Recommending Products by Fusing Online Product Scores and Objective Information Based on Prospect Theory

With the development of information technology, people are likely to choose products through online-shopping platform as it's so convenient to select products based on their reviews. Even for popular products, thousands of reviews are posted on e-commerce sites. That makes it difficult for online consumers to make purchasing decisions based on all the reviews. In order to help consumers make purchase decisions, it is a valuable research topic to rank the products through products' information. For the purpose of obtaining more objective and accurate order of products, in this paper, we propose a prospect theory-based method to rank the products by integrating online product scores and objective data. Alternative products are obtained according to the customers' basic product requirements. Then, online scores and objective values of alternative products based on multiple criteria are collected and fused after the normalisation procedure. Finally, we get the ranking of alternative products according to the weighted prospect values of alternative products based on multi-criteria. An example is given to illustrate the application of the proposed method. The characteristics and advantages of this method are illustrated by comparison and experiments. Using the richer information of products by integrating the subjective and objective information is a new idea in information representation of products. Besides, it is also more in line with the actual buying situation by means of considering buyers' risk attitudes.