Communications of the ACM
User models: theory, method, and practice
International Journal of Man-Machine Studies
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
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
Latent semantic indexing: a probabilistic analysis
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Clustering in large graphs and matrices
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Fast computation of low rank matrix approximations
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
The Marketing Information Revolution
The Marketing Information Revolution
Fast Monte-Carlo Algorithms for finding low-rank approximations
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Recommendation Systems: A Probabilistic Analysis
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Fast Monte-Carlo Algorithms for Approximate Matrix Multiplication
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
On the value of private information
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
Pass efficient algorithms for approximating large matrices
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Robustness Analyses of Instance-Based Collaborative Recommendation
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Convergent algorithms for collaborative filtering
Proceedings of the 4th ACM conference on Electronic commerce
Sampling lower bounds via information theory
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
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
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Scale and Translation Invariant Collaborative Filtering Systems
Information Retrieval
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
Tensor-CUR decompositions for tensor-based data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Asynchronous recommendation systems
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Spectral clustering with limited independence
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
CRD: fast co-clustering on large datasets utilizing sampling-based matrix decomposition
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Collaborative Ranking: An Aggregation Algorithm for Individuals' Preference Estimation
AAIM '07 Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Competitive collaborative learning
Journal of Computer and System Sciences
An improved approximation algorithm for the column subset selection problem
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Finding similar users in social networks: extended abstract
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Manipulation-resistant collaborative filtering systems
Proceedings of the third ACM conference on Recommender systems
Foundations and Trends® in Theoretical Computer Science
Asynchronous active recommendation systems
OPODIS'07 Proceedings of the 11th international conference on Principles of distributed systems
Spectral methods for matrices and tensors
Proceedings of the forty-second ACM symposium on Theory of computing
Collaborative scoring with dishonest participants
Proceedings of the twenty-second annual ACM 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
Multiagent environment design in human computation
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
A novel protocol for communicating reputation in p2p networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
Competitive collaborative learning
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Improved collaborative filtering
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
Low rank approximation and regression in input sparsity time
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
| Hi-index | 0.00 |
A recommendation system tracks past purchases of a group of users to make product recommendations to individual members of the group. In this paper we present a notion of competitive recommendation systems, building on recent theoretical work on this subject. We reduce the problem of achieving competitiveness to a problem in matrix reconstruction. We then present a matrix reconstruction scheme that is competitive: it requires a small overhead in the number of users and products to be sampled, delivering in the process a net utility that closely approximates the best possible with full knowledge of all user-product preferences.