Conference Paper

Collaborate with strangers to find own preferences.

DOI: 10.1007/s00224-007-9016-7 Conference: SPAA 2005: Proceedings of the 17th Annual ACM Symposium on Parallelism in Algorithms and Architectures, July 18-20, 2005, Las Vegas, Nevada, USA
Source: DBLP

ABSTRACT Abstract We consider a model with n players and m objects. Each player has an unknown,grade for each object, modeled by a “preference vector” of length m. A player can learn his grade for an object by probing that object, but performing a probe incurs cost. The goal of the players is to learn their own evaluations of objects with minimal cost, by adopting the results of probes performed by other players. To facilitate communication, we assume that players collaborate by posting their grades for objects on a shared billboard: reading from the billboard is free. We consider players whose preference vectors are popular, i.e., players whose preferences are common to many other players. We present distributed and sequential algorithms to solve the problem with logarithmic cost overhead. Submitted as a regular presentation. Please consider as a brief announcement as well.

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    ABSTRACT: We consider a model of recommendation systems, where each member from a given set of players has a binary preference to each element in a given set of objects: in- tuitively, each player either likes or dislikes each object. However, the players do not know their preferences. To nd his preference of an object, a player may probe it, but each probe incurs unit cost. The goal of the play- ers is to learn their complete preference vector (approx- imately) while incurring minimal cost. This is possible if many players have similar preference vectors: such a set of players with similar \taste" may split the cost of probing all objects among them, and share the results of their probes by posting them on a public billboard. The problem is that players do not know a priori whose taste is close to theirs. In this paper we present a distributed randomized peer-to-peer algorithm in which each player outputs a vector which is close to the best possible ap-
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    ABSTRACT: We consider the following abstraction of recommendation systems. There are n players and m objects, and each player has an arbitrary binary preference grade ("likes" or "dislikes") for each object. The problem is that these preferences are not known, and the goal of the players is to discover their own preferences. To do that, a player can probe each object, thereby directly finding his preference grade for the objects. However, probing an object incurs cost. To save on cost, players post the results of their probes on a public billboard: writing and reading from the billboard is free. The idea is that cost can be reduced if players with similar preferences share the load of probing, but such similarities are not a priori known to the players. In a synchronous recommendation system, players probe in global rounds, and in an asynchronous system, players probe in an order determined by an arbitrary schedule. In this paper we present thefirst asynchronous recommendation systems that can reconstruct the preferences of players under adversarial asynchronous scheduling, with polylogarithmic overhead in cost with respect to the best possible. We present algorithms both for exact and approximate preference reconstructions.

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May 31, 2014