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
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient k-NN search on vertically decomposed data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Using historical data to enhance rank aggregation
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Unified relevance models for rating prediction in collaborative filtering
ACM Transactions on Information Systems (TOIS)
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A comparison of user and system query performance predictions
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Diagnostic Evaluation of Information Retrieval Models
ACM Transactions on Information Systems (TOIS)
Predicting the performance of recommender systems: an information theoretic approach
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Image browsing: semantic analysis of NNk
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Probabilistic score normalization for rank aggregation
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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Neighbour-based collaborative filtering is a recommendation technique that provides meaningful and, usually, accurate recommendations. The method's success depends however critically upon the similarity metric used to find the most similar users (neighbours), the basis of the predictions made. In this paper, we explore twelve features that aim to explain why some user similarity metrics perform better than others. Specifically, we define two sets of features, a first one based on statistics computed over the distance distribution in the neighbourhood, and, a second one based on the nearest neighbour graph. Our experiments with a public dataset show that some of these features are able to correlate with the performance up to a 90%.