Analysis of Individual Purchase Decision Based on Bayesian Personalized Ranking
Xirui Lin · 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME) · 2020
In recent years, text-based transaction data has been highly valued by merchants. It not only reflects the degree of consumer preference on goods, but also reflects the consumer's satisfied and potential needs. In this paper, we used sentiment analysis, Naive Bayes Algorithm and other methods to build a personalized recommendation model based on texts and time. The paper will conduct model assumptions and tests from the two main bodies: Users and items.