A Context-Aware Recommender Method Based on Text Mining

Autor(es)

Sundermann, Camila Vaccari ; de Padua, Renan ; Tonon, Vitor Rodrigues ; Domingues, Marcos Aurelio ; Rezende, Solange Oliveira ; Oliveira, PM ; Novais, P ; Reis, LP

• Tipo de documento: Artigo Científico
• Fonte: PROGRESS IN ARTIFICIAL INTELLIGENCE, PT II; v. 11805, p. 12-pg., 2019-01-01.

Resumo

A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. In their traditional form, recommender systems do not consider information that might enrich the recommendation process, as contextual information. In this way, we have 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 the contextual one. Thus, in this paper, we propose a context-aware recommender method based on text mining (CARM-TM) that includes two context extraction techniques: (1) CIET.5(embed), a technique based on word embeddings; and (2) Rules Context, a technique based on association rules. For this work, CARM-TM makes use of context by running the CAMF algorithm, a context-aware recommender system based on matrix factorization. To evaluate our method, we compare it against the MF algorithm, an uncontextual recommender system based on matrix factorization. The evaluation showed that our method presented better results than the MF algorithm in most cases. (AU)

• 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