A finite algorithm for finding the projection of a point onto the Canonical simplex of Rn
Journal of Optimization Theory and Applications
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Turning down the noise in the blogosphere
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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We propose a distributed mechanism for finding websurfing strategies that is inspired by the StumbleUpon recommendation engine. Each day, a websurfer visits a sequence of websites recommended by our mechanism, and selects one that matches her daily interests. We formally show that even with this minimal feedback from the surfer--the selected website-- our mechanism finds a websurfing strategy that matches the surfer's interests optimally. The surfer does not need to know--or declare--what her daily interests are before she is presented with content she likes. Moreover, our mechanism is content-agnostic: it is oblivious to the nature of the content the surfer selects. In addition, we study how the performance of this mechanism can be improved if surfers with similar interests share their feedback. Such surfers can be found indirectly, e.g., if they are all registered as friends in a social networking application. Our analysis characterizes the improvement in the mechanism's accuracy, based on the size of the group and the degree of similarity between the surfers' interests. In particular, we show that sharing feedback can significantly accelerate the convergence of our mechanism. Our results are derived analytically using stochastic approximation techniques, but are also validated through a numerical study.