Product recommendation system based on LightGBMRanker algorithm

Yilong Liu

doi:10.1117/12.2660127

22 November 2022 Product recommendation system based on LightGBMRanker algorithm

Yilong Liu

Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 124751X (2022) https://doi.org/10.1117/12.2660127
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China

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.

Citation Download Citation

Yilong Liu "Product recommendation system based on LightGBMRanker algorithm", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 124751X (22 November 2022); https://doi.org/10.1117/12.2660127

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available