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Orientador(es)
Resumo(s)
This paper presents a prototype for a web application recommendation system’s content applied to movies’ recommendations. It
learns the pattern of user content consumption, predicting what he will consume in the future based on similar items to those he has shown interest. It considers similarity with neighbor users, thus creating a user model. Content-based filtering, collaborative filtering, and memory-based on hybrid filtering techniques are used. Content-based filtering allows to extract the fundamental features or attributes of the items and select similar items. Moreover, it proposes predicted classifications for the items of interest not yet classified by the active user. Collaborative filtering allows applying the kNN methodology to identify the similarity between the active user located in the neighborhood and propose predicted classifications for items of interest not yet classified. Hybrid filtering combines the two methodologies to overcome their drawbacks. A weighted approach is applied, allowing a dynamic linear combination of collaborative and content-based filtering. The results obtained were empirically relevant in the experimental evaluation, matching with the results presented in
similar studies validated with RMSE metrics.
Descrição
Palavras-chave
Information systems Recommender systems Human centered computing User Models Web-based interaction
Contexto Educativo
Citação
Jerónimo, M., Pinto, F.C., Duarte, R.P. (2023). Weight-Based Dynamic Hybrid Recommendation System for Web Application Content. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 464. Springer, Singapore. https://doi.org/10.1007/978-981-19-2394-4_2