Proceedings of the 10th ACM Conference on Recommender Systems最新文献_第8页

Mendeley: Recommendations for Researchers 门德利:给研究人员的建议

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959116

S. Vargas, Maya Hristakeva, Kris Jack

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引用次数: 11

Multi-corpus Personalized Recommendations on Google Play Google Play的多语料库个性化推荐

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959129

L. Koc, C. Master

引用次数: 0

RecSys Challenge 2016: Job Recommendations RecSys挑战2016:工作推荐

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959207

F. Abel, A. Benczúr, Daniel Kohlsdorf, M. Larson, Róbert Pálovics

引用次数: 45

Item-to-item Recommendations at Pinterest Pinterest上的逐项推荐

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959130

Stephanie Rogers

引用次数: 6

Feature Selection For Human Recommenders 人类推荐的特征选择

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959123

Katherine A. Livins

引用次数: 0

Contrasting Offline and Online Results when Evaluating Recommendation Algorithms 在评估推荐算法时对比离线和在线结果

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959176

Marco Rossetti, Fabio Stella, M. Zanker

引用次数: 73

Intent-Aware Diversification Using a Constrained PLSA 使用约束PLSA的意图感知多样化

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959177

Jacek Wasilewski, N. Hurley

引用次数: 26

Bayesian Personalized Ranking with Multi-Channel User Feedback 基于多渠道用户反馈的贝叶斯个性化排名

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959163

B. Loni, Roberto Pagano, M. Larson, A. Hanjalic

{"title":"Bayesian Personalized Ranking with Multi-Channel User Feedback","authors":"B. Loni, Roberto Pagano, M. Larson, A. Hanjalic","doi":"10.1145/2959100.2959163","DOIUrl":"https://doi.org/10.1145/2959100.2959163","url":null,"abstract":"Pairwise learning-to-rank algorithms have been shown to allow recommender systems to leverage unary user feedback. We propose Multi-feedback Bayesian Personalized Ranking (MF-BPR), a pairwise method that exploits different types of feedback with an extended sampling method. The feedback types are drawn from different \"channels\", in which users interact with items (e.g., clicks, likes, listens, follows, and purchases). We build on the insight that different kinds of feedback, e.g., a click versus a like, reflect different levels of commitment or preference. Our approach differs from previous work in that it exploits multiple sources of feedback simultaneously during the training process. The novelty of MF-BPR is an extended sampling method that equates feedback sources with \"levels\" that reflect the expected contribution of the signal. We demonstrate the effectiveness of our approach with a series of experiments carried out on three datasets containing multiple types of feedback. Our experimental results demonstrate that with a right sampling method, MF-BPR outperforms BPR in terms of accuracy. We find that the advantage of MF-BPR lies in its ability to leverage level information when sampling negative items.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128966792","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: 116

Group Recommender Systems 小组推荐系统

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959197

Ludovico Boratto

引用次数: 32

Learning Hierarchical Feature Influence for Recommendation by Recursive Regularization 递归正则化学习分层特征对推荐的影响

Proceedings of the 10th ACM Conference on Recommender Systems Pub Date : 2016-09-07 DOI: 10.1145/2959100.2959159

Jie Yang, Zhu Sun, A. Bozzon, Jie Zhang

引用次数: 23