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Article Dans Une Revue Information Sciences Année : 2021

Gorthaur-EXP3: Bandit-based selection from a portfolio of recommendation algorithms balancing the accuracy-diversity dilemma

Résumé

Nowadays, real-world pervasive computing applications increasingly face multi-objective problems. This is the case for recommendation systems where, from a user's view point, recommended items must be both accurate and diverse. In recent years, model-based recommendation systems like those relying on Multi-Armed Bandit algorithms have been extensively studied. They are known to ensure theoretical guarantees of global accuracy. Nevertheless, despite these guarantees, the existing algorithms obtain different results depending on the application or on the dataset they operate on. Hence, when one needs to integrate such solutions, they should first be thoroughly evaluated to ensure the chosen method is efficient for the dynamic and potentially non-stationary nature of the target environments. However, human-based evaluations cost in time and money. Here, we propose a novel algorithm portfolio approach , Gorthaur-EXP3 aiming at automatically selecting the optimal algorithms which best maximise global accuracy and diversity of recommendations according to a prede-fined trade-off. Our method uses the EXP3 bandit algorithm which ensures a continuous exploration and a systematic exploitation of the best algorithm to apply in each situation it encounters. Gorthaur-EXP3 is an extension of the original Gorthaur method, which uses a roulette wheel selection, and obtains better results in most experimental cases.
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Dates et versions

hal-02947209 , version 1 (14-09-2022)

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Paternité - Pas d'utilisation commerciale

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Nicolas Gutowski, Tassadit Amghar, Olivier Camp, Fabien Chhel. Gorthaur-EXP3: Bandit-based selection from a portfolio of recommendation algorithms balancing the accuracy-diversity dilemma. Information Sciences, 2021, 546, pp.378-396. ⟨10.1016/j.ins.2020.08.106⟩. ⟨hal-02947209⟩
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