Journal of Management Information and Decision Sciences (Print ISSN: 1524-7252; Online ISSN: 1532-5806)


Product Recommendation System Using Weighted Factors and Sentiment Analysis.

Author(s): Raghuvanshi, R., Ilyas, MD., & Sharma, S.

Web mining is a subject of data mining for discovering patterns and knowledge from web data. In web mining web server access logs, page contents, and the application structures are used with data mining algorithms. In this paper, we introduce a web recommendation system to recommend relevant products to the end-users for the e-commerce platform. This technique enables us to recommend products to users even when the navigational patterns are not available on the access log. The approach describes seller and buyer’s behavior. Thus using web mining and real-world scenarios systems suggest the products to the users. In this context, sentiment analysis is also used. In order to develop this approach frequent pattern mining, and clustering algorithm is used. Additionally, the rating and reviews of the product are used for finding appropriate products for user. in order to recommend products, a weighted method has been used to combine different factors. The simulation of this approach has been developed using JAVA and experiments were carried out. The results demonstrate the acceptable performance of the system.

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