Personalized Game Recommendation Method Based on Implicit Feedback

Authors

Sha Jing, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China;
Gongli Zeng, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China;
Yang Yang, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China;
Wei Yao, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China;

Abstract

Abstract: Traditional recommendation systems often use explicit feedback for personalized recommendations. But the explicit feedback data is not easy to obtain, and the quality is poor, and the recommendation results unable to meet the requitrment of the user. Implicit feedback data is easier to obtain and can provide users with the better content. A personalized game recommendation method based on implicit feedback data is proposed. The method builds an implicit feedback recommendation model for game user data based on implicit feedback data such as the game duration and game numbers. A personalized recommendation of the game is implemented through an implicit semantic recommendation algorithm. Through comparative experiments on a large number of real data sets, it is shown that the accuracy and recall of the proposed method are better than other methods.

Recommended Citation

Jing, Sha; Zeng, Gongli; Yang, Yang; and Yao, Wei (2021) "Personalized Game Recommendation Method Based on Implicit Feedback," Journal of System Simulation: Vol. 33: Iss. 4, Article 7.
DOI: 10.16182/j.issn1004731x.joss.19-0636
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss4/7

First Page

809

Revised Date

2020-04-13

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.19-0636

Last Page

817

CLC

TH138;R318.6;TP391

Recommended Citation

Sha Jing, Zeng Gongli, Yang Yang, Wei Yao. Personalized Game Recommendation Method Based on Implicit Feedback[J]. Journal of System Simulation, 2021, 33(4): 809-817.

DOI

10.16182/j.issn1004731x.joss.19-0636