Μεταπτυχιακές Διατριβές

Μόνιμο URI για αυτήν τη συλλογήhttps://dspace.library.tuc.gr/handle/123456789/121

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Πλοήγηση Μεταπτυχιακές Διατριβές ανά Συγγραφέα "Chalkiadakis Georgios"

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    A bayesian personalized recommendation system
    (Πολυτεχνείο Κρήτης, 2014) Babas Konstantinos; Μπαμπας Κωνσταντινος; Chalkiadakis Georgios; Χαλκιαδακης Γεωργιος; Lagoudakis Michael; Λαγουδακης Μιχαηλ; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος
    In this research, we propose a novel Bayesian approach for personalized recommendations. We succeed in providing recommendations that are entirely personalized, based on a user’s past item “consumptions”, building a representative user model which reflects agent’s corresponding beliefs. Having a set of items, our agent has to select the one which better matches her beliefs about a specific user, in order to recommend it and receive the corresponding reward. In our approach, we model both user preferences and items under recommendation as multivariate Gaussian distributions; and make use of Normal-Inverse Wishart priors to model the recommendation agent beliefs about user types. We interpret user ratings in an innovative way, using them to guide a Bayesian updating process that helps us both capture a user’s current mood, and maintain her overall user type. We produced several variants of our approach, and applied them in the movie recommendations domain, evaluating them on data from the MovieLens dataset. We developed a generic & domain independent system, able to face the scalability challenge and able to capture user preferences (long-term and short-term). Moreover, we dealt with the exploration vs exploitation dilemma in this domain, via the application of various exploration algorithms (e.g., VPI exploration). Ours is a completely personalized approach, which exploits Bayesian Reinforcement Learning in order to recommend an item or a top-N group of items, without the need of ratings prediction. We do not employ a Collaborative Filtering or Content-based or Preference Elicitation technique, but we are still able to provide successful recommendations. Furthermore, we tackle the famous “cold-start” problem via the use of Bayesian and VPI explorations. Our algorithms are shown to be competitive against a state-of-the-art method, which nevertheless requires a minimum set of ratings from various users to provide recommendations --- unlike our entirely personalized approach.