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

Abstract

A recommender system with good performance for an ecommerce 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.

Feasibility study of deep learning based on data-based e-commerce operations

“…1 Introduction E-commerce is an inevitable product of the rapid development of Internet technology, along with the rapid development of e-commerce, and with it, e-commerce, weblogs, social networks, and Internet search engines generate a large amount of data information on various platforms every day [1][2]. The e-commerce industry is becoming increasingly accepted and concerned, and e-commerce makes it easier to obtain data compared to traditional industries with a stronger reliance on data, which puts higher requirements for data processing in e-commerce [3][4][5]. Taobao is a popular e-commerce platform in China.…”