Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Online evolutionary collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Adapting neighborhood and matrix factorization models for context aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Factorization models for context-/time-aware movie recommendations
Proceedings of the Workshop on Context-Aware Movie Recommendation
Improving one-class collaborative filtering by incorporating rich user information
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Matrix co-factorization for recommendation with rich side information and implicit feedback
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Item popularity and recommendation accuracy
Proceedings of the fifth ACM conference on Recommender systems
Multi-value probabilistic matrix factorization for IP-TV recommendations
Proceedings of the fifth ACM conference on Recommender systems
Adaptive social similarities for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Informative household recommendation with feature-based matrix factorization
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
User graph regularized pairwise matrix factorization for item recommendation
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Build your own music recommender by modeling internet radio streams
Proceedings of the 21st international conference on World Wide Web
Feature based informative model for discriminating favorite items from unrated ones
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Pareto-efficient hybridization for multi-objective recommender systems
Proceedings of the sixth ACM conference on Recommender systems
On top-k recommendation using social networks
Proceedings of the sixth ACM conference on Recommender systems
Alternating least squares for personalized ranking
Proceedings of the sixth ACM conference on Recommender systems
CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
Proceedings of the sixth ACM conference on Recommender systems
Constrained collective matrix factorization
Proceedings of the sixth ACM conference on Recommender systems
A live comparison of methods for personalized article recommendation at forbes.com
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Incorporating popularity in topic models for social network analysis
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Sentiment analysis of user comments for one-class collaborative filtering over ted talks
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Optimizing top-n collaborative filtering via dynamic negative item sampling
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Heat pump detection from coarse grained smart meter data with positive and unlabeled learning
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages
Proceedings of the 22nd international conference on World Wide Web
One-class collaborative filtering with random graphs
Proceedings of the 22nd international conference on World Wide Web
Scientific articles recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Evaluation of recommendations: rating-prediction and ranking
Proceedings of the 7th ACM conference on Recommender systems
Pairwise learning in recommendation: experiments with community recommendation on linkedin
Proceedings of the 7th ACM conference on Recommender systems
Sage: recommender engine as a cloud service
Proceedings of the 7th ACM conference on Recommender systems
GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Improving pairwise learning for item recommendation from implicit feedback
Proceedings of the 7th ACM international conference on Web search and data mining
Cost-Aware Collaborative Filtering for Travel Tour Recommendations
ACM Transactions on Information Systems (TOIS)
A survey of collaborative filtering based social recommender systems
Computer Communications
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Many applications of collaborative filtering (CF), such as news item recommendation and bookmark recommendation, are most naturally thought of as one-class collaborative filtering (OCCF) problems. In these problems, the training data usually consist simply of binary data reflecting a user's action or inaction, such as page visitation in the case of news item recommendation or webpage bookmarking in the bookmarking scenario. Usually this kind of data are extremely sparse (a small fraction are positive examples), therefore ambiguity arises in the interpretation of the non-positive examples. Negative examples and unlabeled positive examples are mixed together and we are typically unable to distinguish them. For example, we cannot really attribute a user not bookmarking a page to a lack of interest or lack of awareness of the page. Previous research addressing this one-class problem only considered it as a classification task. In this paper, we consider the one-class problem under the CF setting. We propose two frameworks to tackle OCCF. One is based on weighted low rank approximation; the other is based on negative example sampling. The experimental results show that our approaches significantly outperform the baselines.