impact the well-being of others. Systems can monitor group well-being. They can encourage and support effective group behaviours, for example, by providing feedback on how group members and the group as a whole function. They can support the building of group identity and cohesion. They can support groups in making decisions that are good for group well-being. Overall, we envision adaptive systems as effective and emotionally intelligent co
{"title":"Towards Utter Well-Being: Personalization for Guardian Angels","authors":"J. Masthoff","doi":"10.1145/3320435.3323710","DOIUrl":"https://doi.org/10.1145/3320435.3323710","url":null,"abstract":"Researchers claim that we are facing a global loneliness epidemic, and that mental illness, anxiety disorders, stress and burnout are on the rise. Technology, such as social media, is often found to have a detrimental effect on mental health, self-esteem and sleep, and to cause anxiety and feelings of loneliness. This talk is about how adaptive systems can actively improve well-being, instead of contributing to making it worse. We will discuss different ways of doing so, the work already done, the challenges faced, and our vision of a new kind of personalized systems that act as guardian angels. First, systems can provide emotional support, adapted to the recipient's characteristics such as their personality, affective state, cultural background, and stressors experienced. Second, systems can aid humans to provide emotional support. People often struggle to support others, and may say something that is counter productive or nothing at all. Systems can train people on how to provide support. They can also mediate emotional support, adapting support messages to both the support giver and recipient, taking into account for example the closeness of relationships and people's personality. Third, systems can support and motivate people to adopt behaviours that improve their well-being and that of others, and to better regulate their emotions. There has been much research on persuasive technology to support people in changing behaviours, and it has been shown that both the behaviour change techniques used, and attributes of techniques need adapting. Whilst much persuasive technology research has focused on physical well-being and sustainability, the emphasis in this presentation will be on mental well-being and encouraging people to help each other. Fourth, systems can team people up. Systems can decide who are best placed to provide support and motivation, encouraging particular people to support (or ask help from) particular other people. Additionally, adaptive group formation (or peer-to-peer recommendations) can be used for joint problem solving scenarios, with a system deciding or recommending who should work with whom. There are many benefits to group work, but it is also often a source of negative emotions. Adaptive group formation can consider affect and personality in addition to expertise, to minimize such negative emotions. Finally, systems can improve the well-being of groups and not just individuals. People's well-being is influenced by the well-being of others in their surroundings, and people's actions impact the well-being of others. Systems can monitor group well-being. They can encourage and support effective group behaviours, for example, by providing feedback on how group members and the group as a whole function. They can support the building of group identity and cohesion. They can support groups in making decisions that are good for group well-being. Overall, we envision adaptive systems as effective and emotionally intelligent co","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114595035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Against the background of current trends towards natural and adaptive in-vehicle Spoken Dialog Systems, this paper aims at evaluating potential factors involved in the perception of naturalness and comprehensibility of system prompts. By conducting an exploratory user study investigating various syntactic paraphrases, we were able to identify several system- and user-sided characteristics which should be considered in the design of system prompts. We conclude from our results that the choice of a syntactic structure for in-vehicle prompts is a relevant question and interestingly depends on several individual user characteristics, such as personality.
{"title":"Linguistic Design of In-Vehicle Prompts in Adaptive Dialog Systems: An Analysis of Potential Factors Involved in the Perception of Naturalness","authors":"D. Stier, Ellen Sigloch","doi":"10.1145/3320435.3320469","DOIUrl":"https://doi.org/10.1145/3320435.3320469","url":null,"abstract":"Against the background of current trends towards natural and adaptive in-vehicle Spoken Dialog Systems, this paper aims at evaluating potential factors involved in the perception of naturalness and comprehensibility of system prompts. By conducting an exploratory user study investigating various syntactic paraphrases, we were able to identify several system- and user-sided characteristics which should be considered in the design of system prompts. We conclude from our results that the choice of a syntactic structure for in-vehicle prompts is a relevant question and interestingly depends on several individual user characteristics, such as personality.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124593738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leave out other types of information that can contribute to determine users' global reputation; e.g., public recognition of reviewers' quality. We are interested in understanding if and when these additional types of feedback improve Top-N recommendation. For this purpose, we propose a multi-faceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks. We aim at identifying general classes of data in order to make our model applicable to different case studies. Then, we test the model by applying it to a variant of User-to-User Collaborative filtering (U2UCF) which supports the fusion of rating similarity, local trust derived from social relations, and multi-faceted reputation for rating prediction. We test our model on two datasets: the Yelp one publishes generic friend relations between users but provides different types of trust feedback, including user profile endorsements. The LibraryThing dataset offers fewer types of feedback but it provides more selective friend relations aimed at content sharing. The results of our experiments show that, on the Yelp dataset, our model outperforms both U2UCF and state-of-the-art trust-based recommenders that only use rating similarity and social relations. Differently, in the LibraryThing dataset, the combination of social relations and rating similarity achieves the best results. The lesson we learn is that multi-faceted trust can be a valuable type of information for recommendation. However, before using it in an application domain, an analysis of the type and amount of available trust evidence has to be done to assess its real impact on recommendation performance.
{"title":"Multi-faceted Trust-based Collaborative Filtering","authors":"Noemi Mauro, L. Ardissono, Zhongli Filippo Hu","doi":"10.1145/3320435.3320441","DOIUrl":"https://doi.org/10.1145/3320435.3320441","url":null,"abstract":"Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leave out other types of information that can contribute to determine users' global reputation; e.g., public recognition of reviewers' quality. We are interested in understanding if and when these additional types of feedback improve Top-N recommendation. For this purpose, we propose a multi-faceted trust model to integrate local trust, represented by social links, with various types of global trust evidence provided by social networks. We aim at identifying general classes of data in order to make our model applicable to different case studies. Then, we test the model by applying it to a variant of User-to-User Collaborative filtering (U2UCF) which supports the fusion of rating similarity, local trust derived from social relations, and multi-faceted reputation for rating prediction. We test our model on two datasets: the Yelp one publishes generic friend relations between users but provides different types of trust feedback, including user profile endorsements. The LibraryThing dataset offers fewer types of feedback but it provides more selective friend relations aimed at content sharing. The results of our experiments show that, on the Yelp dataset, our model outperforms both U2UCF and state-of-the-art trust-based recommenders that only use rating similarity and social relations. Differently, in the LibraryThing dataset, the combination of social relations and rating similarity achieves the best results. The lesson we learn is that multi-faceted trust can be a valuable type of information for recommendation. However, before using it in an application domain, an analysis of the type and amount of available trust evidence has to be done to assess its real impact on recommendation performance.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132470322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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