Extracting Fine-Grained Service Value Features and Distributions for Accurate Service Recommendation

Abstract

With more proliferation of services and higher degree of personalization, higher accurate approaches to service recommendation are becoming more and more pivotal. Performance of existing service recommendation approaches is not satisfactory due to the sparseness of available data set or the incomplete information of the global service market, which make it difficult to identify a customer's potential preferences on available services. In this paper, we extract finegrained value features from customer reviews, and identify the personalized distribution of each value features to demonstrate the value preference of a specific customer. Then, a novel recommendation algorithm (VFDSR) is proposed. An algorithm VFMine based on text mining is presented to effectively extract value features from customer reviews. A VFDAnalysis algorithm based on sentiment analysis is employed to identify the value feature distributions. Based on it, VFDSR recommends top-satisfying services to customers. In addition, the value feature distributions are visualized in the form of "heatmaps". Comprehensive experiments are conducted on a Yelp dataset and the experimental results show the superiority of our approach.