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
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)
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) is important in the e-business era as it can help business companies to predict customer preferences. However, Sparsity is still a major problem preventing it from achieving better effectiveness. Lots of ratings in the training matrix are unknown. Few current CF methods try to fill in those blanks before predicting the ratings of an active user. In this work, we have validated the effectiveness of matrix filling methods for the collaborative filtering. Moreover, we have tried three different matrix filling methods based on the whole training dataset and their clustered subsets with different weights to show the different effects. By comparison, we have analyzed the characteristics of those methods and have found that the mainstream method, Personality diagnosis (PD), can work better with most matrix filling method. Its MAE can reach 0.935 on a 2%-density EachMovie training dataset by item based matrix filling method, which is a 10.1% improvement. Similar improvements can be found both on EachMovie and MovieLens datasets. Our experiments also show that there is no need to do cluster-based matrix filling but the filled values should be assigned with a lower weight during the prediction process.