CoBaR: Confidence-Based Recommender

Autor: Neto, Fernando S. Aguiar, da Costa, Arthur F., Manzato, Marcelo G.

Neighborhood-based collaborative filtering algorithms usually adopt a fixed neighborhood size for every user or item, although groups of users or items may have different lengths depending on users' preferences. In this paper, we propose an extension

Externí odkaz: http://arxiv.org/abs/1808.07089

Boosting collaborative filtering with an ensemble of co-trained recommenders

Autor: da Costa, Arthur F., Manzato, Marcelo G., Campello, Ricardo J.G.B.

Publikováno v: In Expert Systems With Applications January 2019 115:427-441

Ensemble Clustering Approaches Applied in Group-based Collaborative Filtering Supported by Multiple Users’ Feedback

Autor: da Costa, Arthur F., Manzato, Marcelo G., Campello, Ricardo J. G. B.

Publikováno v: Journal of Information and Data Management; v. 8, n. 3 (2017): JOURNAL OF INFORMATION AND DATA MANAGEMENT; 180
Journal of Information and Data Management; Vol 8 No 3 (2017): JOURNAL OF INFORMATION AND DATA MANAGEMENT; 180
Journal of Information and Data Management; v. 8 n. 3 (2017): JOURNAL OF INFORMATION AND DATA MANAGEMENT; 180

In this article, we extend our previous work based on group collaborative filtering to improve the quality of groups generated through clustering algorithms with different types of feedback. On the Web, users can interact with content in different wa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::2aac43416bead6b3f797feee04f5ed34
https://seer.ufmg.br/index.php/jidm/article/view/4560