Abstract
Identifying a neighbourhood based on multi-clusters was successfully applied to recommender systems, increasing recommendation accuracy and eliminating divergence related to differences in clustering schemes generated by traditional methods. Multi-Clustering Collaborative Fil- tering algorithm was developed for this purpose, which was described in the author’s previ- ous papers. However, the solutions involving many clusters face substantial challenges around memory consumption and scalability. Differently, some groups are not useful due to their high similarity to other ones. Selection of the clusters to provide to the recommender system’s in- put, without deterioration in recommendation accuracy, can be used as a precaution to address these problems. The article describes a solution of a clustering schemes’ selection based on internal indices evaluation. The results confirmed its positive impact on the system’s overall recommendation performance. They were compared with baseline recommenders’ outcomes.
Paper Type
Short Paper
DOI
10.62036/ISD.2023.51
Scheme Selection Based on Clusters’ Quality in Multi-Clustering M − CCF Recommender System
Identifying a neighbourhood based on multi-clusters was successfully applied to recommender systems, increasing recommendation accuracy and eliminating divergence related to differences in clustering schemes generated by traditional methods. Multi-Clustering Collaborative Fil- tering algorithm was developed for this purpose, which was described in the author’s previ- ous papers. However, the solutions involving many clusters face substantial challenges around memory consumption and scalability. Differently, some groups are not useful due to their high similarity to other ones. Selection of the clusters to provide to the recommender system’s in- put, without deterioration in recommendation accuracy, can be used as a precaution to address these problems. The article describes a solution of a clustering schemes’ selection based on internal indices evaluation. The results confirmed its positive impact on the system’s overall recommendation performance. They were compared with baseline recommenders’ outcomes.
Recommended Citation
Kuzelewska, U. (2023). Scheme Selection Based on Clusters’ Quality in Multi-Clustering M − CCF Recommender System. In A. R. da Silva, M. M. da Silva, J. Estima, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development, Organizational Aspects and Societal Trends (ISD2023 Proceedings). Lisbon, Portugal: Instituto Superior Técnico. ISBN: 978-989-33-5509-1. https://doi.org/10.62036/ISD.2023.51