Abstract
Recommendation system is unable to achive the optimal algorithm, recommendation system precision problem into bottleneck. Based on the perspective of product marketing, paper takes the inherent attribute as the classification standard and focuses on the core problem of “matching of product classification and recommendation algorithm of users’ purchase demand”. Three hypotheses are proposed: (1) inherent attributes of the product directly affect user demand; (2) classified product is suitable for different recommendation algorithms; (3) recommendation algorithm integration can achieve personalized customization. Based on empirical research on the relationship between characteristics of recommendation information (independent variable) and purchase intention (dependent variable), it is concluded that predictability and difference of recommendation information are not fully perceived and stimulation is insufficient. Therefore, SIS dynamic network model based on the distribution model of SIS virus is constructed. It discusses the spreading path of recommendation information and “infection” situation of consumers to enhance accurate matching of recommendation system.
Highlights
"Information explosion" is an unprecedented challenge to information providers and users
This paper product categories in focus of marketing concept, build "epidemic dynamics perspective SIS dynamic network model based on product semantics", realize the customer personalization and high performance matching
This paper involves the independent variable is the inherent nature of goods, the dependent variable (X) for consumer purchase intention, it is divided into five dimensions: "consumers purchase intention" first purchase (X1), interactive (X2) and merit (X3), difference between purchase (X4), habitual purchase (X5), Independent variables inherent attribute of recommendation system under "goods" is divided into five dimensions: electronic recommendation information recognition (T1), predictive (T2), difference (T3), periodically (T4), personalized (T5).With the help of a certain scale to measure the variables and reflected in the questionnaire
Summary
"Information explosion" is an unprecedented challenge to information providers and users. Information explosion is how to quickly find the information users need from massive information flow. It is how to provide the information accurately to users who really need [1]. The algorithm of electronic recommendation system is constantly optimized, the personalized definition of recommendation is difficult to achieve effective matching. Excessive product information recommendation leads to bad user experience and resource waste. This paper product categories in focus of marketing concept, build "epidemic dynamics perspective SIS dynamic network model based on product semantics", realize the customer personalization and high performance matching
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