GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
TiVo: making show recommendations using a distributed collaborative filtering architecture
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
The effect of sparsity on collaborative filtering metrics
ADC '09 Proceedings of the Twentieth Australasian Conference on Australasian Database - Volume 92
User's latent interest-based collaborative filtering
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
When sparsity meets noise in collaborative filtering
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Collaborative filtering using multidimensional psychometrics model
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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Neighbourhood-based collaborative filtering is one of the most popular recommendation techniques, and has been applied successfully in various fields. User ratings are often used by neighbourhood-based collaborative filtering to compute the similarity between two users or items, but, user ratings may not always be representatives of their true preferences, resulting in unreliable similarity information and poor recommendation. To solve these problems, this paper proposes to use latent preferences for neighbourhood-based collaborative filtering instead of user ratings. Latent preferences are based on user latent interest estimated from ratings through a psychometric model. Experimental results show that latent preferences can improve the recommendation accuracy and coverage while lessening the prediction time of neighbourhood-based collaborative filtering by finding out reliable and effective neighbours; and latent preferences are better than user ratings for representing user preferences.