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
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Competitive recommendation systems
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Adaptive Collaboration in Peer-to-Peer Systems
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
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
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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We consider the following abstraction of recommendation systems. There are players and objects, and each player has an arbitrary binary preference grade ("likes" or "dislikes") for each object. The preferences are unknown at start. A player can find his grade for an object by "probing" it, but each probe incurs cost. The goal of a recommendation algorithm is to find the preferences of the players while minimizing cost. To save on cost, players post the results of their probes on a public "billboard" (writing and reading from the billboard is free). In asynchronous systems, an adversary controls the order in which players probe. Active algorithms get to tell players which objects to probe when they are scheduled. In this paper we present the first low-overhead algorithms that can provably reconstruct the preferences of players under asynchronous scheduling. "Low overhead" means that the probing cost is only a polylogarithmic factor over the best possible cost; and by "provably" we mean that the algorithm works with high probability (over internal coin tosses) for all inputs, assuming that each player gets some minimal number of probing opportunities. We present algorithms in this model for exact and approximate preference reconstruction.