Implementing faceted classification for software reuse
Communications of the ACM - Special issue on software engineering
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
Communications of the ACM
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A hierarchy-aware approach to faceted classification of objected-oriented components
ACM Transactions on Software Engineering and Methodology (TOSEM)
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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A recommender system assists customers in product selection by matching client preferences to suitable items. This paper describes a preference matching technique for products categorized by a faceted feature classification scheme. Individual ratings of features and products are used to identify a customer's predictive neighborhood. A recommendation is obtained by an inferred ranking of candidate products drawn from the neighborhood. The technique addresses the problem of sparse customer activity databases characteristic of e-commerce. Product search is conducted in a controlled, effective manner based on customer similarity. The inference mechanism evaluates the probabilty that a candidate product satisfies a customer query. The inference algorithm is presented and illustrated by a practical example.