Published Paper Details:

In recent years, shopping online is becoming more popular. When it need to decide whether to purchase a product or not on line, the opinions of others become important. It presents a great opportunity to share our viewpoints for various products purchase. However, people face the 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 consider some factors, such as user’s purchase records, product category, and geographic location. In this work, it proposes a sentiment-based rating prediction method to improve prediction accuracy in recommender systems. Firstly, it proposes a social user sentimental measurement approach and calculates each user’s sentiment on items. Secondly, it not only considers a user’s own sentimental attributes but also take interpersonal sentimental influence into consideration. Then, consider item reputation, which can be inferred by the sentimental distributions of a user set that reflect customers’ comprehensive evaluation. At last, by fusing three factors-user sentiment similarity, interpersonal sentimental influence, and item’s reputation similarity into recommender system to make an accurate rating prediction. It conducts a performance evaluation of the three sentimental factors on a real-world dataset. Experimental results show the sentiment can well characterize user preferences, which help to improve the recommendation performance.