Abstract
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) RulesContext, 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.
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Acknowledgment
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brasil (CNPq) - grants #403648/2016-5 and #426663/2018-7, and Fundação Araucária de Apoio ao Desenvolvimento Científico e Tecnológico do Estado do Paraná - Brasil (FAPPR). The authors also would like to thank FAPESP (grant #2018/04651-0, São Paulo Research Foundation (FAPESP)).
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Sundermann, C.V., de Pádua, R., Tonon, V.R., Domingues, M.A., Rezende, S.O. (2019). A Context-Aware Recommender Method Based on Text Mining. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_32
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