| Texto completo | |
| Autor(es): |
Sundermann, Camila Vaccari
[1]
;
de Padua, Renan
[1]
;
Tonon, Vitor Rodrigues
[1]
;
Marcacini, Ricardo Marcondes
[1]
;
Domingues, Marcos Aurelio
[2]
;
Rezende, Solange Oliveira
[1]
Número total de Autores: 6
|
| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo - Brazil
[2] Univ Estadual Maringa, Dept Informat, Maringa, Parana - Brazil
Número total de Afiliações: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | EXPERT SYSTEMS; v. 37, n. 6, SI AUG 2020. |
| Citações Web of Science: | 3 |
| Resumo | |
A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context-aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous work, we proposed a context-aware recommendation method based on text mining (CARM-TM). The method includes two techniques to extract context from reviews:CIET.5(embed), a technique based on word embeddings; andRulesContext, a technique based on association rules. In this work, we have extended our previous method by includingCEOM, a new technique which extracts context by using aspect-based opinions. We call our extension of CARM-TOM (context-aware recommendation method based on text and opinion mining). To generate recommendations, our method makes use of the CAMF algorithm, a context-aware recommender based on matrix factorization. To evaluate CARM-TOM, we ran an extensive set of experiments in a dataset about restaurants, comparing CARM-TOM against the MF algorithm, an uncontextual recommender system based on matrix factorization; and against a context extraction method proposed in literature. The empirical results strongly indicate that our method is able to improve a context-aware recommender system. (AU) | |
| Processo FAPESP: | 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD) |
| Beneficiário: | Agma Juci Machado Traina |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 18/04651-0 - Geração de explicações em sistemas de recomendação baseados em técnicas de fatoração de matrizes utilizando contexto |
| Beneficiário: | Vítor Rodrigues Tonon |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |