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Poster
Online Reciprocal Recommendation with Theoretical Performance Guarantees
Claudio Gentile · Nikos Parotsidis · Fabio Vitale

Thu Dec 06 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #137
in Thu Poster Session B »

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 of both users. 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 those 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.

Keywords: Online Learning · Active Learning · Collaborative Filtering · Recommender Systems
[ Paper] 

Author Information

Claudio Gentile (INRIA)
Nikos Parotsidis (University of Rome Tor Vergata)
Fabio Vitale (Sapienza University of Rome)

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