Explore, exploit, and explain: personalizing explainable recommendations with bandits

By James McInerney, B. Lacker, Samantha Hansen, Karl Higley, Hugues Bouchard, Alois Gruson, Rishabh Mehrotra. 2018

185 citations recorded by the source corpus.

The multi-armed bandit is an important framework for balancing exploration with exploitation in recommendation. Exploitation recommends content (e.g., products, movies, music playlists) with the highest predicted user engagement and has traditionally been the focus of recommender systems. Exploration recommends content with uncertain predicted user engagement for the purpose of gathering more information. The importance of exploration has been recognized in recent years, particularly in settings with new users, new items, non-stationary preferences and attributes. In parallel, explaining recommendations ("recsplanations") is crucial if users are to understand their recommendations. Existing work has looked at bandits and explanations independently. We provide the first method that combines both in a principled manner. In particular, our method is able to jointly (1) learn which explanations each user responds to; (2) learn the best content to recommend for each user; and (3) balance exploration with exploitation to deal with uncertainty. Experiments with historical log data and tests with live production traffic in a large-scale music recommendation service show a significant improvement in user engagement.

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

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Citations in the corpus

Wireless Network Intelligence at the Edge (2018, methodology): “Processing the training operations together with other incumbent applications is another viable solution, in which their operation scheduling is optimized as done in MEC [199] and in the context of exploration–exploitation tradeoff [200].”
Explainable Recommendation: A Survey and New Perspectives (2018, background): “McInerney et al. (2018) developed a bandit approach to explainable recommendation.”
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph (2019, background): “blems. Recently, recommendation algorithms achieve remarkable improvements during these years with help of deep learning models [12, 14, 30, 47, 49] and the successful introducing of side-information [15, 19, 22, 23, 34]. In this study, we focus on the introducing of side-information in the knowledg”