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Mixing Bandits: A Recipe for Improved Cold-Start Recommendations in a Social Network
| Content Provider | CiteSeerX |
|---|---|
| Author | Caron, Stéphane Bhagat, Smriti |
| Abstract | Recommending items to new or “cold-start ” users is a chal-lenging problem for recommender systems. Collaborative filtering approaches fail when the preference history of users is not available. A promising direction that has been ex-plored recently [12] is to utilize the information in the so-cial networks of users to improve the quality of cold-start recommendations. That is, given that users are part of a social network, a new user shows up in the network with no preference history and limited social links, the recommender system tries to learn the user’s tastes as fast as possible. In this work, we model the learning of preferences of cold-start users using multi-armed bandits [5] embedded in a so-cial network. We propose two novel strategies leveraging neighborhood estimates to improve the learning rate of ban-dits for cold-start users. Our first strategy, MixPair, com-bines estimates from pairs of neighboring bandits. It extends the well-known UCB1 algorithm [5] and inherits its asymp-totically optimal guarantees. Although our second strategy, MixNeigh, is a heuristic based on consensus in the neighbor-hood of a user, it performed the best among the evaluated strategies. Our experiments on a dataset from Last.fm show that our strategies yield significant improvements, learning 2 to 5 times faster than our baseline, UCB1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Social Network Improved Cold-start Recommendation Cold-start User Preference History So-cial Network Recommender System Promising Direction Novel Strategy Multi-armed Bandit Significant Improvement Com-bines Estimate Learning Rate Neighborhood Estimate Cold-start Recommendation Evaluated Strategy Limited Social Link Collaborative Filtering Approach First Strategy Chal-lenging Problem New User Well-known Ucb1 Algorithm Second Strategy Asymp-totically Optimal Guarantee |
| Content Type | Text |
