GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
International Journal of Electronic Commerce
Incremental probabilistic latent semantic analysis for automatic question recommendation
Proceedings of the 2008 ACM conference on Recommender systems
Improving Collaborative Filtering Recommendations Using External Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
Collaborative filtering recommender systems
The adaptive web
An energy-efficient mobile recommender system
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting user interests for collaborative filtering: interests expansion via personalized ranking
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Cost-aware travel tour recommendation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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Nowadays, recommender systems are becoming increasingly important because they can filter noisy information and predict users' preferences. As a result, recommender system has become one of the key technologies for the emerging personalized information services. To these services, when making recommendations, the items' qualities, items' correlation, and users' preferences are all important factors to consider. However, traditional memory-based recommender systems, including the widely used user-oriented and item-oriented collaborative filtering methods, can not take all these information into account. Meanwhile, the model-based methods are often too complex to implement. To that end, in this paper we propose a Gaussian process based recommendation model, which can aggregate all of above factors into a unified system to make more appropriate and accurate recommendations. This model has a solid statistical foundation and is easy to implement. Furthermore, it has few tunable parameters, therefore it is very suitable for a baseline algorithm. The experimental results on the MovieLens data set demonstrate the effectiveness of our method, and it outperforms several state-of-the-art algorithms.