Online Reciprocal Recommendation with Theoretical Performance Guarantees
This addresses the challenge of matching users with mutual preferences in online platforms, representing a theoretical advance over traditional item-to-user recommendation.
The paper tackles the reciprocal recommendation problem where both users must have mutual interest, developing a sequential learning algorithm with theoretical guarantees that uncovers mutual likes at a pace comparable to an omniscient algorithm, and validates it with improved performance on synthetic and real-world datasets.
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 clearvoyant 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.