LFG: A Generative Network for Real-Time Recommendation
Authors: 
Junyi Liu, Chuanmin MiAuthors Info & Claims
Pages 55 - 60
Abstract
Recommender systems are essential information technologies today, and recommendation algorithms combined with deep learning have become a research hotspot in this field. The recommendation model known as LFM (Latent Factor Model), which captures latent features through matrix factorization and gradient descent to fit user preferences, has given rise to various recommendation algorithms that bring new improvements in recommendation accuracy. However, collaborative filtering recommendation models based on LFM lack flexibility and has shortcomings for real-time recommendations, as they need to redo the matrix factorization and retrain using gradient descent when new users arrive. In response to this, this paper innovatively proposes a Latent Factor Generator (LFG) network, and set the movie recommendation as research theme. The LFG dynamically generates user latent factors through deep neural networks without the need for re-factorization or retrain. Experimental results indicate that the LFG recommendation model outperforms traditional matrix factorization algorithms in recommendation accuracy, providing an effective solution to the challenges of real-time recommendations with LFM.
References
[1]
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Comput. Surv., vol. 52, no. 1, p. Article 5. https://doi.org/10.1145/3285029.
[2]
G. Adomavicius and A. Tuzhilin. 2005. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734-749. https://doi.org/10.1109/TKDE.2005.99.
[3]
Aanchal Mongia, Neha Jhamb, Emilie Chouzenoux, and Angshul Majumdar. 2020. Deep latent factor model for collaborative filtering. Signal Processing, vol. 169, p. 107366, 2020/04/01/. https://doi.org/10.1016/j.sigpro.2019.107366.
[4]
Y. Koren, R. Bell, and C. Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer, vol. 42, no. 8, pp. 30-37. https://doi.org/10.1109/MC.2009.263.
[5]
M. H. Mohamed, M. H. Khafagy, and M. H. Ibrahim. 2019. Recommender Systems Challenges and Solutions Survey. in 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), 2-4 Feb. 2019, pp. 149-155. https://doi.org/10.1109/ITCE.2019.8646645.
[6]
Xiaochen Yue and Qicheng Liu. 2021. Parallel Algorithm of Improved FunkSVD Based on Spark. KSII Transactions on Internet & Information Systems, vol. 15, no. 5, 2021.
[7]
J. Fan, R. Ji, and Y. Tian. 2021. Collaborative Filtering Recommendation Algorithm Based on Improved BiasSVD. in 2021 6th International Conference on Control, Robotics and Cybernetics (CRC), 9-11 Oct. 2021, pp. 297-300. https://doi.org/10.1109/CRC52766.2021.9620165.
[8]
S. Jiang, J. Li, and W. Zhou. 2020. An Application of SVD++ Method in Collaborative Filtering. in 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 18-20 Dec. 2020, pp. 192-197. https://doi.org/10.1109/ICCWAMTIP51612.2020.9317347.
[9]
Guibing Guo. 2012. Resolving Data Sparsity and Cold Start in Recommender Systems. Berlin, Heidelberg, 2012: Springer Berlin Heidelberg, in User Modeling, Adaptation, and Personalization, pp. 361-364.
[10]
F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst., vol. 5, no. 4, p. Article 19. https://doi.org/10.1145/2827872.
[11]
Xun Zhou, Jing He, Guangyan Huang, and Yanchun Zhang. 2015. SVD-based incremental approaches for recommender systems. Journal of Computer and System Sciences, vol. 81, no. 4, pp. 717-733, 2015/06/01/. https://doi.org/10.1016/j.jcss.2014.11.016.
[12]
Poonam B Thorat, Rajeshwari M Goudar, and Sunita Barve. 2015. Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications, vol. 110, no. 4, pp. 31-36.
[13]
Manolis G Vozalis and Konstantinos G Margaritis. 2006. Applying SVD on Generalized Item-based Filtering. Int. J. Comput. Sci. Appl., vol. 3, no. 3, pp. 27-51.
[14]
Lars Ruthotto and Eldad Haber. 2021. An introduction to deep generative modeling. GAMM-Mitteilungen, vol. 44, no. 2, p. e202100008. https://doi.org/10.1002/gamm.202100008.
[15]
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. presented at the Proceedings of the 2018 World Wide Web Conference, Lyon, France. https://doi.org/10.1145/3178876.3186150.
[16]
Salam Salloum and Dananjaya Rajamanthri. 2021. Implementation and Evaluation of Movie Recommender Systems Using Collaborative Filtering. Journal of Advances in Information Technology, Vol. 12, No. 3, pp. 189-196. https://doi.org/10.12720/jait.12.3.189-196
Index Terms
- LFG: A Generative Network for Real-Time Recommendation
Recommendations
Collaborative filtering and kNN based recommendation to overcome cold start and sparsity issues: A comparative analysis
AbstractCollaborative Filtering (CF) has intrigued several researchers whose goal is to enhance Recommender System’s performance by mitigating their drawbacks. CF’s common idea is to recognize user’s preferences by considering their ratings given to the ...
Generative Adversarial Framework for Cold-Start Item Recommendation
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalThe cold-start problem has been a long-standing issue in recommendation. Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions. Therefore, such embedding-based models ...
Pairwise preference regression for cold-start recommendation
RecSys '09: Proceedings of the third ACM conference on Recommender systemsRecommender systems are widely used in online e-commerce applications to improve user engagement and then to increase revenue. A key challenge for recommender systems is providing high quality recommendation to users in ``cold-start" situations. We ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2023
80 pages
ISBN:9798400708695
DOI:10.1145/3640872
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 February 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
BDE 2023
BDE 2023: 2023 5th International Conference on Big Data Engineering
November 17 - 20, 2023
Zhuhai, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 14Total Downloads
- Downloads (Last 12 months)14
- Downloads (Last 6 weeks)4
Reflects downloads up to 08 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format