Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Fab: content-based, collaborative recommendation
Communications of the ACM
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Implicit user modeling for personalized search
Proceedings of the 14th ACM international conference on Information and knowledge management
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Mining long-term search history to improve search accuracy
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
One-Class Collaborative Filtering
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Collaborative filtering for orkut communities: discovery of user latent behavior
Proceedings of the 18th international conference on World wide web
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
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
MEET: a generalized framework for reciprocal recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
Social recommendation across multiple relational domains
Proceedings of the 21st ACM international conference on Information and knowledge management
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
iHR: an online recruiting system for Xiamen Talent Service Center
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Hi-index | 0.00 |
One-Class Collaborative Filtering (OCCF) is an emerging setup in collaborative filtering in which only positive examples or implicit feedback can be observed. Compared with the traditional collaborative filtering setting where the data has ratings, OCCF is more realistic in many scenarios when no ratings are available. In this paper, we propose to improve OCCF accuracy by exploiting the rich user information that is often naturally available in community-based interactive information systems, including a user's search query history, purchasing and browsing activities. We propose two ways to incorporate such user information into the OCCF models: one is to linearly combine scores from different sources and the other is to embed user information into collaborative filtering. Experimental results on a large-scale retail data set from a major e-commerce company show that the proposed methods are effective and can improve the performance of the One-Class Collaborative Filtering over baseline methods through leveraging rich user information.