Research Area:  Data Mining

Abstract:

In recent years, we have witnessed a flourish of review websites. It presents a great opportunity to share our viewpoints for various products we purchase. However, we face an information overloading problem. How to mine valuable information from reviews to understand a users preferences and make an accurate recommendation is crucial. Traditional recommender systems (RS) consider some factors, such as users purchase records, product category, and geographic location. In this work, we propose a sentiment-based rating prediction method (RPS) to improve prediction accuracy in recommender systems. Firstly, we propose a social user sentimental measurement approach and calculate each users sentiment on items/products. Secondly, we not only consider a users own sentimental attributes but also take interpersonal sentimental influence into consideration. Then, we consider product reputation, which can be inferred by the sentimental distributions of a user set that reflect customers comprehensive evaluation. At last, we fuse three factors-user sentiment similarity, interpersonal sentimental influence, and items reputation similarity-into our recommender system to make an accurate rating prediction. We conduct a performance evaluation of the three sentimental factors on a real-world dataset collected from Yelp. Our experimental results show the sentiment can well characterize user preferences, which helps to improve the recommendation performance.

Keywords:  

Author(s) Name:  Xiaojiang Lei; Xueming Qian and Guoshuai Zhao

Publisher name:  IEEE

DOI:  10.1109/TMM.2016.2575738

Volume Information:  Volume: 18, Issue: 9, Sept. 2016,Page(s): 1910 - 1921

Paper Link:   https://ieeexplore.ieee.org/document/7484319