GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
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
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Convergent algorithms for collaborative filtering
Proceedings of the 4th ACM conference on Electronic commerce
Recommendation Systems: A Probabilistic Analysis
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Collaboration of untrusting peers with changing interests
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Utility-based neighbourhood formation for efficient and robust collaborative filtering
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
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
Online collaborative filtering with nearly optimal dynamic regret
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Asynchronous recommendation systems
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Reputation, Trust and Recommendation Systems in Peer-to-Peer Systems
SIROCCO '08 Proceedings of the 15th international colloquium on Structural Information and Communication Complexity
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
Finding similar users in social networks: extended abstract
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Asynchronous active recommendation systems
OPODIS'07 Proceedings of the 11th international conference on Principles of distributed systems
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
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
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We consider a model of competitive recommendation systems proposed by Drineas et al. [4]. In recommendation systems (e.g., for books or movies), the system tracks which product each user chose in the past, and tries to deduce which other products an asking user is likely to be satisfied with. Obviously, recommendation systems can be effective only for users who share preferences with many other users. Such users are said to belong to a "dominant type." Current approaches to on-line recommendation systems involve using Singular Value Decomposition (SVD), which is computationally intensive and, more important, often applicable only under additional strong conditions. Specifically, correctness is guaranteed in [4] only if users of different dominant types essentially do not share a product they like ("type separability"), and only if the number of users in non-dominant types is significantly smaller than the number of users in dominant types ("gap assumption"). The complexity of that algorithm is O(mn), where m and n denote the number of users and products, respectively. In this paper, we show that in fact, very simple combinatorial algorithms can make good recommendations without using SVD. Our algorithms require neither the type separability nor the gap assumption, they are naturally amenable to distibuted computation, and their complexity is lower. In particular, the paper presents an O(m + n) time centralized algorithm and a distributed algorithm that can be implemented in a peer-to-peer model even in the presence of adaptively colluding malicious players, with only logarithmic over-head.