Research on the Model of Agricultural Product Purchase Intention Based on Live Broadcast AI and Recommendation Algorithm

This paper proposes a model of agricultural product purchase intention based on live broadcast AI and recommendation algorithm (LiveRec). By combining AI behavior analysis in live broadcast with traditional recommendation algorithm, this model can adjust the recommendation strategy in real time according to the user's viewing behavior, interaction, comments and other information, thereby improving the user's purchase intention. This paper makes use of the live broadcast data from the e-commerce platform of agricultural products, including user behavior data and sales records, to evaluate the effectiveness of the model in the forecast of buying intention and the exchange rate. Experiments show that the precision of the prediction is increased by 18 %, and the conversion ratio is 15 % higher than that of conventional logistic regression and SVM. In particular, the model has good adaptability and real time performance in the user's personalized recommendation. The study indicates that the model based on live AI and recommended algorithm can increase the sales efficiency of agriculture products, and it will have a bright future. Future research will further optimize the model's real time performance and processing efficiency, and extend its application to other electronic commerce.