Explore-exploit in top-N recommender systems via Gaussian processes

Hastagiri P. Vanchinathan, Isidor Nikolic, Fabio De Bona, Andreas Krause
2014 Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14  
We address the challenge of ranking recommendation lists based on click feedback by efficiently encoding similarities among users and among items. The key challenges are threefold: (1) combinatorial number of lists; (2) sparse feedback and (3) context dependent recommendations. We propose the CGPRANK algorithm, which exploits prior information specified in terms of a Gaussian process kernel function, which allows to share feedback in three ways: Between positions in a list, between items, and
more » ... tween contexts. Under our model, we provide strong performance guarantees and empirically evaluate our algorithm on data from two large scale recommendation tasks: Yahoo! news article recommendation, and Google books. In our experiments, our CGPRANK approach significantly outperforms state-of-the-art multi-armed bandit and learning-to-rank methods, with an 18% increase in clicks.
doi:10.1145/2645710.2645733 dblp:conf/recsys/VanchinathanNBK14 fatcat:fcg3jbnvhvbcvga5l6jg4qxjeq