Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Journal of Artificial Intelligence Research
Probabilistic latent preference analysis for collaborative filtering
Proceedings of the 18th ACM conference on Information and knowledge management
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
Hi-index | 0.00 |
Most of the current recommender systems heavily rely on explicit user feedback such as ratings on items to model users' interests. However, in many applications, it is very hard to collect the explicit feedback, while implicit feedback such as user clicks may be more available. Furthermore, it is often more suitable for many recommender systems to address a ranking problem than a rating predicting problem. This paper proposes a latent pairwise preference learning (LPPL) approach for recommendation with implicit feedback. LPPL directly models user preferences with respect to a set of items rather than the rating scores on individual items, which are modeled with a set of features by analyzing clickthrough data available in many real-world recommender systems. The LPPL approach models both the latent variables of group structure of users and the pairwise preferences simultaneously. We conduct experiments on the testbed from a real-world recommender system and demonstrate that the proposed approach can effectively improve the recommendation performance against several baseline algorithms.