A Context-Aware Recommender Method Based on Text Mining

Sundermann, Camila Vaccari; Padua, Renan de; Tonon, Vitor Rodrigues; Domingues, Marcos Aurélio; Rezende, Solange Oliveira

doi:10.1007/978-3-030-30244-3_32

Cited by 2 publications

(8 citation statements)

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“…In Table 1, we summarize the works presented in this section. We verified that there are methods of context-aware recommender systems that do not consider the user opinion (Bauman & Tuzhilin, 2014;Hariri et al, 2011;Li et al, 2010;Sundermann et al, 2018Sundermann et al, , 2019. Besides, there are works that model user preferences at the item level and do not consider item aspects, that is, they only consider contextual information related to the overall evaluation of the item (Colace, De Santo, Greco, Amato, et al, 2015;Missaoui et al, 2017;Yang et al, 2015).…”

Section: Related Worksupporting

confidence: 57%

“…RulesContext Technique (Sundermann et al, 2019) F I G U R E 5 Exemplifying the RulesContext process. In this example, the input of technique is all the dataset reviews.…”

Section: F I G U R E 4 Overview Of Thementioning

confidence: 99%

“…Taking into account all these facts, in Sundermann, de Pádua, Tonon, Domingues, and Rezende (2019), 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.…”

Section: Introductionmentioning

confidence: 99%

“…As part of this work, we have proposed the CEOM technique, which extracts context by using aspect‐based opinions. Thus, different from work Sundermann et al (2019), our method now includes three different context extraction techniques: CIET.5 embed , RulesContext and CEOM . CARM‐TOM makes use of context to generate recommendations by running the CAMF algorithm, a context‐aware recommender based on matrix factorization.…”

Section: Introductionmentioning

confidence: 99%

“…As part of this work, we have proposed the CEOM technique, which extracts context by using aspect-based opinions. Thus, different from work Sundermann et al (2019), our method now includes three different context extraction techniques: CIET.5 embed , RulesContext and CEOM.…”

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confidence: 99%

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

et al. 2020

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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 contextaware 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.

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Section: Related Worksupporting

confidence: 57%

“…RulesContext Technique (Sundermann et al, 2019) F I G U R E 5 Exemplifying the RulesContext process. In this example, the input of technique is all the dataset reviews.…”

Section: F I G U R E 4 Overview Of Thementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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