Personalized recommendation using similarity powered pairwise amplifier network

Zhang, Tongda1; Qian, Jun2; Sun, Xiao2; Guo, Daqiang1

10.1145/3377713.3377728

2019

2nd International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2019

138-146

Sanya, China

Association for Computing Machinery

Online advertising, one typical application of recommendation system, calls for effective and accurate recommendations of keywords. Extreme sparse and large scale data makes online advertising a challenging problem. To achieve better performance and accuracy of the recommendation, a better model with a short turnaround time is needed. In this paper, we address the problem of personalized online advertising for extreme sparse and large scale data. We develop a novel machine learning model (Similarity Powered Pairwise Amplifier Network, SPPAN for short). The complexity of this model (a.k.a. the number of parameters) grows with the amount of observed data, which makes it suitable to extremely sparse data. The training algorithm based on gradient descent makes it easy to parallelize. The similarity model combines the user neighborhood and item neighborhood ideas in collaborative filtering smartly, obtaining a cost-effective way to handle large scale data. The proposed framework is evaluated on a large set of real-world data set from a large internet company (expressed by "Company A"). The experiment results demonstrate that the proposed SPPAN model can greatly improve the prediction and recommendation accuracy on that extreme sparse data set compared with existing approaches.
© 2019 ACM.

第一

EI

20200908248326

Artificial intelligence ; Collaborative filtering ; Cost effectiveness ; Gradient methods ; Learning algorithms ; Neural networks

Artificial Intelligence:723.4 ; Information Sources and Analysis:903.1 ; Industrial Economics:911.2 ; Marketing:911.4 ; Numerical Methods:921.6

EV Compendex

1.Department of Mechanical and Energy Engineering, South University of Science and Technology, Shenzhen, China
2.National Engineering Laboratory for E-Commerce Technology, Tsinghua University, Beijing National Research Center for Information Science and Technology, Beijing, China

Zhang, Tongda,Qian, Jun,Sun, Xiao,et al. Personalized recommendation using similarity powered pairwise amplifier network[C]:Association for Computing Machinery,2019:138-146.