Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220300084-7.doi: 10.11896/jsjkx.220300084

• Big Data & Data Science • Previous Articles     Next Articles

Recommendation Model Based on Decision Tree and Improved Deep & Cross Network

KE Haiping, MAO Yijun, GU Wanrong   

  1. College of Mathematics and Informatics,South China Agricultural University,Guangzhou 510642,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:KE Haiping,born in 1997,postgra-duate,is a student member of China Computer Federation.Her main research interests include recommendation system and deep learning. GU Wanrong,born in 1982,Ph.D,master.His main research interests include search engine,Internet big data analysis and mining, recommendation system and biological information mining.
  • Supported by:
    National Statistical Science Research Project of China(2020LY018),Philosophy and Social Science Planning Project of Guangdong Province(GD19CGL34),Open Fund Project of Guangdong Key Laboratory of Computing Science,Sun Yat-sen University(2021010),General Program of Guangdong Natural Science Foundation(2022A1515011489) and Guangzhou Key Laboratory of Intelligent Agriculture Project(201902010081).

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Abstract: Feature mining is a key step to learn the interaction between users and items in the recommendation algorithm model,which is of great significance to improve the accuracy of the recommendation model.Among the existing feature mining models,although the linear logistic regression model is simple and can achieve good fitting effect,its generalization ability is weak,and the model has a large demand for feature parameters.Deep & Cross network can effectively realize the cross extraction of features,but its representation ability of data features is still insufficient.Therefore,by introducing the idea of multiple residual structure and cross coding,an improved recommendation model of Deep & Cross network based on decision tree is proposed.Firstly,it designs a tree structure based on GBDT algorithm to construct enhanced features,which strengthens the deep mining of the model on potential features.Secondly,the input parameter dimension of the embedded layer of the model is amplified and optimized.Finally,the improved Deep & Cross network recommendation model is used for recommendation prediction.This design can not only break the limitations of existing models in generalization ability,but also keep the feature parameters simple and strengthen their representation ability,so as to effectively mine the hidden associations of users and improve the accuracy of recommendation.Experimental results based on the public test data set show that the prediction effect of the proposed model is better than the exis-ting feature interaction methods.

Key words: Feature mining, Feature crossover, Enhanced feature, Decision tree, Recommendation model

CLC Number: 

  • TP301.6
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