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@inproceedings{krause_yahoo, author = {Vanchinathan, Hastagiri P. and Nikolic, Isidor and De Bona, Fabio and Krause, Andreas}, title = {Explore-Exploit in Top-N Recommender Systems via Gaussian Processes}, year = {2014}, isbn = {9781450326681}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2645710.2645733}, doi = {10.1145/2645710.2645733}, abstract = {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 between 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, CGPRank significantly outperforms state-of-the-art multi-armed bandit and learning-to-rank methods, with an 18% increase in clicks.}, booktitle = {Proceedings of the 8th ACM Conference on Recommender Systems}, pages = {225–232}, numpages = {8}, keywords = {kernel methods, cgprank, recommender systems, user feedback, exploration-exploitation tradeoffs}, location = {Foster City, Silicon Valley, California, USA}, series = {RecSys '14} }