Communication Dans Un Congrès Année : 2020

Bandits Under the Influence

Résumé

Recommender systems should adapt to user interests as the latter evolve. A prevalent cause for the evolution of user interests is the influence of their social circle. In general, when the interests are not known, online algorithms that explore the recommendation space while also exploiting observed preferences are preferable. We present online recommendation algorithms rooted in the linear multi-armed bandit literature. Our bandit algorithms are tailored precisely to recommendation scenarios where user interests evolve under social influence. In particular, we show that our adaptations of the classic LinREL and Thompson Sampling algorithms maintain the same asymptotic regret bounds as in the non-social case. We validate our approach experimentally using both synthetic and real datasets.

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Dates et versions

hal-03189995 , version 1 (05-04-2021)

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Citer

Silviu Maniu, Stratis Ioannidis, Bogdan Cautis. Bandits Under the Influence. 2020 IEEE International Conference on Data Mining (ICDM), Nov 2020, Sorrento, Italy. pp.1172-1177, ⟨10.1109/ICDM50108.2020.00144⟩. ⟨hal-03189995⟩
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