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Multiwinner election mechanisms for diverse personalized Bayesian recommendations for the tourism domain

Streviniotis Errikos, Chalkiadakis Georgios

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Summary

In this work, we employ several multiwinner voting rules from the social choice literature to the personalized recommendations problem. Specifically, we equip with such mechanisms a Bayesian recommender for the tourism domain, allowing for effective personalized recommendations while promoting diverse results with respect to travel-related features. Our system models both users and items-i.e., tourist points of interest (POIs)-as multivariate normal distributions. We employ a novel, lightweight preference elicitation process, during which the user is presented with and asked to rate a small number of POIs-related images. We then use these ratings to guide a Bayesian updating process of beliefs regarding the user's preferences. Moreover, we study the effectiveness of our approach when we equip our system with some prior knowledge regarding the (average) preferences of a specific tourists' type (i.e., tourists of a specific age group), given data collected via questionnaires from actual visitors of a popular tourist resort on a Greek island. Finally, we conduct a systematic experimental evaluation of our approach by applying it on a real-world dataset. Our results (i) highlight the ability of our system to successfully produce personalized recommendations that match the specific interests of a single user; (ii) confirm that the employment of prior knowledge regarding the preferences of tourists, based on their demographics, guides our recommender to avoid the cold-start problem; and (iii) demonstrate that the use of multiwinner mechanisms allows for diverse recommendations with respect to travel-related features, and increased system performance in the case of limited user-system interactions.

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