| Full text | |
| Author(s): |
de Aguiar Neto, Fernando S.
[1]
;
da Costa, Arthur F.
[1]
;
Manzato, Marcelo G.
[1]
;
Campello, Ricardo J. G. B.
[1, 2]
Total Authors: 4
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo - Brazil
[2] Univ Newcastle, Sch Math & Phys Sci, Callaghan, NSW - Australia
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | INFORMATION SCIENCES; v. 534, p. 172-191, SEP 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Recommender Systems (RS) support users to find relevant contents, such as movies, books, songs, and other products based on their preferences. Such preferences are gathered by analyzing past users' interactions, however, data collected for this purpose are typically prone to sparsity and high dimensionality. Clustering-based techniques have been proposed to handle those problems effectively and efficiently by segmenting the data into a number of similar groups based on predefined characteristics. Although such techniques have gained increasing attention in the recommender systems community, they are usually bound to a particular recommender system and/or require critical parameters, such as the number of clusters. In this paper, we present three variants of a general-purpose method to optimally extract users' groups from a hierarchical clustering algorithm, specifically targeting RS problems. The proposed extraction methods do not require critical parameters and enable any recommender algorithm to be used at the recommendation step. Our experiments have shown promising recommendation results in the context of nine well-known public datasets from different domains. (C) 2020 Elsevier Inc. All rights reserved. (AU) | |
| FAPESP's process: | 16/04798-5 - Pre-Processing Approaches Applied to Data Clustering Based Recommender Systems. |
| Grantee: | Fernando Soares de Aguiar Neto |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 16/20280-6 - Semantic Organization of Collaborative Users' Annotations Applied in Recommender Systems |
| Grantee: | Marcelo Garcia Manzato |
| Support Opportunities: | Regular Research Grants |