Specialized Review Selection for Feature Rating Estimation

—On participatory Websites, users provide opinions about products, with both overall ratings and textual reviews. In this paper, we propose an approach to accurately estimate feature ratings of the products. This approach selects user reviews that extensively discuss specific features of the products (called specialized reviews), using information distance of reviews on the features. Experiments on real data show that overall ratings of the specialized reviews can be used to represent their feature ratings. The average of these overall ratings can be used by recommender systems to provide feature specific recommendations that better help users make purchasing decisions. Keywords-Data Mining; Text Mining; Kolmogorov Complexity; Information Distance

• Type: Conference
• Year: 2009
• Where: WEBI
• Authors: Chong Long, Jie Zhang, Minlie Huang, Xiaoyan Zhu, Ming Li, Bin Ma
• Keywords: Feature Ratings, Internet Technology, Overall Ratings, Users Provide Opinions, WEBI 2009