A context-aware recommender method based on text and opinion mining

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]

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

• 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