期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 18, 期 9, 页码 1910-1921出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2016.2575738
关键词
Item reputation; rating prediction; recommender system (RS); reviews; sentiment influence; user sentiment
资金
- Program 973 [2012CB316400]
- National Science Foundation of China [60903121, 61173109, 61332018]
- Microsoft Research Asia
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 user's preferences and make an accurate recommendation is crucial. Traditional recommender systems (RS) consider some factors, such as user's 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 user's sentiment on items/products. Secondly, we not only consider a user's 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 item's 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.
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