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Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization最新文献

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Indirect Context Suggestion 间接上下文建议
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079654
Yong Zheng
{"title":"Indirect Context Suggestion","authors":"Yong Zheng","doi":"10.1145/3079628.3079654","DOIUrl":"https://doi.org/10.1145/3079628.3079654","url":null,"abstract":"Context suggestion refers to the task of recommending appropriate contexts to the users to improve the user experience. The suggested contexts could be time, location, companion, category, and so forth. In this paper, we particularly focus on the task of suggesting appropriate contexts to a user on a specific item. We evaluate the indirect context suggestion approaches over a movie data collected from user surveys, in comparison with direct context prediction approaches. Our experimental results reveal that indirect context suggestion is better and tensor factorization is generally the best way to suggest contexts to a user when given an item.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122752612","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
Are Item Attributes a Good Alternative to Context Elicitation in Recommender Systems? 在推荐系统中，项目属性是上下文引出的一个好选择吗?
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079651
A. L'Huillier, Sylvain Castagnos, A. Boyer
{"title":"Are Item Attributes a Good Alternative to Context Elicitation in Recommender Systems?","authors":"A. L'Huillier, Sylvain Castagnos, A. Boyer","doi":"10.1145/3079628.3079651","DOIUrl":"https://doi.org/10.1145/3079628.3079651","url":null,"abstract":"Context-aware recommendation became a major topic of interest within the recommender systems community as the context is crucial to provide the right items at the right moment. Many studies aim at developing complex models to include contextual factors in the recommendation process. Despite a real improvement on the recommendations quality, such contextual factors face users' privacy and data collection issues. We support the idea that context could be expressed in term of item attributes rather than contextual factors. To investigate that hypothesis, we designed an online experiment where 174 users were asked to describe the context in which they would listen the proposed songs for which we collected 12 musical attributes. We make available all the material collected during this study for research purposes and non-commercial use.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115338164","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
Item Contents Good, User Tags Better: Empirical Evaluation of a Food Recommender System 项目内容好，用户标签更好:食品推荐系统的实证评价
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079640
David Massimo, Mehdi Elahi, Mouzhi Ge, F. Ricci
{"title":"Item Contents Good, User Tags Better: Empirical Evaluation of a Food Recommender System","authors":"David Massimo, Mehdi Elahi, Mouzhi Ge, F. Ricci","doi":"10.1145/3079628.3079640","DOIUrl":"https://doi.org/10.1145/3079628.3079640","url":null,"abstract":"Traditional food recommender systems exploit items' ratings and descriptions in order to generate relevant recommendations for the users. While this data is important, it might not entirely capture the true users' preferences. In this paper, we analyse the performance of a food recommender that allows users to enter their preferences in the form of both ratings and tags, which are then used by a Matrix Factorization (MF) rating prediction model. The performed offline and online experiments have clarified the importance of user tags in comparison to content features. While item content contributes more to the quality of the prediction accuracy, user tags yields better ranking quality. Even more importantly, a live user study has revealed that a system variant, which leverages user tags in the prediction model and in the interface, achieves a significantly better user evaluation in terms of perceived effectiveness, choice satisfaction and choice difficulty.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115598045","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
Fine-Grained Open Learner Models: Complexity Versus Support 细粒度开放学习者模型:复杂性与支持
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079682
Julio Daniel Guerra Hollstein, Jordan Barria-Pineda, C. Schunn, S. Bull, Peter Brusilovsky
{"title":"Fine-Grained Open Learner Models: Complexity Versus Support","authors":"Julio Daniel Guerra Hollstein, Jordan Barria-Pineda, C. Schunn, S. Bull, Peter Brusilovsky","doi":"10.1145/3079628.3079682","DOIUrl":"https://doi.org/10.1145/3079628.3079682","url":null,"abstract":"Open Learner Models (OLM) show the learner model to users to assist their self-regulated learning by, for example, helping prompt reflection, facilitating planning and supporting navigation. OLMs can show different levels of detail of the underlying learner model, and can also structure the information differently. As a result, a trade-off may exist between the potential for better support for learning and the complexity of the information shown. This paper investigates students' perceptions about whether offering more and richer information in an OLM will result in more effective support for their self-regulated learning. In a first study, questionnaire responses relating to designs for six visualisations of varying complexity led to the implementation of three variations on one of the designs. A second controlled study involved students interacting with these variations. The study revealed that the most useful variation for searching for suitable learning material was a visualisation combining a basic coloured grid, an extended bar chart-like visualisation indicating related concepts, and a learning gauge.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122015430","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: 18
Modeling the Dynamics of Online News Reading Interests 网络新闻阅读兴趣动态建模
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079636
Elena V. Epure, B. Kille, Jon Espen Ingvaldsen, R. Deneckère, C. Salinesi, S. Albayrak
{"title":"Modeling the Dynamics of Online News Reading Interests","authors":"Elena V. Epure, B. Kille, Jon Espen Ingvaldsen, R. Deneckère, C. Salinesi, S. Albayrak","doi":"10.1145/3079628.3079636","DOIUrl":"https://doi.org/10.1145/3079628.3079636","url":null,"abstract":"Online news readers exhibit a very dynamic behavior. News publishers have been investigating ways to predict such changes in order to adjust their recommendation strategies and better engage the readers. Existing research focuses on analyzing the evolution of reading interests associated with news categories. Compared to these, we study also how relations among news interests change in time. Observations over a 10-month period on a German news publisher indicate that overall, the relations amid news categories change, but stable periods spanning months are also found. The reasons of these changes and how news publishers could integrate this knowledge in their solutions are subject to further investigation.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125118384","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: 3
