Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Collaborative filtering and the generalized vector space model (poster session)
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
SWAMI (poster session): a framework for collaborative filtering algorithm development and evaluation
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Similarity measure and instance selection for collaborative filtering
WWW '03 Proceedings of the 12th 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)
Soft clustering criterion functions for partitional document clustering: a summary of results
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Hierarchical Clustering Algorithms for Document Datasets
Data Mining and Knowledge Discovery
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Collaborative Filtering (CF) techniques are important in the e-business era as vital components of many recommender systems, for they facilitate the generation of high-quality recommendations by leveraging the similar preferences of community users. However, there is still a major problem preventing CF algorithms from achieving better effectiveness, the sparsity of training data. Lots of ratings in the training matrix are not collected. Few current CF methods try to do data smoothing before predicting the ratings of an active user. In this work, we have validated the effectiveness of data smoothing for memory-based and hybrid collaborative filtering algorithms. Our experiments show that all these algorithms achieve a higher accuracy after proper smoothing. The average mean absolute error improvements of the three CF algorithms, Item Based, k Nearest Neighbor and Personality Diagnosis, are 6.32%, 8.85% and 38.0% respectively. Moreover, we have compared different smoothing methods to show which works best for each of the algorithms.