GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
The Journal of Machine Learning Research
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Evaluation methods for topic models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A random-walk based scoring algorithm applied to recommender engines
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
Cost-aware travel tour recommendation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with collective training
Proceedings of the fifth ACM conference on Recommender systems
Gaussian process for recommender systems
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Efficient personalized pagerank with accuracy assurance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with user ratings and tags
Proceedings of the 1st International Workshop on Context Discovery and Data Mining
Collaborative filtering with social regularization for TV program recommendation
Knowledge-Based Systems
Cost-Aware Collaborative Filtering for Travel Tour Recommendations
ACM Transactions on Information Systems (TOIS)
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In real applications, a given user buys or rates an item based on his/her interests. Learning to leverage this interest information is often critical for recommender systems. However, in existing recommender systems, the information about latent user interests are largely under-explored. To that end, in this paper, we propose an interest expansion strategy via personalized ranking based on the topic model, named iExpand, for building an interest-oriented collaborative filtering framework. The iExpand method introduces a three-layer, user-interest-item, representation scheme, which leads to more interpretable recommendation results and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues, such as the overspecialization and the cold-start problems. Finally, we evaluate iExpand on benchmark data sets, and experimental results show that iExpand outperforms state-of-the-art methods.