A context‐aware recommender method based on text and opinion mining. Issue 6 (10th August 2020)

• Record Type: Journal Article
• Authors: Sundermann, Camila Vaccari; de Padua, Renan; Tonon, Vítor Rodrigues; Marcacini, Ricardo Marcondes; Domingues, Marcos Aurélio; Rezende, Solange Oliveira
• Other Names: Gonzalez‐Pardo Antonio guestEditor.; Tallón‐Ballesteros Antonio J. guestEditor.; Yin Hujun guestEditor.; Cortez Paulo guestEditor.; Bifet Albert guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-10
• Subjects: association rules -- context‐aware recommender systems -- opinion mining -- text mining -- word embedding; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12618
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: 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; and RulesContext, a technique based on association rules. In this work, we have extended our previous method by including CEOM, 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.