Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Lightweight Document Matching for Help-Desk Applications
IEEE Intelligent Systems
Leightweight Document Clustering
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Recommender System for Music CDs Using a Graph Partitioning Method
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
Internal link prediction: A new approach for predicting links in bipartite graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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A lightweight method for collaborative filtering is described that processes binary encoded data. Examples of transactions that can be described in this manner are items purchased by customers or web pages visited by individuals. As with all collaborative filtering, the objective is to match a person's records to customers with similar records. For example, based on prior purchases of a customer, one might recommend new items for purchase by examining stored records of other customers who made similar purchases. Because the data are binary (true-or-false) encoded, and not ranked preferences on a numerical scale, efficient and lightweight schemes are described for compactly storing data, computing similarities between new and stored records, and making recommendations tailored to an individual.