Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization最新文献_第2页

Session details: Doctoral Consortium I 会议详情:博士联盟I

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3258612

S. Berkovsky, V. Dimitrova

引用次数: 0

Data-Driven Destination Recommender Systems 数据驱动的目的地推荐系统

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3213591

Linus W. Dietz

引用次数: 11

Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation 基于矩阵分解的服装包装推荐约束

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209228

A. Wibowo, Advaith Siddharthan, J. Masthoff, Chenghua Lin

引用次数: 11

On Cultural-centered Graphical Passwords: Leveraging on Users' Cultural Experiences for Improving Password Memorability 以文化为中心的图形密码:利用用户文化体验提高密码记忆能力

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209254

Argyris Constantinides, Marios Belk, C. Fidas, G. Samaras

{"title":"On Cultural-centered Graphical Passwords: Leveraging on Users' Cultural Experiences for Improving Password Memorability","authors":"Argyris Constantinides, Marios Belk, C. Fidas, G. Samaras","doi":"10.1145/3209219.3209254","DOIUrl":"https://doi.org/10.1145/3209219.3209254","url":null,"abstract":"Adaptive user authentication policies are moving in the center of attention lately aiming to assist users in creating memorable and secure passwords. Focusing on graphical user authentication, state-of-the-art research has provided evidence that image-related attributes affect password memorability and security. Nonetheless, the effects of users' contemporary cultural-related memories towards password memorability and security have not been investigated so far, although it is known that user authentication is a cross-cultural task. Aiming to shed light on whether such effects exist, we conducted a study in which users created a graphical password with a contemporary culture-intensive vs. a culture-neutral image. Results indicate that image content related to one's cultural-related memories affects the interaction behavior during password composition, and consequently password memorability. Findings point towards a promising new direction for considering human contemporary cultural memories in the design of adaptive password policies to increase memorability and preserve security.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"12 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":"129173636","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: 19

Culture-Aware Music Recommendation 文化意识音乐推荐

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209258

Eva Zangerle, M. Pichl, M. Schedl

引用次数: 15

Enhancing Session-Based Recommendations through Sequential Modeling 通过顺序建模增强基于会话的推荐

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209259

Stéphane Martin, B. Faltings, Vincent Schickel

引用次数: 0

Perceived Persuasive Effect of Behavior Model Design in Fitness Apps 健身应用中行为模型设计的感知说服效应

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209240

Kiemute Oyibo, I. Adaji, Rita Orji, Babatunde Olabenjo, M. Azizi, Julita Vassileva

{"title":"Perceived Persuasive Effect of Behavior Model Design in Fitness Apps","authors":"Kiemute Oyibo, I. Adaji, Rita Orji, Babatunde Olabenjo, M. Azizi, Julita Vassileva","doi":"10.1145/3209219.3209240","DOIUrl":"https://doi.org/10.1145/3209219.3209240","url":null,"abstract":"Behavior modeling has become a very important behavior change technique employed in most fitness apps. However, its effect as a persuasive strategy on users has not been well investigated. Consequently, we conducted an empirical study among 669 participants to uncover: (1) how the perceived persuasiveness of behavior model design influences three social cognitive theory (SCT) determinants of behavior: self-efficacy, self-regulation and outcome expectation; and (2) the moderating effect of gender-based personalization. We based our study on user evaluation of prototypes of behavior models performing push-up and squat exercise behaviors as a case study. Our results show that, overall, the perceived persuasiveness of behavior models significantly influences all of the three SCT factors. The effect of persuasiveness on self-regulation (β = 0.42, p < 0.001) and outcome expectation (β = 0.41, p < 0.001) is stronger than on self-efficacy (β = 0.13, p < 0.05). Moreover, the behavior model design has a stronger effect on females' self-efficacy and males' outcome expectation if personalized to their gender. We discuss the implication of our findings.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"21 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":"114181188","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: 31

