Proceedings of the 9th ACM Conference on Recommender Systems最新文献_第9页

Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles 内容驱动的用户分析评论值得推荐的新闻和博客文章

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800186

Trapit Bansal, M. Das, C. Bhattacharyya

{"title":"Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles","authors":"Trapit Bansal, M. Das, C. Bhattacharyya","doi":"10.1145/2792838.2800186","DOIUrl":"https://doi.org/10.1145/2792838.2800186","url":null,"abstract":"We consider the problem of recommending comment-worthy articles such as news and blog-posts. An article is defined to be comment-worthy for a particular user if that user is interested to leave a comment on it. We note that recommending comment-worthy articles calls for elicitation of commenting-interests of the user from the content of both the articles and the past comments made by users. We thus propose to develop content-driven user profiles to elicit these latent interests of users in commenting and use them to recommend articles for future commenting. The difficulty of modeling comment content and the varied nature of users' commenting interests make the problem technically challenging. The problem of recommending comment-worthy articles is resolved by leveraging article and comment content through topic modeling and the co-commenting pattern of users through collaborative filtering, combined within a novel hierarchical Bayesian modeling approach. Our solution, Collaborative Correspondence Topic Models (CCTM), generates user profiles which are leveraged to provide a personalized ranking of comment-worthy articles for each user. Through these content-driven user profiles, CCTM effectively handle the ubiquitous problem of cold-start without relying on additional meta-data. The inference problem for the model is intractable with no off-the-shelf solution and we develop an efficient Monte Carlo EM algorithm. CCTM is evaluated on three real world data-sets, crawled from two blogs, ArsTechnica (AT) Gadgets (102,087 comments) and AT-Science (71,640 comments), and a news site, DailyMail (33,500 comments). We show average improvement of 14% (warm-start) and 18% (cold-start) in AUC, and 80% (warm-start) and 250% (cold-start) in Hit-Rank@5, over state of the art.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114186047","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: 75

A Probabilistic Model for Using Social Networks in Personalized Item Recommendation 基于社会网络的个性化商品推荐的概率模型

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800193

A. Chaney, D. Blei, Tina Eliassi-Rad

引用次数: 177

Making the Most of Preference Feedback by Modeling Feature Dependencies 通过建模特征依赖来充分利用偏好反馈

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799678

S Chandra Mouli, Sutanu Chakraborti

引用次数: 10

"Please, Not Now!": A Model for Timing Recommendations “求求你，现在不行!”:时间推荐的模型

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799672

Nofar Dali Betzalel, Bracha Shapira, L. Rokach

引用次数: 18

Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study 评价标签推荐算法在现实世界的大众分类法:比较研究

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2799664

Dominik Kowald, E. Lex

引用次数: 21

HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems 混合推荐系统的灵活和可扩展的概率框架

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-16 DOI: 10.1145/2792838.2800175

Pigi Kouki, Shobeir Fakhraei, James R. Foulds, M. Eirinaki, L. Getoor

{"title":"HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems","authors":"Pigi Kouki, Shobeir Fakhraei, James R. Foulds, M. Eirinaki, L. Getoor","doi":"10.1145/2792838.2800175","DOIUrl":"https://doi.org/10.1145/2792838.2800175","url":null,"abstract":"As the amount of recorded digital information increases, there is a growing need for flexible recommender systems which can incorporate richly structured data sources to improve recommendations. In this paper, we show how a recently introduced statistical relational learning framework can be used to develop a generic and extensible hybrid recommender system. Our hybrid approach, HyPER (HYbrid Probabilistic Extensible Recommender), incorporates and reasons over a wide range of information sources. Such sources include multiple user-user and item-item similarity measures, content, and social information. HyPER automatically learns to balance these different information signals when making predictions. We build our system using a powerful and intuitive probabilistic programming language called probabilistic soft logic, which enables efficient and accurate prediction by formulating our custom recommender systems with a scalable class of graphical models known as hinge-loss Markov random fields. We experimentally evaluate our approach on two popular recommendation datasets, showing that HyPER can effectively combine multiple information types for improved performance, and can significantly outperform existing state-of-the-art approaches.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125253554","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: 99

Dynamic Poisson Factorization 动态泊松分解

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-09-15 DOI: 10.1145/2792838.2800174

Laurent Charlin, R. Ranganath, James McInerney, D. Blei

{"title":"Dynamic Poisson Factorization","authors":"Laurent Charlin, R. Ranganath, James McInerney, D. Blei","doi":"10.1145/2792838.2800174","DOIUrl":"https://doi.org/10.1145/2792838.2800174","url":null,"abstract":"Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed pref- erences and behaviors of users are assumed to be generated without order. These assumptions limit the explorative and predictive capabilities of such models, since users' interests and item popularity may evolve over time. To address this, we propose dPF, a dynamic matrix factorization model based on the recent Poisson factorization model for recommendations. dPF models the time evolving latent factors with a Kalman filter and the actions with Poisson distributions. We derive a scalable variational inference algorithm to infer the latent factors. Finally, we demonstrate dPF on 10 years of user click data from arXiv.org, one of the largest repository of scientific papers and a formidable source of information about the behavior of scientists. Empirically we show performance improvement over both static and, more recently proposed, dynamic recommendation models. We also provide a thorough exploration of the inferred posteriors over the latent variables.","PeriodicalId":325637,"journal":{"name":"Proceedings of the 9th ACM Conference on Recommender Systems","volume":"43 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120876696","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: 94

Fast Differentially Private Matrix Factorization 快速微分私有矩阵分解

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 2015-05-06 DOI: 10.1145/2792838.2800191

Ziqi Liu, Yu-Xiang Wang, Alex Smola

引用次数: 112

Proceedings of the 9th ACM Conference on Recommender Systems 第九届ACM推荐系统会议论文集

Proceedings of the 9th ACM Conference on Recommender Systems Pub Date : 1900-01-01 DOI: 10.1145/2792838

引用次数: 14