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A simple multi-armed nearest-neighbor bandit for interactive recommendation

Publicado en:RecSys 2019 - 13th ACM Conference on Recommender Systems. 358-362 - 2019-09-10 (), DOI: 10.1145/3298689.3347040

Autores: Sanz-Cruzado J; Castells P; López E

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© 2019 Copyright is held by the authors. The cyclic nature of the recommendation task is being increasingly taken into account in recommender systems research. In this line, framing interactive recommendation as a genuine reinforcement learning problem, multi-armed bandit approaches have been increasingly considered as a means to cope with the dual exploitation/exploration goal of recommendation. In this paper we develop a simple multi-armed bandit elaboration of neighbor-based collaborative filtering. The approach can be seen as a variant of the nearest-neighbors scheme, but endowed with a controlled stochastic exploration capability of the users' neighborhood, by a parameter-free application of Thompson sampling. Our approach is based on a formal development and a reasonably simple design, whereby it aims to be easy to reproduce and further elaborate upon. We report experiments using datasets from different domains showing that neighbor-based bandits indeed achieve recommendation accuracy enhancements in the mid to long run.

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