Explanations that are Intrinsic to Recommendations 对推荐的内在解释

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209230

Arpit Rana, D. Bridge

{"title":"Explanations that are Intrinsic to Recommendations","authors":"Arpit Rana, D. Bridge","doi":"10.1145/3209219.3209230","DOIUrl":"https://doi.org/10.1145/3209219.3209230","url":null,"abstract":"Explanations can give credibility to recommendations and help users to make better choices. In current recommender systems, explanation is a step that comes after recommendation. In this paper, we describe an approach that turns recommender systems on their head. In our approach, which we call Recommendation-by-Explanation (hr-by-e ), the system constructs a reason, or explanation, for recommending each candidate item; then it recommends those candidate items that have the best explanations. By unifying recommendation and explanation, r-by-e finds relevant recommendations with explanations that have a high degree of fidelity. We present the results of an offline experiment using a movie recommendation dataset. We show that r-by-e achieves higher precision than a comparable recommender, while both produce recommendations with roughly equal levels of diversity and serendipity. We also present the results of deploying a web-based system through which we have conducted two user trials. In one trial, we evaluate recommendation quality. Participants in this trial found r-by-e's recommendations to be more diverse, serendipitous and relevant than those of the competitor system. In another trial, we evaluate explanation quality. We used a re-rating task: users rated recommendations initially in the case where they were given only the explanation and not the identity of the movie, and then re-rated in the case where they were given information about the recommended movie. We found a stronger correlation between the pairs of ratings in the case of r-by-e. This suggests that r-by-e's explanations allow users to make more accurate judgments about the quality of recommended items.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"13 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":"121096632","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: 19

Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach 改进学习和减少时间:一种基于约束动作的强化学习方法

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209232

Shitian Shen, Markel Sanz Ausin, B. Mostafavi, Min Chi

{"title":"Improving Learning & Reducing Time: A Constrained Action-Based Reinforcement Learning Approach","authors":"Shitian Shen, Markel Sanz Ausin, B. Mostafavi, Min Chi","doi":"10.1145/3209219.3209232","DOIUrl":"https://doi.org/10.1145/3209219.3209232","url":null,"abstract":"Constrained action-based decision-making is one of the most challenging decision-making problems. It refers to a scenario where an agent takes action in an environment not only to maximize the expected cumulative reward but where it is subject to certain action-based constraints; for example, an upper limit on the total number of certain actions being carried out. In this work, we construct a general data-driven framework called Constrained Action-based Partially Observable Markov Decision Process (CAPOMDP) to induce effective pedagogical policies. Specifically, we induce two types of policies: CAPOMDPLG using learning gain as reward with the goal of improving students' learning performance, and CAPOMDPTime using time as reward for reducing students' time on task. The effectiveness of CAPOMDPLG is compared against a random yet reasonable policy and the effectiveness of CAPOMDPTime is compared against both a Deep Reinforcement Learning induced policy and a random policy. Empirical results show that there is an Aptitude-Treatment Interaction effect: students are split into High vs. Low based on their incoming competence; while no significant difference is found among the High incoming competence groups, for the Low groups, students following CAPOMDPTime indeed spent significantly less time than those using the two baseline policies and students following CAPOMDPLG significantly outperform their peers on both learning gain and learning efficiency.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"35 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":"129998791","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

A Hybrid Recommendation Approach for Open Research Datasets 开放研究数据集的混合推荐方法

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209250

A. Devaraju, S. Berkovsky

{"title":"A Hybrid Recommendation Approach for Open Research Datasets","authors":"A. Devaraju, S. Berkovsky","doi":"10.1145/3209219.3209250","DOIUrl":"https://doi.org/10.1145/3209219.3209250","url":null,"abstract":"Open data initiatives and policies have triggered a dramatic increase in the volume of available research data. This, in turn, has brought to the fore the challenge of helping users to discover relevant datasets. Research data repositories support data search primarily through keyword search and faceted navigation. However, these mechanisms may suit users, who are familiar with the structure and terminology of the repository. This raises the problem of personalized dataset recommendations for users unfamiliar with the repository or not able to clearly articulate their information needs. To this end, we present and evaluate in this paper a recommendation approach applied to a new task --- recommending research datasets. Our approach hybridizes content-based similarity with item-to-item co-occurrence, tuned to a feature weighting model obtained through a survey involving real users. We applied the approach in the context of a live research data repository and evaluated it in a user study. The obtained user judgments reveal the ability of the proposed approach to accurately quantify the relevance of datasets and they constitute an important step towards developing a practical dataset recommender.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"8 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":"128186400","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: 2