Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization最新文献_第4页

Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels 引文和主题标签推荐的多模态对抗性自动编码器

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209236

Lukas Galke, Florian Mai, Iacopo Vagliano, A. Scherp

引用次数: 21

Effects of Individual Traits on Diversity-Aware Music Recommender User Interfaces 个体特征对音乐推荐用户界面多样性的影响

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209225

Yucheng Jin, N. Tintarev, K. Verbert

{"title":"Effects of Individual Traits on Diversity-Aware Music Recommender User Interfaces","authors":"Yucheng Jin, N. Tintarev, K. Verbert","doi":"10.1145/3209219.3209225","DOIUrl":"https://doi.org/10.1145/3209219.3209225","url":null,"abstract":"When recommendations become increasingly personalized, users are often presented with a narrower range of content. To mitigate this issue, diversity-enhanced user interfaces for recommender systems have in the past found to be effective in increasing overall user satisfaction with recommendations. However, users may have different requirements for diversity, and consequently different visualization requirements. In this paper, we evaluate two visual user interfaces, SimBub and ComBub, to present the diversity of a music recommender system from different perspectives. SimBub is a baseline bubble chart that shows music genres and popularity by color and size, respectively. In addition, ComBub visualizes selected audio features along the X and Y axis in a more advanced and complex visualization. Our goal is to investigate how individual traits such as musical sophistication (MS) and visual memory (VM) influence the satisfaction of the visualization for perceived music diversity, overall usability, and support to identify blind-spots. We hypothesize that music experts, or people with better visual memory, will perceive higher diversity in ComBub than SimBub. A within-subjects user study (N=83) is conducted to compare these two visualizations. Results of our study show that participants with high MS and VM tend to perceive significantly higher diversity from ComBub compared to SimBub. In contrast, participants with low MS perceived significantly higher diversity from SimBub than ComBub; however, no significant result is found for the participants with low VM. Our research findings show the necessity of considering individual traits while designing diversity-aware interfaces.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131662477","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: 26

Gaze-Enhanced Student Modeling for Game-based Learning 基于游戏的学习的注视增强的学生建模

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209238

Andrew Emerson, R. Sawyer, R. Azevedo, James C. Lester

引用次数: 11

Personalisation and Privacy Issues in the Age of Exposure 曝光时代的个性化和隐私问题

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209271

Esma Aïmeur

引用次数: 3

Investigating the Decision-Making Behavior of Maximizers and Satisficers in the Presence of Recommendations 推荐存在下最大化者和满足者的决策行为研究

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209252

Michael Jugovac, Ingrid Nunes, D. Jannach

{"title":"Investigating the Decision-Making Behavior of Maximizers and Satisficers in the Presence of Recommendations","authors":"Michael Jugovac, Ingrid Nunes, D. Jannach","doi":"10.1145/3209219.3209252","DOIUrl":"https://doi.org/10.1145/3209219.3209252","url":null,"abstract":"Psychological theory distinguishes between maximizing and satisficing decision-making styles. Maximizers tend to explore more or all alternatives when making a choice, while satisficers evaluate options until they find one that is good enough. There is limited research that examines how the existence of a recommender influences the choice process and decisions of different types of decision-makers. We report the results of a controlled study, in which we monitored the choice process of participants when provided with automated recommendations and different types of additional information regarding available options. Our analyses show that none of the differences that were expected based on the literature manifested itself in the experiment. Maximizers neither inspected more items, nor invested more time to study them. Instead, like satisficers, they mostly picked one of the top-ranked items recommended by the system, which emphasizes the value of recommenders in particular for maximizers, who would otherwise face a more challenging decision problem. The analysis of the preferences of participants over different types of additional information revealed that highlighting key pros and cons was perceived as particularly helpful for the maximizers, an insight that can be used for the design of explanation approaches for recommenders.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121580672","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: 10

Intent-aware Item-based Collaborative Filtering for Personalised Diversification 基于意向感知项目的个性化多样化协同过滤

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209234

Jacek Wasilewski, N. Hurley

{"title":"Intent-aware Item-based Collaborative Filtering for Personalised Diversification","authors":"Jacek Wasilewski, N. Hurley","doi":"10.1145/3209219.3209234","DOIUrl":"https://doi.org/10.1145/3209219.3209234","url":null,"abstract":"Diversity has been identified as one of the key dimensions of recommendation utility that should be considered besides the overall accuracy of the system. A common diversification approach is to rerank results produced by a baseline recommendation engine according to a diversification criterion. The intent-aware framework is one of the frameworks that has been proposed for recommendations diversification. It assumes existence of a set of aspects associated with items, which also represent user intentions, and the framework promotes diversity across the aspects to address user expectations more accurately. In this paper we consider item-based collaborative filtering and suggest that the traditional view of item similarity is lacking a user perspective. We argue that user preferences towards different aspects should be reflected in recommendations produced by the system. We incorporate the intent-aware framework into the item-based recommendation algorithm by injecting personalised intent-aware covariance into the item similarity measure, and explore the impact of such change on the performance of the algorithm. Our experiments show that the proposed method improves both accuracy and diversity of recommendations, offering better accuracy/diversity tradeoff than existing solutions.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121823065","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: 21

Session details: Personalized Recommender Systems IV & Intelligent User Interfaces 会议细节:个性化推荐系统IV和智能用户界面

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3258614

R. Burke

引用次数: 0

Modeling Learners' Interest with a Domain-Independent Ontology-Based Framework 用基于领域的本体框架为学习者的兴趣建模

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3213598

Dekita Moon

引用次数: 0

A Dataset for Inferring Contextual Preferences of Users Watching TV 一个推断用户看电视语境偏好的数据集

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209263

M. S. Kristoffersen, S. Shepstone, Z. Tan

引用次数: 3

IDEA: Instant Detection of Eating Action using Wrist-Worn Sensors in Absence of User-Specific Model 想法:在没有用户特定模型的情况下，使用腕戴式传感器即时检测进食动作

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209265

Junghyo Lee, Prajwal Paudyal, Ayan Banerjee, Sandeep K. S. Gupta

引用次数: 2