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arXiv · 1507.05371

Regret Guarantees for Item-Item Collaborative Filtering

Guy Bresler·Devavrat Shah·Luis F. Voloch
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Abstract

There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amounts to online binary matrix completion, where at each time a random user requests a recommendation and the algorithm chooses an entry to reveal in the user's row. The goal is to minimize regret, or equivalently to maximize the number of +1 entries revealed at any time. We analyze an item-item collaborative filtering algorithm that can achieve fundamentally better performance compared to user-user collaborative filtering. The algorithm achieves good "cold-start" performance (appropriately defined) by quickly making good recommendations to new users about whom there is little information.

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BibTeXRIS

Guy Bresler, Devavrat Shah, Luis F. Voloch. 2015-07-20. Regret Guarantees for Item-Item Collaborative Filtering. https://arxiv.org/abs/1507.05371

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