Scenario based e-commerce recommendation algorithm based on customer interest in Internet of things environment

Abstract

With the development of mobile commerce, situational awareness and Internet of things, the boundaries of e-commerce have been greatly expanded, and it has entered a big data era of business information. However, customers are faced with the problem that information is rich but useful information is hard to get. E-commerce is facing the challenge of how to provide personalized information recommendation services for customers and motivate customers to purchase continuously. Therefore, this paper studies the problem of e-commerce recommendation under the condition of large data, and proposes a scenario-based e-commerce recommendation algorithm based on customer interest. Firstly, according to the characteristics of customer interest such as situational sensitivity and diversity in personalized recommendation, a multi-dimensional customer interest feature vector is established by using distributed cognitive theory to differentiate the sensitive scenarios of customer interest. Then, the collaborative filtering recommendation algorithm is used to realize customer similarity judgment and product recommendation in sensitive scenarios. Experimental results show that the method has good customer interest extraction ability. Compared with other recommendation methods, it has higher recommendation accuracy and can adapt to the high-quality commodity recommendation service in the process of customer continuous purchase under complex circumstances.