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Online Reciprocal Recommendation with Theoretical Performance Guarantees

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Abstract

A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists. The problem thus is sharply different from the more traditional items-to-users recommendation, since a good match requires meeting the preferences at both sides. We initiate a rigorous theoretical investigation of the reciprocal recommendation task in a specific framework of sequential learning. We point out general limitations, formulate reasonable assumptions enabling effective learning and, under these assumptions, we design and analyze a computationally efficient algorithm that uncovers mutual likes at a pace comparable to that achieved by a clairvoyant algorithm knowing all user preferences in advance. Finally, we validate our algorithm against synthetic and real-world datasets, showing improved empirical performance over simple baselines.
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Dates and versions

hal-01916979 , version 1 (09-11-2018)

Identifiers

  • HAL Id : hal-01916979 , version 1

Cite

Fabio Vitale, Nikos Parotsidis, Claudio Gentile. Online Reciprocal Recommendation with Theoretical Performance Guarantees. NIPS 2018 - 32nd Conference on Neural Information Processing Systems, Dec 2018, Montreal, Canada. ⟨hal-01916979⟩
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