Research And Analysis of Online Shopper Intention

Abstract

In recent years, with the rapid development of online shopping sites, more and more consumers are accustomed to online shopping. In fact, shopping sites invest a lot of money and manpower to improve shopping applications in order to discover different items of interest to consumers. By more accurately predicting consumers' preferences for products, shopping websites can more accurately push the products they are interested in to consumers. This not only improves the experience of consumers in the online shopping process and the efficiency of shopping, but also improves the competitiveness and performance of shopping websites. This paper discusses the influence of consumer behavior on online shopping desire from four aspects: cultural factors, social factors, personal factors and psychological factors. The influence of consumer acceptance on online shopping intention is discussed. Further, this paper introduces several common methods for predicting user purchase behavior, including collaborative filtering algorithm and hybrid recommendation method. For each method, the paper also provides advantages and disadvantages in their use, which will provide reference for further research related to this topic.