Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Using confidence bounds for exploitation-exploration trade-offs
The Journal of Machine Learning Research
SNACK: incorporating social network information in automated collaborative filtering
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
IEEE Transactions on Knowledge and Data Engineering
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-armed bandit problems with dependent arms
Proceedings of the 24th international conference on Machine learning
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Tuning Bandit Algorithms in Stochastic Environments
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Linearly Parameterized Bandits
Mathematics of Operations Research
The Journal of Machine Learning Research
New objective functions for social collaborative filtering
Proceedings of the 21st international conference on World Wide Web
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Recommending items to new or "cold-start" users is a challenging problem for recommender systems. Collaborative filtering approaches fail when the preference history of users is not available. A promising direction that has been explored recently [12] is to utilize the information in the social 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 social network. We propose two novel strategies leveraging neighborhood estimates to improve the learning rate of bandits for cold-start users. Our first strategy, MixPair, combines estimates from pairs of neighboring bandits. It extends the well-known UCB1 algorithm [5] and inherits its asymptotically optimal guarantees. Although our second strategy, MixNeigh, is a heuristic based on consensus in the neighborhood 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.