| Full text | |
| Author(s): |
Sundermann, Camila Vaccari
[1]
;
de Padua, Renan
[1]
;
Tonon, Vitor Rodrigues
[1]
;
Marcacini, Ricardo Marcondes
[1]
;
Domingues, Marcos Aurelio
[2]
;
Rezende, Solange Oliveira
[1]
Total Authors: 6
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo - Brazil
[2] Univ Estadual Maringa, Dept Informat, Maringa, Parana - Brazil
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | EXPERT SYSTEMS; v. 37, n. 6, SI AUG 2020. |
| Web of Science Citations: | 3 |
| 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; 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) | |
| FAPESP's process: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD) |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/04651-0 - Generating explanations in recommender systems based on matrix factorization techniques using context |
| Grantee: | Vítor Rodrigues Tonon |
| Support Opportunities: | Scholarships in Brazil - Master |