Research Area: Machine Learning
The main goal of Aspect-Based Opinion Mining is to extract product aspects and the associated user opinions from the user text review. Although this serves as vital source information for enhancing rating prediction performance, few studies have attempted to fully utilize it for better accuracy of recommendation systems. Most of these studies typically assign equal weights to all aspects in the opinion mining process, however, in practices; users tend to give different priority on different aspects of the product when reaching overall ratings. In addition, most of the existing methods typically rely on handcrafted, rule-based or double propagation methods in the opinion mining process which are known to be time-consuming and often inclined to errors. This could affect the reliability and performance of the recommender systems (RS). Therefore, in this paper, we propose a weighted Aspect-based Opinion mining using Deep learning method for Recommender system (AODR) that can extract product aspects and the underlying weighted user opinions from the review text using a deep learning method and then fuse them into extended collaborative filtering (CF) technique for improving the RS. The proposed method is basically comprised of two components: (1) Aspect-based opinion mining module which aims to extract the product aspects from the review text to generate aspect rating matrix. (2) Recommendation generation component that uses tensor factorization (TF) technique to compute weighted aspect ratings and finally infer the overall rating prediction. We evaluate the proposed model in terms of both aspect extraction and recommendation performance. Experiment results on different datasets show that our AODR model achieves better results compared to the baselines.
Keywords:
Author(s) Name: AminuDau, Naomie Salim, Idris Rabiu, and Akram Osman
Journal name: Expert Systems with Applications
Conferrence name:
Publisher name: Elsevier B V
DOI: https://doi.org/10.1016/j.eswa.2019.112871
Volume Information: Volume 140
