A fusion of aspect and contextual information for rating prediction in recommender system using a latent factor model. (16th June 2021)

• Record Type: Journal Article
• Authors: Patel, Jitali; Chhinkaniwala, Hitesh
• Is Part Of: International journal of Web engineering and technology. Volume 16:Number 1(2021)
• Page Start: 30
• Page End: 52
• Publication Date: 2021-06-16
• Subjects: recommender system -- latent factor model -- aspect extraction -- sentiment analysis -- spam detection -- context-aware recommender system; World Wide Web -- Periodicals; Web site development -- Periodicals; Application software -- Development -- Periodicals; 006.7
• Languages: English
• ISSNs: 1476-1289

Abstract

Referring to reviews, checking online comments and, visiting different websites before buying any product is a call of the day. Online reviews are an excellent source of information both for users and organisations alike. In this article, a hybrid model, named as aspect and context-based latent factor model (ACMF), is proposed to predict user rating on an item based on star ratings provided by users, feature-opinion information, and context information. ACMF mainly consists of three phases: the first phase extracts spam reviews, the second phase extracts features and opinions from written reviews and calculates the polarity score of opinions. In the last phase, reviews and context information are aggregated to predict the unknown rating of a user for better recommendations. The proposed model is tested on ratings and reviews downloaded from the Amazon website. Experiment results show RMSE of ACMF has been achieved significantly less than other relevant methods.