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Markov chain Monte Carlo for effective personalized recommendations

Papilaris Michail-Aggelos, Chalkiadakis Georgios

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Year 2018
Type of Item Conference Full Paper
Bibliographic Citation M.-A. Papilaris and G. Chalkiadakis, "Markov chain Monte Carlo for effective personalized recommendations," in Multi-Agent Systems, vol. 11450, Lecture Notes in Computer Science, M. Slavkovik, Ed., Cham, Switzerland: Springer Nature, 2019, pp. 188-204. doi: 10.1007/978-3-030-14174-5_13
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This paper adopts a Bayesian approach for finding top recommendations. The approach is entirely personalized, and consists of learning a utility function over user preferences via employing a sampling-based, non-intrusive preference elicitation framework. We explicitly model the uncertainty over the utility function and learn it through passive user feedback, provided in the form of clicks on previously recommended items. The utility function is a linear combination of weighted features, and beliefs are maintained using a Markov Chain Monte Carlo algorithm. Our approach overcomes the problem of having conflicting user constraints by identifying a convex region within a user’s preferences model. Additionally, it handles situations where not enough data about the user is available, by exploiting the information from clusters of (feature) weight vectors created by observing other users’ behavior. We evaluate our system’s performance by applying it in the online hotel booking recommendations domain using a real-world dataset, with very encouraging results.