The Influence of City Size on Dietary Choices and Food Recommendation 城市规模对饮食选择和食物推荐的影响
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079641
Hao Cheng, Markus Rokicki, E. Herder
{"title":"The Influence of City Size on Dietary Choices and Food Recommendation","authors":"Hao Cheng, Markus Rokicki, E. Herder","doi":"10.1145/3079628.3079641","DOIUrl":"https://doi.org/10.1145/3079628.3079641","url":null,"abstract":"Contextual features have been leveraged by recommender systems in many different domains. Traditional contextual features -- such as location and time -- have successfully been combined with collaborative filtering or content-based features. However, it is likely that there are other -- domain-specific -- features that may have even more impact. In this paper, we focus on the influence of city size on food preferences. Apart from location and time, our results show that city size can significantly boost the performance of food recommendation.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134474466","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
A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks 利用递归神经网络的基于内容推荐的深度架构
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079684
Alessandro Suglia, Claudio Greco, C. Musto, M. Degemmis, P. Lops, G. Semeraro
{"title":"A Deep Architecture for Content-based Recommendations Exploiting Recurrent Neural Networks","authors":"Alessandro Suglia, Claudio Greco, C. Musto, M. Degemmis, P. Lops, G. Semeraro","doi":"10.1145/3079628.3079684","DOIUrl":"https://doi.org/10.1145/3079628.3079684","url":null,"abstract":"In this paper we investigate the effectiveness of Recurrent Neural Networks (RNNs) in a top-N content-based recommendation scenario. Specifically, we propose a deep architecture which adopts Long Short Term Memory (LSTM) networks to jointly learn two embeddings representing the items to be recommended as well as the preferences of the user. Next, given such a representation, a logistic regression layer calculates the relevance score of each item for a specific user and we returns the top-N items as recommendations. In the experimental session we evaluated the effectiveness of our approach against several baselines: first, we compared it to other shallow models based on neural networks (as Word2Vec and Doc2Vec), next we evaluated it against state-of-the-art algorithms for collaborative filtering. In both cases, our methodology obtains a significant improvement over all the baselines, thus giving evidence of the effectiveness of deep learning techniques in content-based recommendation scenarios and paving the way for several future research directions.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133493458","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: 47
Recommender Systems as Multistakeholder Environments 作为多利益相关者环境的推荐系统
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079657
Himan Abdollahpouri, R. Burke, B. Mobasher
{"title":"Recommender Systems as Multistakeholder Environments","authors":"Himan Abdollahpouri, R. Burke, B. Mobasher","doi":"10.1145/3079628.3079657","DOIUrl":"https://doi.org/10.1145/3079628.3079657","url":null,"abstract":"Recommender systems are typically evaluated on their ability to provide items that satisfy the needs and interests of the end user. However, in many real world applications, users are not the only stakeholders involved. There may be a variety of individuals or organizations that benefit in different ways from the delivery of recommendations. In this paper, we re-define the recommender system as a multistakeholder environment in which different stakeholders are served by delivering recommendations, and we suggest a utility-based approach to evaluating recommendations in such an environment that is capable of distinguishing among the distributions of utility delivered to different stakeholders.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128929191","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: 60
Harvesting Entity-relation Social Networks from the Web: Potential and Challenges 从网络中获取实体关系社会网络:潜力和挑战
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079656
Saeed Amal, T. Kuflik, Einat Minkov
{"title":"Harvesting Entity-relation Social Networks from the Web: Potential and Challenges","authors":"Saeed Amal, T. Kuflik, Einat Minkov","doi":"10.1145/3079628.3079656","DOIUrl":"https://doi.org/10.1145/3079628.3079656","url":null,"abstract":"We describe a graph-based entity profiling system (GBEP) that extracts information about persons of interest from the Web and uses this information to construct a joint social graph. GBEP then employs graph-based measures to assess inter-personal relatedness, performing social recommendation. Importantly, GBEP provides detailed explanations for its suggestions in the form of relational connecting paths. Initial positive results were obtained for recommending related conference participants to each other using a joint social graph constructed for this purpose.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128688063","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
Adaptive City Characteristics: How Location Familiarity Changes What Is Regionally Descriptive 适应性城市特征:地点熟悉度如何改变区域描述
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization Pub Date : 2017-07-09 DOI: 10.1145/3079628.3079665
Vikas Kumar, Saeideh Bakhshi, L. Kennedy, David A. Shamma
{"title":"Adaptive City Characteristics: How Location Familiarity Changes What Is Regionally Descriptive","authors":"Vikas Kumar, Saeideh Bakhshi, L. Kennedy, David A. Shamma","doi":"10.1145/3079628.3079665","DOIUrl":"https://doi.org/10.1145/3079628.3079665","url":null,"abstract":"Proliferation of GPS-enabled mobile devices has brought a plurality of location-aware applications leveraging the location characteristics in the shared content, like photos and check-ins. While these applications provide contextual and relevant information, they also assume geo-tagged contents to be representative of the geo-bounded characteristics of location. In this paper, however, we show that the characteristics geo-tagged contents capture about a location can vary based on the familiarity of user (sharing the content) with the location. Using a large dataset of geo-tagged photos, we learn descriptive spatial photo characteristics and user temporal-location-familiarity to highlight unique characteristics photos capture of location, which vary significantly if taken by locals versus tourists. We then propose a ranking-approach to find most representative photos for a given city. A user-based evaluation shows photos are more diverse and characteristic of location compared to other popular baselines while being representative of how locals and tourists would describe the city.","PeriodicalId":216017,"journal":{"name":"Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125467587","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: 1
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