Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/4106
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dc.contributor.authorVeloso, Bruno-
dc.contributor.authorLeal, Fátima-
dc.contributor.authorMalheiro, Benedita-
dc.date.accessioned2022-05-16T13:49:33Z-
dc.date.available2022-05-16T13:49:33Z-
dc.date.issued2022-04-21-
dc.identifier.citationVeloso, 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/11328/4106pt_PT
dc.identifier.isbn978-981-16-7617-8 (Print)-
dc.identifier.isbn978-981-16-7618-5 (Online)-
dc.identifier.urihttp://hdl.handle.net/11328/4106-
dc.description.abstractHuman 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.pt_PT
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relation.ispartofseriesLecture Notes in Networks and Systems;350-
dc.rightsrestrictedAccesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectUser modelingpt_PT
dc.subjectMulti-sourcept_PT
dc.subjectProfilingpt_PT
dc.subjectRecommender systemspt_PT
dc.titlePersonalised combination of multi-source data for user profilingpt_PT
dc.typeconferenceObjectpt_PT
dc.peerreviewedyespt_PT
degois.publication.firstPage707pt_PT
degois.publication.lastPage717pt_PT
degois.publication.volume350pt_PT
degois.publication.titleProceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systemspt_PT
dc.identifier.doihttps://doi.org/10.1007/978-981-16-7618-5_60pt_PT
Appears in Collections:REMIT - Publicações em Livros de Atas Internacionais / Papers in International Proceedings

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