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

Kindness is Contagious: Study into Exploring Engagement and Adapting Persuasive Games for Wellbeing 善良是会传染的:研究探索参与和适应有说服力的游戏为幸福

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209233

Ana Ciocarlan, J. Masthoff, N. Oren

{"title":"Kindness is Contagious: Study into Exploring Engagement and Adapting Persuasive Games for Wellbeing","authors":"Ana Ciocarlan, J. Masthoff, N. Oren","doi":"10.1145/3209219.3209233","DOIUrl":"https://doi.org/10.1145/3209219.3209233","url":null,"abstract":"Intentional engagement in positive activities, such as practicing kindness, showing generosity or expressing gratitude, can help people increase their happiness levels and improve their wellbeing. In this paper we explore how a gamified digital behaviour change intervention can be adapted to encourage people of different personality types to engage in simple acts of kindness. Participants were assigned 5 daily activities for 7 days, and asked to complete as many as possible by the end of each day. Participants received different persuasive notifications every day to encourage them to complete a higher number of activities. We investigated how participation levels are influenced by different personality types, different persuasive message types and different categories of activities. Furthermore, we analysed the influence of the intervention on participant behaviour and the effect on behavioural intention, by comparing pre-intention and post-intention to perform different kinds of positive activities. The findings from this study have implications for future work on personalising persuasive interventions to improve wellbeing and prevent mental health problems.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"115 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":"116707139","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: 20

Towards Practical Link Prediction Approaches in Signed Social Networks 面向签名社会网络的实用链接预测方法

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3213595

Xiaoming Li

引用次数: 3

Session details: Keynote Address III 会议详情:主题演讲

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3258613

Li Chen

引用次数: 0

A Framework for Interaction-driven User Modeling of Mobile News Reading Behaviour 基于交互驱动的移动新闻阅读行为用户建模框架

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209229

Marios Constantinides, J. Dowell

引用次数: 16

Collaborative Filtering with Behavioral Models 行为模型协同过滤

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209235

Dusan Sovilj, S. Sanner, Harold Soh, Hanze Li

{"title":"Collaborative Filtering with Behavioral Models","authors":"Dusan Sovilj, S. Sanner, Harold Soh, Hanze Li","doi":"10.1145/3209219.3209235","DOIUrl":"https://doi.org/10.1145/3209219.3209235","url":null,"abstract":"Collaborative filtering (CF) has made it possible to build personalized recommendation models leveraging the collective data of large user groups, albeit with prescribed models that cannot easily leverage the existence of known behavioral models in particular settings. In this paper, we facilitate the combination of CF with existing behavioral models by introducing Bayesian Behavioral Collaborative Filtering (BBCF). BBCF works by embedding arbitrary (black-box) probabilistic models of human behavior in a latent variable Bayesian framework capable of collectively leveraging behavioral models trained on all users for personalized recommendation. There are three key advantages of BBCF compared to traditional CF and non-CF methods: (1) BBCF can leverage highly specialized behavioral models for specific CF use cases that may outperform existing generic models used in standard CF, (2) the behavioral models used in BBCF may offer enhanced intepretability and explainability compared to generic CF methods, and (3) compared to non-CF methods that would train a behavioral model per specific user and thus may suffer when individual user data is limited, BBCF leverages the data of all users thus enabling strong performance across the data availability spectrum including the near cold-start case. Experimentally, we compare BBCF to individual and global behavioral models as well as CF techniques; our evaluation domains span sequential and non-sequential tasks with a range of behavioral models for individual users, tasks, or goal-oriented behavior. Our results demonstrate that BBCF is competitive if not better than existing methods while still offering the interpretability and explainability benefits intrinsic to many behavioral models.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"45 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":"126881023","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: 4

Easy to Please: Separating User Experience from Choice Satisfaction 易于取悦:将用户体验与选择满意度分开

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209222

J. Schaffer, J. O'Donovan, Tobias Höllerer

{"title":"Easy to Please: Separating User Experience from Choice Satisfaction","authors":"J. Schaffer, J. O'Donovan, Tobias Höllerer","doi":"10.1145/3209219.3209222","DOIUrl":"https://doi.org/10.1145/3209219.3209222","url":null,"abstract":"Recommender systems are evaluated based on both their ability to create a satisfying user experience and their ability to help a user make better choices. Despite this, quantitative evidence from previous research in recommender systems indicate very high correlations between user experience attitudes and choice satisfaction. This might imply invalidity in the measurement methodologies of these constructs, whereas they may not be measuring what researchers think they are measuring. To remedy this, we present a new methodology for the measurement of choice satisfaction. Part of our approach is to measure a user's \"ease of satisfaction,\" or that user's natural propensity to be satisfied, which is measured using three different approaches. An (N=526) observational study is conducted wherein users browse a movie catalog. A factor analysis is done to assess the discriminant validity of our proposed choice satisfaction apparatus from user experience. A statistical analysis suggests that accounting for ease-of-satisfaction allows for a model of choice satisfaction that is not only discriminant, but independent, from user experience. This enables researchers to more objectively identify recommender system factors that lead users to good choices.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"735 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":"114825864","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

Modeling Learners' Cognitive and Affective States to Scaffold SRL in Open-Ended Learning Environments 开放式学习环境下学习者认知和情感状态建模与SRL构建

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209241

Anabil Munshi, R. Rajendran, Jaclyn L. Ocumpaugh, Gautam Biswas, R. Baker, L. Paquette

引用次数: 29

A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems 推荐系统中用户返回时间预测的贝叶斯点过程模型

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209261

Sherin Thomas, P. K. Srijith, M. Lukasik

引用次数: 2

PACELA

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209231

Thanh-Nam Doan, Ee-Peng Lim

{"title":"PACELA","authors":"Thanh-Nam Doan, Ee-Peng Lim","doi":"10.1145/3209219.3209231","DOIUrl":"https://doi.org/10.1145/3209219.3209231","url":null,"abstract":"Check-in prediction using location-based social network data is an important research problem for both academia and industry since an accurate check-in predictive model is useful to many applications, e.g. urban planning, venue recommendation, route suggestion, and context-aware advertising. Intuitively, when considering venues to visit, users may rely on their past observed visit histories as well as some latent attributes associated with the venues. In this paper, we therefore propose a check-in prediction model based on a neural framework called Preference and Context Embeddings with Latent Attributes (PACELA). PACELA learns the embeddings space for the user and venue data as well as the latent attributes of both users and venues. More specifically, we use a probabilistic matrix factorization-based technique to infer the latent attributes specific to users and locations in location-based social networks (LBSNs), considering the user visitation decisions that could be affected by area attraction, neighborhood competition, and social homophily. PACELA also includes a deep learning neural network to combine both embedding and latent features to predict if a user performs check-in on a location. Our experiments on three different real world datasets show that PACELA yields the best check-in prediction accuracy against several baseline methods.","PeriodicalId":110331,"journal":{"name":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization","volume":"16 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":"117025984","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: 4

Predicting Users' Personality from Instagram Pictures: Using Visual and/or Content Features? 从Instagram图片预测用户个性:使用视觉和/或内容特征?

Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization Pub Date : 2018-07-03 DOI: 10.1145/3209219.3209248

B. Ferwerda, M. Tkalcic

引用次数: 56