Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization最新文献_第3页

Rating-based Preference Elicitation for Recommendation of Stress Intervention 基于评分的压力干预推荐偏好启发

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3324990

Helma Torkamaan, J. Ziegler

{"title":"Rating-based Preference Elicitation for Recommendation of Stress Intervention","authors":"Helma Torkamaan, J. Ziegler","doi":"10.1145/3320435.3324990","DOIUrl":"https://doi.org/10.1145/3320435.3324990","url":null,"abstract":"In recent years, recommender systems have emerged as a key component for personalization in health applications. Central in the development of recommender systems is rating-based preference elicitation, based both on single-criterion and multi-criteria rating. Though its use has already been studied in various domains of recommender systems, far too little attention has been paid to preference elicitation in health recommender systems~(HRS). The purpose of this paper is to develop a better understanding of this preference elicitation by studying the criteria that users consider when they rate a health promotion recommendation from HRS, and accordingly, to offer a design solution as a functional feedback model for mobile health applications. This paper investigates the user-perceived importance of various criteria, as well as latent factors for eliciting user feedback on the recommendations. It also reports the relationship of explanation and trust to the overall rating. By aggregating a list of all possible criteria, we further discover that not all criteria are equally important to users, and that the effectiveness of a recommendation plays a dominant role.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"51 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":"122080922","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}

引用次数: 7

What Makes an Image Tagger Fair? 什么使图像标签公平?

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320442

Pinar Barlas, S. Kleanthous, K. Kyriakou, Jahna Otterbacher

{"title":"What Makes an Image Tagger Fair?","authors":"Pinar Barlas, S. Kleanthous, K. Kyriakou, Jahna Otterbacher","doi":"10.1145/3320435.3320442","DOIUrl":"https://doi.org/10.1145/3320435.3320442","url":null,"abstract":"Image analysis algorithms have been a boon to personalization in digital systems and are now widely available via easy-to-use APIs. However, it is important to ensure that they behave fairly in applications that involve processing images of people, such as dating apps. We conduct an experiment to shed light on the factors influencing the perception of \"fairness.\" Participants are shown a photo along with two descriptions (human- and algorithm-generated). They are then asked to indicate which is \"more fair\" in the context of a dating site, and explain their reasoning. We vary a number of factors, including the gender, race and attractiveness of the person in the photo. While participants generally found human-generated tags to be more fair, API tags were judged as being more fair in one setting - where the image depicted an \"attractive,\" white individual. In their explanations, participants often mention accuracy, as well as the objectivity/subjectivity of the tags in the description. We relate our work to the ongoing conversation about fairness in opaque tools like image tagging APIs, and their potential to result in harm.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"21 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":"125935197","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}

引用次数: 11

Socially-Aware Diagnosis for Constraint-Based Recommendation 基于约束推荐的社会意识诊断

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320436

Muesluem Atas, Ralph Samer, A. Felfernig, Thi Ngoc Trang Tran, Seda Polat Erdeniz, Martin Stettinger

引用次数: 1

Diversity and Novelty in Social-Based Collaborative Filtering 基于社会的协同过滤的多样性和新颖性

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320479

Dimitris Sacharidis

引用次数: 8

Beyond Explicit Reports: Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference 超越明确的报告:比较数据驱动的方法来研究音乐偏好的潜在维度

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320462

Jaehun Kim, Andrew M. Demetriou, Sandy Manolios, Cynthia C. S. Liem

{"title":"Beyond Explicit Reports: Comparing Data-Driven Approaches to Studying Underlying Dimensions of Music Preference","authors":"Jaehun Kim, Andrew M. Demetriou, Sandy Manolios, Cynthia C. S. Liem","doi":"10.1145/3320435.3320462","DOIUrl":"https://doi.org/10.1145/3320435.3320462","url":null,"abstract":"Prior research from the field of music psychology has suggested that there are factors common to music preference beyond individual genres. Specifically, research has shown that self-reported ratings of preference for individual musical genres can be reduced to 4 or 5 dimensions, which in turn have been shown to correlate to relevant psychological constructs, such as personality. However, the number of dimensions emerging from multiple studies has varied despite the care taken in conducting such research. Data-driven approaches offer opportunities to further this line of research with actual listening data, at a scale and scope surpassing that of traditional psychological studies. Although listening data can be considered more direct and comprehensive evidence of listening preference, transforming this data into meaningful measurements is non-trivial. In the current paper, we report on investigations seeking to find interpretable underlying dimensions of music taste, using implicit large-scale listening data. Offering a critical reflection on potential researchers' degrees of freedom, we adopt an explicit systematic approach, investigating the impact of varying different parameters, analysis, and normalization techniques. More precisely, we consider various ways to extract listening preference information from two large, openly available datasets of music listening behavior, making use of principal component analysis and variational autoencoders to extract potential underlying dimensions. Results and implications are discussed in light of prior psychological theory, and the potential of user listening data to further research on music preference.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"17 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":"124970167","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}

引用次数: 0

ContextPlay: Evaluating User Control for Context-Aware Music Recommendation ContextPlay:评估用户对上下文感知音乐推荐的控制

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320445

Yucheng Jin, N. Htun, N. Tintarev, K. Verbert

引用次数: 21

Session details: ACM UMAP 2019 Main Track 会议详情:ACM UMAP 2019主会场

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3340228

D. Jannach, O. Santos

引用次数: 0

Personalized Recommendations for Music Genre Exploration 音乐流派探索个性化推荐

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320455

Yu Liang, M. Willemsen

{"title":"Personalized Recommendations for Music Genre Exploration","authors":"Yu Liang, M. Willemsen","doi":"10.1145/3320435.3320455","DOIUrl":"https://doi.org/10.1145/3320435.3320455","url":null,"abstract":"Most recommender systems generate recommendations to match the user's current preference. However, users sometimes might have the goal to develop new preferences away from their current preference and use the recommender to guide them towards it. In this paper, we asked users to select a new genre to explore and studied what kind of recommendations would be more helpful for users to start exploring this new music taste. Three different recommendation methods are tested: one non-personalized which recommends the most representative tracks of the genre, one personalized method which considers songs from the new genre that best matches users' current preferences, and one mixed method which makes a trade-off between the two approaches. A comparative design was used in a user experiment in which participants were asked to evaluate the differences between the personalized method/mixed method and the non-personalized baseline. The mixed method results in recommendations that are more accurate and representative for the new genre than the personalized method. Users' perceived helpfulness for exploring the new genre is positively related to both perceived accuracy and perceived representativeness of the recommended items. Besides, recommendations from the mixed method are perceived more helpful for users high on Musical Sophistication Index for Active Engagement (MSAE). To our knowledge, this is one of the first studies using a recommender system to support users' preference development, and provides insights in how recommender systems can help users attain new goals and tastes.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"171 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":"124155105","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}

引用次数: 12

Session details: Keynote & Invited Talks 会议详情:主题演讲和特邀演讲

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3340227

G. A. Papadopoulos, G. Samaras, Stephan Weibelzahl

引用次数: 0

Stuck? No worries!: Task-aware Command Recommendation and Proactive Help for Analysts 卡住了吗?没烦恼!任务感知命令推荐和分析师的主动帮助

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320477

Aadhavan M. Nambhi, Bhanu Prakash Reddy Guda, Aarsh Prakash Agarwal, Gaurav Verma, Harvineet Singh, I. Burhanuddin

引用次数: 3