Learning User Similarity and Rating Style for Collaborative Recommendation
Information Retrieval
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
IEEE Transactions on Knowledge and Data Engineering
A semantic-expansion approach to personalized knowledge recommendation
Decision Support Systems
Trigger to Switch Individual's Interest Toward Unconscious Preference
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
A collaborative filtering method based on artificial immune network
Expert Systems with Applications: An International Journal
Reflective visualisation and verbalisation of unconscious preference
International Journal of Advanced Intelligence Paradigms
Learning user similarity and rating style for collaborative recommendation
ECIR'03 Proceedings of the 25th European conference on IR research
Expert Systems with Applications: An International Journal
Hybrid personalized recommender system using centering-bunching based clustering algorithm
Expert Systems with Applications: An International Journal
A hybrid approach for personalized recommendation of news on the Web
Expert Systems with Applications: An International Journal
Using latent class models for neighbors selection in collaborative filtering
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
A social network-based approach to expert recommendation system
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Smoothing approach to alleviate the meager rating problem in collaborative recommender systems
Future Generation Computer Systems
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With the advent of the World Wide Web, providing just-in-time personalized product recommendations to customers now becomes possible. Collaborative recommender systems utilize correlation between customer preference ratings to identify "like-minded" customers and predict their product preference. One factor determining the success of the recommender systems is the prediction accuracy, which in many cases is limited by lacking adequate ratings (the sparsity problem). Recently, the use of latent class model (LCM) has been proposed to alleviate this problem. In this paper, we first study how the LCM can be extended to handle customers and products outside the training set. In addition, we propose the use of a pair of LCMs (called dual latent class model-DLCM), instead of a single LCM, to model customers' likes and dislikes separately for enhancing the prediction accuracy. Experimental results based on the EachMovie dataset show that DLCM outperforms both LCM and the conventional correlation-based method when the available ratings are sparse.