| Texto completo | |
| Autor(es): |
de Aguiar Neto, Fernando S.
[1]
;
da Costa, Arthur F.
[1]
;
Manzato, Marcelo G.
[1]
;
Campello, Ricardo J. G. B.
[1, 2]
Número total de Autores: 4
|
| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo - Brazil
[2] Univ Newcastle, Sch Math & Phys Sci, Callaghan, NSW - Australia
Número total de Afiliações: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INFORMATION SCIENCES; v. 534, p. 172-191, SEP 2020. |
| Citações Web of Science: | 0 |
| Resumo | |
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) | |
| Processo FAPESP: | 16/04798-5 - Abordagens de Pré-Processamento Aplicadas à Sistemas de Recomendação Baseados em Agrupamento de Dados |
| Beneficiário: | Fernando Soares de Aguiar Neto |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |
| Processo FAPESP: | 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria |
| Beneficiário: | Francisco Louzada Neto |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |
| Processo FAPESP: | 16/20280-6 - Organização Semântica de Anotações Colaborativas de Usuários Aplicada em Sistemas de Recomendação |
| Beneficiário: | Marcelo Garcia Manzato |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |