Download pdf
The analysis of recommender systems: Attacks, sentiment analysis, evaluation and hybrid methods
- 1 College of Art and Science, New York University, New York, 10012, United States
- 2 College of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou, 450046, China
- 3 School of Computer, Data & Information Sciences, University of Wisconsin-Madison, Madison, 53715, United States
* Author to whom correspondence should be addressed.
Abstract
Attacks on recommender systems varies depending on the type of the recommender system being targeted. This paper will focus on the methods of attacks against Content-based and Collaborative Filtering recommender system. Sentiment Analysis is an important branch in the discipline of natural language processing, which is widely used in analyzing public opinion and recommendation systems. It is a computational study of the emotions, opinions and attitudes expressed by people about products, services, organizations, individuals, events, topics, etc. Sentiment analysis is a common application of natural language processing (NLP). Sentiment Analysis, also known as tendency analysis or opinion mining, is an important information analysis and processing technology. Its research purpose is to automatically mine the position, viewpoint, opinion, emotion, likes and dislikes in the text. Evaluating recommender systems refers to the process of assessing the quality and effectiveness of recommendation algorithms. This involves determining how well the systems are able to provide accurate and relevant recommendations to users, as well as measuring other important metrics such as coverage, diversity, and novelty. Hybrid recommender systems aim to address these limitations by combining different methods to make inferences more powerful. These systems can leverage multiple data sources, such as user behavior, item attributes, and contextual data, to offer personalized recommendations that meet the diverse needs of users. By using a combination of methods, hybrid recommender systems can overcome the limitations of individual algorithms and provide more effective recommendations.
Keywords
content-based system, collaborative system, sentiment analysis, natural language processing, evaluating recommender system, hybrid recommender system, recommender system
[1]. Katrekar, A., & Analytics, B. D.. An Introduction to Sentiment Analysis.
[2]. Chen, M., & Liu, P. (2017). Performance evaluation of recommender systems. International Journal of Performability Engineering, 13(8), 1246.
[3]. Chojnacki, S., & Kłopotek, Mieczysław. (2010). Random graphs for performance evaluation of recommender systems. Control & Cybernetics.
[4]. Celma, O. (2010). Music recommendation and discovery: The long tail, long fail, and long play in the digital music space. Springer Science & Business Media.
[5]. Herrada, B. C., & Oscar. (2008). Music recommendation and discovery in the long tail. Ceedings of International Congress on Electron Microscopy Methods Enzymol, 11(1), 7-8.
Cite this article
Xu,Y.;Mi,J.;Li,S. (2024). The analysis of recommender systems: Attacks, sentiment analysis, evaluation and hybrid methods. Applied and Computational Engineering,30,163-172.
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 2023 International Conference on Machine Learning and Automation
© 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).