Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 5 , 30 May 2023


Open Access | Article

RS on video games based on item-based collaborative filtering algorithm

Hongyun Zhu * 1
1 Chengdu University of Technology, Department of Software Engineering

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 5, 11-17
Published 30 May 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Hongyun Zhu. RS on video games based on item-based collaborative filtering algorithm. ACE (2023) Vol. 5: 11-17. DOI: 10.54254/2755-2721/5/20230515.

Abstract

With the rapid development of the Internet and e-commerce, recommender systems have received great attention and wide application in this environment. Because it is difficult for people to choose the one that they like in the face of the dazzling array of items on the Internet, and these e-commerce sites also need to consider how to improve efficiency, the recommendation system is an excellent solution. This paper mainly reviewed the development of recommender systems, focusing on the research and experiments of a recommender system based on an item-based collaborative filtering algorithm. According to the experimental results and some previous studies, summarizing the advantages and disadvantages of this method, proposing some solutions, and pointing out some problems that will be faced by future researches on recommendation systems.

Keywords

Recommendation system, Collaborative filtering, Item-based Collaborative filtering

References

1. Ren X L and Liu L 2008 Research progress and prospect of recommender system 2008 China Journal of Information Systems 2 p 82-90.

2. Wang Y F, Chuang Y L, Hsu M H and Keh H C 2004 A personalized recommender system for the cosmetic business Expert Systems with Applications 26 p 427-434

3. Liu M C 2016 Research on Content-Based Recommendation Technology Modern Marketing 6 p 243

4. Huang S L 2011 Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods - ScienceDirect Electronic Commerce Research and Applications 10 p 398-407

5. Ma H G, Zhang G W and Li P 2009 Survey of Collaborative Filtering Algorithms Journal of Chinese Computer Systems 30(7) p 1282-1288.

6. Liu Z B 2016 Application of Machine Learning Methods in Personalized Recommender Systems Information Research 4

7. Huang L W, Jiang B T, Lv S Y, Liu Y B and Li D Y 2018 Survey on Deep Learning Based Recommender Systems Chinese Journal of Computers 41(7)

8. Sun X H 2005 Research on Sparsity and Cold Start Problem of Collaborative Filtering System (Doctoral dissertation: Zhejiang University)

9. Kim B M, Li Q, Kim J W and Kim J 2004 A new collaborative recommender system addressing three problems In Pacific Rim International Conference on Artificial Intelligence. (Berlin, Heidelberg: Springer) p 495-504

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-57-7
ISBN (Online)
978-1-915371-58-4
Published Date
30 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/5/20230515
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated