Ontology and context based recommendation system using Neuro-Fuzzy Classification. (March 2019)

• Record Type: Journal Article
• Authors: Razia Sulthana, A.; Ramasamy, Subburaj
• Is Part Of: Computers & electrical engineering. Volume 74(2019)
• Page Start: 498
• Page End: 510
• Publication Date: 2019-03
• Subjects: Recommendation systems -- Context -- Domain ontology -- Neuro-fuzzy rules -- Classification -- Features set -- Weightage matrix -- Cold start; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.01.034
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Recommendation Systems (RS) identify the products of likely interest to the user. In earlier studies, the recommendation systems classify the reviews of products as positive or negative, verbatim on the features contained in the reviews, without reference to their context. In this paper, we develop an Ontology and Context Based Recommendation System (OCBRS) to assess the context of and determine the opinion of the review. We propose a Neuro-Fuzzy Classification approach using fuzzy rules to extract the context of the review. This approach automatically classifies the reviews under the respective fuzzy rule. Ontology facilitates a hierarchical and systematic methodology to group the context and acts as repository of context. The proposed approach appears to improve the accuracy of the RS.