Proceedings of the 7th ACM conference on Recommender systems最新文献_第5页

Improving user profile with personality traits predicted from social media content 通过社交媒体内容预测的个性特征来改善用户档案

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507219

R. Gao, Bibo Hao, Shuotian Bai, Lin Li, Ang Li, T. Zhu

引用次数: 63

Top-N recommendations from implicit feedback leveraging linked open data 利用链接开放数据的隐式反馈提供的Top-N建议

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507172

V. Ostuni, T. D. Noia, R. Mirizzi, E. Sciascio

引用次数: 160

Workshop on recommender systems meet big data & semantic technologies: SeRSy 2013 推荐系统满足大数据和语义技术研讨会:SeRSy 2013

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508005

M. Degemmis, T. D. Noia, O. Lassila, P. Lops, Thomas Lukasiewicz, G. Semeraro

引用次数: 0

Effectiveness of the data generated on different time in latent factor model 潜在因素模型中不同时间生成数据的有效性

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507202

Qianru Zheng, H. Ip

{"title":"Effectiveness of the data generated on different time in latent factor model","authors":"Qianru Zheng, H. Ip","doi":"10.1145/2507157.2507202","DOIUrl":"https://doi.org/10.1145/2507157.2507202","url":null,"abstract":"User selection data accumulates as time goes by. Although the recent selections are usually assumed to have higher impact on the recommendation accuracy, empirical studies on this problem are limited. For old data, whether they can contribute to the recommendation accuracy is still to be determined. On one hand, changes in short-term user preference over time may limit their effectiveness in prediction, but on the other hand, one cannot rule out their potential in capturing long term user preferences. The result is important for the system owner to determine which data is useful to make the recommendation accurately. While there have been some related studies on the time dependency of data quality using neighbor-based CF methods (e.g., [4]), its effects remain unverified for other CF methods. In this paper, we study the effect of data generated over different time period on recommendation precision using several popular model-based CF algorithms (latent factor models). experiment results show that while more recent data expectedly have larger impacts, the usefulness of older data cannot be ignored as long as there are sufficient old samples. However, the addition of insufficient amount of old data seems to have negative impacts.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132987582","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

Session details: Technical session: methods, algorithms, and theory II 会议详情:技术会议:方法，算法和理论II

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/3249965

Alexandros Karatzoglou

引用次数: 0

Session details: Technical session: social media and recommender systems 技术部分:社交媒体和推荐系统

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/3249961

Jie Tang

引用次数: 0

Workshop on human decision making in recommender systems: decisions@RecSys'13 推荐系统中的人类决策研讨会:decisions@RecSys’13

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508002

Li Chen, M. Degemmis, A. Felfernig, P. Lops, F. Ricci, G. Semeraro, M. Willemsen

引用次数: 2

Query-driven context aware recommendation 查询驱动的上下文感知推荐

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2507187

N. Hariri, B. Mobasher, R. Burke

{"title":"Query-driven context aware recommendation","authors":"N. Hariri, B. Mobasher, R. Burke","doi":"10.1145/2507157.2507187","DOIUrl":"https://doi.org/10.1145/2507157.2507187","url":null,"abstract":"Context aware recommender systems go beyond the traditional personalized recommendation models by incorporating a form of situational awareness. They provide recommendations that not only correspond to a user's preference profile, but that are also tailored to a given situation or context. We consider the setting in which contextual information is represented as a subset of an item feature space describing short-term interests or needs of a user in a given situation. This contextual information can be provided by the user in the form of an explicit query, or derived implicitly. We propose a unified probabilistic model that integrates user profiles, item representations, and contextual information. The resulting recommendation framework computes the conditional probability of each item given the user profile and the additional context. These probabilities are used as recommendation scores for ranking items. Our model is an extension of the Latent Dirichlet Allocation (LDA) model that provides the capability for joint modeling of users, items, and the meta-data associated with contexts. Each user profile is modeled as a mixture of the latent topics. The discovered latent topics enable our system to handle missing data in item features. We demonstrate the application of our framework for article and music recommendation. In the latter case, the set of popular tags from social tagging Web sites are used for context descriptions. Our evaluation results show that considering context can help improve the quality of recommendations.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121537033","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: 39

Recommendation in social networks 社交网络中的推荐

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/2507157.2508062

M. Ester

{"title":"Recommendation in social networks","authors":"M. Ester","doi":"10.1145/2507157.2508062","DOIUrl":"https://doi.org/10.1145/2507157.2508062","url":null,"abstract":"1. TUTORIAL DESCRIPTION With the emergence of online social networks, academia and industry have explored ways to exploit the information in social networks to improve the quality of recommendations and to support new recommendation tasks. The underlying motivation is to capture the effects that govern the evolution of social networks, i.e. social influence, selection, correlational influence and transitivity, to enhance the typically very sparse rating matrix. Recommender systems exploiting a social network promise to outperform traditional recommenders in particular for cold-start (new) users who have not yet provided enough information about their preferences. After introducing the motivation and some of the practical applications, we discuss social networks and the main factors affecting their evolution. We then review state-ofthe-art methods for item recommendation in social networks, both memory-based approaches and model-based approaches, in particular matrix factorization. We discuss friend recommendation, an important recommendation task that is unique to the context of social networks. We conclude the tutorial with a discussion of future research directions such as privacypreserving recommendations and social recommendation in distributed/peer-to-peer networks.","PeriodicalId":225573,"journal":{"name":"Proceedings of the 7th ACM conference on Recommender systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127508376","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

Session details: Demonstrations 会议详情:演示

Proceedings of the 7th ACM conference on Recommender systems Pub Date : 2013-10-12 DOI: 10.1145/3249969

Rong Zheng

引用次数: 0