| Resumo: | Human interaction with intelligent systems, services and devices generates large volumes of user-related data. This multi-source information can be used to build richer user profiles and improve personalisation. Our goal is to combine multi-source user-related data to create user profiles by assigning dynamic individual weights to the different sources. This paper describes the proposed multi-source user profiling methodology and illustrates its application with a film recommendation system. The contemplated data sources include: (i) personal history, (ii) explicit preferences (ratings); and (iii) social activities (likes, comments or shares). The MovieLens dataset was selected and adapted to assess our approach by comparing the recommendations generated with the standard and the proposed methodologies. In the standard approach, we calculate the best global weights to apply to the different profile sources and generate all user profiles, accordingly. In the proposed approach, we determine, for each user, individual weights for the different profile sources to combine the available data and build the user profile. As a whole, our approach proved to be an efficient solution to a complex problem by continuously updating the individual data source weights and improving the accuracy of the generated personalised multimedia recommendations.
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