Research On Customer Experience Evaluation Of E-commerce Recommendation System

The rapid development of the Internet induced a huge growth of E-commerce sector in China.The key players of this industry provide to Chinese customers several convenient online services and electronic platforms,which help people to make their purchase decision.As the platforms build more and more advanced recommender algorithms,customers start to pay a lot of attention to the experience of online shopping.That is why the increasing number of the analysts today start to research the influence of these recommender systems on consumer behavior and customer experience.First of all,this paper provides an overview of the theory of E-commerce recommender systems and customer experience.The combination of professional research methods allows author to make a complete system of indicators,which measure the consumer behavior regarding the recommender algorithms of online platforms.The author uses an investigation questionnaire method to collect the data,then applies exploratory factor analysis and confirmatory factor analysis to test and verify the influence of this data to customer experience,next establishes a system for evaluating the influence of recommender algorithms on customer experience.While doing weight calculations,author coalesces two approaches: subjective weighting level analysis and objective weighting entropy method,resulted in calculations of a combined weight.Finally,using fuzzy comprehensive evaluation method,author derives a specific estimate of customer experience.On this research basis,the author provides some valuable suggestions about the following development of the recommender systems,in order to improve consumer experience.