Product recommendation system based on LightGBMRanker algorithm

Abstract

With the continuous development of e-commerce websites, online shopping has become an indispensable part of life. However, the huge amount of product information makes customers lost and weakens the ability to obtain the expected information. In recent years, sorting algorithm has been applied in recommendation system to recommend information and products to customers according to product information and customer behavior patterns. Based on a large number of historical sales commodity data and customer data, combined with LightGBMRanker algorithm and feature engineering processing, this paper establishes a commodity recommendation and sorting model. And judge the accuracy of the sorting model according to the subsequent purchase data of customers. The results show that our model can effectively provide customers with desired product recommendations, and the prediction accuracy is higher than other algorithms. Specifically, the maximum map@12 value of our model is 0.0284, which is 26.22% and 22.41% higher than SVM algorithm and LSTM algorithm respectively. In addition, the ranking of important features that affect product sorting and recommendation is given in this paper, and some constructive guidance is obtained.