Abstract
With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer’s feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems.
Highlights
The past few years have witnessed an astounding growth of electronic commerce, and online shopping has become a staple for many people
An e-commerce personalized recommendation system emerges, as the times require. It is a business intelligence platform based on massive data mining, which can help e-commerce websites provide personalized decision support and information service for customers
The paper’s organization is as follows: In Section 2, we review the literature of the recommendation system, hybrid algorithm and iterative design
Summary
The past few years have witnessed an astounding growth of electronic commerce, and online shopping has become a staple for many people. If consumers want to find the product that they are interested in, they need to browse a lot of irrelevant information [3] This process may lead to the continuous loss of consumers, and hinder the development of e-commerce. Consumers are in urgent need of a purchasing assistant to recommend products according to their interests. In this case, an e-commerce personalized recommendation system emerges, as the times require. If Lily knows nothing about computers, she cannot accurately describe her preferences by means of specific configuration indicators In this case, the recommendation system needs to constantly identify her preferences by interacting with the customer. Generate the customer portrait based on consumer information, and use multiple recommendation methods to get a list of original recommendation results.
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