Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/4106
Title: Personalised combination of multi-source data for user profiling
Authors: Veloso, Bruno
Leal, Fátima
Malheiro, Benedita
Keywords: User modeling
Multi-source
Profiling
Recommender systems
Issue Date: 21-Apr-2022
Publisher: Springer
Citation: Veloso, B., Leal, F., & Malheiro, B. (2022). Personalised combination of multi-source data for user profiling. In A. Ullah, S. Anwar, Á. Rocha, S. Gill (Eds.), Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, (vol. 350, pp. 707-717). Springer. https://doi.org/10.1007/978-981-16-7618-5_60. Repositório Institucional UPT. http://hdl.handle.net/11328/4106
Series/Report no.: Lecture Notes in Networks and Systems;350
Abstract: 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 personalization. Our goal is to combine multi-source data to create user profiles by assigning dynamic individual weights. This paper describes a 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 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. The 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.
URI: http://hdl.handle.net/11328/4106
ISBN: 978-981-16-7617-8 (Print)
978-981-16-7618-5 (Online)
Appears in Collections:REMIT - Publicações em Livros de Atas Internacionais / Papers in International Proceedings

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