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In this paper we propose a novel user model for personalized recommendation in domains, where items are described by multiple characteristics (e.g., metadata attributes) and users’ preferences are expressed on the level of items by some kind of explicit feedback (e.g., rating) or derived from implicit user feedback (e.g., time spent on items). The proposed user model is composed of two parts. The first one represents user preferences by items descriptive metadata (e.g., genres, keywords, actors and directors in movie domain used as an example in this paper) expressed as preference vectors. The second part is represented by a vector describing user’s preferences on previously rated items. Our main contribution is the addition of individual user preference measures to the vectors of both user model parts. User preferences expressed as the vector weights are calculated based on the global preference of large amount of users and also on individual variations in preferences of particular users to the average of the whole set of users. The weights reflect how much the different types of information are important to the user. We evaluated the proposed user model in movie domain through collaborative recommendation by comparing its performance to two reference user models using Movielens 10 M dataset.

User Preference Modeling by Global and Individual Weights for Personalized