Asynchronous Active Recommendation Systems

DOI: 10.1007/978-3-540-77096-1_4
Source: DBLP

ABSTRACT 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.

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