Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 494-499.doi: 10.11896/JsJkx.190900016

• Database & Big Data & Data Science • Previous Articles     Next Articles

Adaptive High-order Rating Distance Recommendation Model Based on Newton Optimization

ZOU Hai-tao, ZHENG Shang, WANG Qi, YU Hua-long and GAO Shang   

  1. School of Computer Science,Jiangsu University of Science and Technology,ZhenJiang,Jiangsu 212003,China
  • Published:2020-07-07
  • About author:ZOU Hai-tao, born in 1984, Ph.D, lecturer.His main research interests include data mining and information retrieval.
    GAO Shang, born in 1972, Ph.D, professor, is a member of China Computer Federation.His main research interests include intelligent computing and pattern recognition.
  • Supported by:
    This work was suppored by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China (18JBK520011) and Primary Research & Developement Plan (Social development) of ZhenJiang City (SH2019021).

Abstract: Some existing recommendation algorithms introduce latent factor model to overcome the problems caused by data scarcity,so as to provide more effective recommendations for users.In general,those methods construct an optimization function to achieve the minimum rating error or maximum preference,etc,by integrating several polynomials with the corresponding parameters to balance each part,and use stochastic gradient descent to solve this function.Nevertheless,the above mentioned models only consider the difference between the estimated and real ratings of the same user-item pair (i.e.,the first-order rating distance),and ignore the difference between the estimated and real ratings of the same user across different items (i.e.,the second-order rating distance).Hence,high-order rating distance model,HoORaYs,with good accuracy in terms of item ranking and predictive ratings which takes these two kinds of distances into account is proposed.Unfortunately,this model still has some flaws in adap-tability and efficiency due to its manually setting parameters,its non-convergence.Aiming at improving the recommendation adap-tability and efficiency,an adaptive high-order rating distance model which integrates a data scale sensitive function is proposed.It utilizes Newton method to solve the convex optimization problem about rating distance.This method not only eliminates manually setting parameters,but also accelerates the optimization function convergence speed.The proposed model has a solid theoretical support.Experiments on three real datasets show that,it has good prediction accuracy and operation efficiency.

Key words: Convex optimization, High-order rating distance, Latent factor model, Newton method, Recommender systems

CLC Number: 

  • TP181
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