A recommender system based on contextual information of click and purchase data to items for e-commerce

A recommender system with good performance for an e-commerce web site is important for both customers and merchants. In most of the existing recommender systems, only the purchase information is utilized data and the navigational and behavioral data are seldom concerned. In this paper, we design a novel recommender system for comprehensive online shopping sites. In the proposed recommender system, the contextual information data, such as access, click, read, and purchase information of a customer, are utilized to calculate the preference degree to each item; then items with larger preference degrees are recommended to the customer. In addition, nonexpendable items are distinguished from expendable ones and handled by a different way. Lastly, we structure an example to show the performance of the proposed recommender system. The results show that the proposed method is well-performed.