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

By Hastagiri P. Vanchinathan, Isidor Nikolic, Fabio De Bona, Andreas Krause. 2014

97 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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Citations in the corpus

How algorithmic confounding in recommendation systems increases homogeneity and decreases utility (2017, background): “Some investigators have modeled these systems using temporal dynamics [35] or framed them in an explore/exploit paradigm [38, 60].”
Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, methodology): “The techniques reviewed in this work have been successfully used for the recommendation of news articles [97], [38], [153].”
Towards Conversational Recommender Systems (2016, methodology): “[32] uses Gaussian processes to allow feedback sharing among similar contexts and arms.”
Constrained Bayesian Optimization for Automatic Chemical Design (2017, methodology): “e learning libraries [120, 44]. The myriad applications beyond hyperparameter tuning include A/B testing [58, 101, 13], environmental monitoring and sensor networks [115, 26, 74], Recommender systems [64, 13, 123], Natural Language Processing (NLP) [126, 129] and Statistical Machine Translation (SMT”