Download pdf
The Analysis of Recommendation Algorithms in Different Domains and Future Development Trends
- 1 University of California, Irvine, CA, US 92697
* Author to whom correspondence should be addressed.
Abstract
Recommendation algorithms are a crucial research direction in the fields of artificial intelligence and data science, with widespread applications in e-commerce, streaming media, education, healthcare, and social networks. The demand for accurate and personalized information has driven the development of recommendation systems. However, different application scenarios place varying emphases on recommendation algorithms. For instance, e-commerce focuses on conversion rates, social platforms emphasize user relationship expansion, and the healthcare sector prioritizes accuracy and privacy protection. Consequently, optimizing recommendation algorithms based on industry-specific characteristics has become a key research focus. This paper summarizes the core technologies of recommendation algorithms and their applications across different domains. It also analyzes current challenges such as data sparsity, the cold start problem, and privacy protection, along with corresponding countermeasures. To address these issues, researchers have proposed optimization methods that integrate deep learning and reinforcement learning, as well as improvements such as cross-domain data fusion and user intent modeling. Furthermore, future trends in recommendation systems include cross-domain recommendations, enhanced privacy protection techniques, improved interpretability, and the adoption of federated learning to ensure user data security while enhancing recommendation quality. With the continuous advancement of artificial intelligence, recommendation systems will become more intelligent, personalized, and secure, providing users with more accurate and efficient recommendation services.
Keywords
Recommendation Algorithms, Deep learning, Collaborative filtering, Cross-domain Recommendation, Personalized Recommendation
[1]. M. Techlabs. (2021) “What are the Types of Recommendation Systems?,” Medium, Aug. 18. https://marutitech.medium.com/what-are-the-types-of-recommendation-systems-3487cbafa7c9
[2]. S. H. Nallamala, U. R. Bajjuri, S. Anandarao, Dr. D. D. Prasad, and Dr. P. Mishra. (2020) “A Brief Analysis of Collaborative and Content Based Filtering Algorithms used in Recommender Systems,” IOP Conference Series: Materials Science and Engineering, Dec., vol. 981, pp. 022008.
[3]. Sarwar, Badrul, et al. (2001) Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th international conference on World Wide Web.
[4]. Gomez-Uribe, Carlos A., and Neil Hunt. (2015) The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6.4: 1-19.
[5]. Covington, Paul, Jay Adams, and Emre Sargin. (2016) Deep neural networks for Youtube recommendations. Proceedings of the 10th ACM conference on recommender systems.
[6]. L. Hardesty. (2019) “The history of Amazon’s recommendation algorithm,” Amazon Science. https://www.amazon.science/the-history-of-amazons-recommendation-algorithm
[7]. V. Elliot. (2023) “Unraveling the Cold-Start Conundrum: How New Products Break Through on Amazon - Signalytics,” Signalytics, Nov. 19. https://signalytics.ai/unraveling-the-cold-start-conundrum-on-amazon/ (accessed Feb. 27, 2025).
[8]. Covington, Paul, Jay Adams, and Emre Sargin. (2016) Deep neural networks for Youtube recommendations. Proceedings of the 10th ACM conference on recommender systems.
[9]. Pardos, Zachary A., and Neil T. (2010) Heffernan. Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. Journal of Machine Learning Research W & CP, 40.
[10]. Qiang Yang, et al. (2019) Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10.2: 1-19.
[11]. Arora, Anmol, and Ananya Arora. (2022) Generative adversarial networks and synthetic patient data: current challenges and future perspectives. Future Healthcare Journal, 9.2: 190-193.
[12]. Yongfeng Zhang, and Chen Xu. (2020) Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval, 14.1: 1-101.
Cite this article
Tao,D. (2025). The Analysis of Recommendation Algorithms in Different Domains and Future Development Trends. Applied and Computational Engineering,145,22-28.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).