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
Coloring random and semi-random k-colorable graphs
Journal of Algorithms
Randomized algorithms
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
Communications of the ACM
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Competitive recommendation systems
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Latent Class Models for Collaborative Filtering
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Recommendation Systems: A Probabilistic Analysis
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Collaboration of untrusting peers with changing interests
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Using mixture models for collaborative filtering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Multi-Attribute Dynamic Pricing for Online Markets Using Intelligent Agents
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Improved recommendation systems
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Collaborate with strangers to find own preferences
Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures
Tell me who I am: an interactive recommendation system
Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures
A rigorous analysis of population stratification with limited data
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Using mixture models for collaborative filtering
Journal of Computer and System Sciences
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Finding similar users in social networks: extended abstract
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Recommender systems with non-binary grades
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
Improved collaborative filtering
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
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A collaborative filtering system analyzes data on the past behavior of its users so as to make recommendations --- a canonical example is the recommending of books based on prior purchases. The full potential of collaborative filtering implicitly rests on the premise that, as an increasing amount of data is collected, it should be possible to make increasingly high-quality recommendations. Despite the prevalence of this notion at an informal level, the theoretical study of such convergent algorithms has been quite limited.To investigate such algorithms, we generalize a model of collaborative filtering proposed by Kumar et al., in which the recommendations made by an algorithm are compared to those of an omniscient algorithm that knows the hidden preferences of users. Within our generalized model, we develop a recommendation algorithm with a strong convergence property --- as the amount of data increases, the quality of its recommendations approach those of the optimal omniscient algorithm. We also consider a further generalization, a mixture model proposed by Hofmann and Puzichaomt; here we prove that, in a natural sense, no recommendation algorithm can achieve convergent behavior with respect to the optimum.