Problems and Goals of Recommender Systems
Keywords:
recommendation system, collaborative filtering, content-based recommendation systems, hybrid recommender systemsAbstract
Background. On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems.
Method: In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective.
Result: Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.
Conclusion. Recommender systems are an advanced form of software applications, more specifically decision-support systems, that efficiently assist the users in finding items of their interest.
Downloads
References
F. Abel, Q. Gao, G.-J. Houben, and K. Tao. Analyzing user modeling on twitter for personalized news recommendations. In Proc. 19th International Conference on User Modeling, Adaption, and Personalization, UMAP’11, pages 1–12, Berlin, Heidelberg, 2011. Springer-Verlag. ISBN 978-3-642-22361-7. URL http://dl.acm.org/citation.cfm?id=2021855.2021857.
Lu, J.; Wu, D.; Mao, M.; aj.: Recommender System Application Development s: A Survey 2019
Sarwar, B.; Karypis, G.; Konstan, J.; aj.: Item - based collaborative filtering recommendation algorithms. 2018
Resnick, P.; Iacovou, N.; Suchak, M.; aj.: GroupLens: an open architecture for collaborative filtering of netnews. 2018
Shambour, Q.; Lu, J.: hybrid trust - enhanced collaborative filtering recommendation approach for personalized government - to - business e - services, International Journal of Intelligent Systems. 2017
Madadipouya, K.; Chelliah, S.: A Literature Review on Recommender Systems Algorithms, Techniques and Evaluations. BRAIN: Broad Research in Artificial Intelligence and Neuroscience, ročník 8, č. 2, July 2017.
P., L.; de Gemmis M.; G., S.: Content-based Recommender Systems: State of the Art and Trends. In: Ricci F., Rokach L., Shapira B., Kantor P. (eds) Recommender Systems Handbook. Springer, Boston, MA, 2011, ISBN 978-0-387-85819-7.
Lisa Wenige, Johannes Ruhland, Retrieval by recommendation: using LOD technologies to improve digital library search, © Springer Verlag GmbH Germany 2017
Mingdan Si, Qingshan Li, Shilling attacks against collaborative recommender systems: a review, Artificial Intelligence Review (2020) 53:291–319
Hui Li, Yan Gu, Saroj Koul, A Review of Digital Library Book Recommendation Models, 2015
Joseph A. Konstan, John Riedl, Recommender systems: from algorithms to user experience, © Springer Science+Business Media B.V. 2012
Emmanouil Vozalis, Konstantinos G. Margaritis, Analysis of Recommended Systems Algorithms, 2014
Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37, Aug. 2009.
Q. Lu, T. Chen, W. Zhang, D. Yang, and Y. Yu. Serendipitous personalized ranking for top-n recommendation. In Proc. The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01, WI-IAT ’12, pages 258–265, Washington, DC, USA, 2012. IEEE Computer Society.Rustamov A., Bekkamov F., Recommender systems: an overview, Scientific reports of Bukhara state university, 2021/3(85).
Буранова, М. А. (2020). ИННОВАЦИИ-ЗАЛОГ РАЗВИТИЯ И КОНКУРЕНТОСПОСОБНОСТИ ПРОМЫШЛЕННОСТИ СТРАНЫ. Интернаука, (13-2), 9-11.
Хашимова, Н. А., & Буранова, М. А. (2020). РАЗВИТИЕ ИНТЕЛЛЕКТУАЛЬНОГО ПОТЕНЦИАЛА ЗАЛОГ УСПЕШНОЙ ПОЛИТИКИ РУЗ. Интернаука, (13-2), 28-29.
Буранова, М. А., & Сайфутдинова, Н. Ф. (2020). РАЗВИТИЕ ПРОМЫШЛЕННОСТИ-ОСНОВА КОНКУРЕНТОСПОСОБНОСТИ СТРАНЫ. Интернаука, (13-2), 12-14.
Буранова, М. А. (2019). Перспективы развития электроэнергетической отрасли в условиях модернизации экономики Узбекистана. Российский внешнеэкономический вестник, (7), 60-63.
Буранова, М. А. (2019). Модернизация–ключ к развитию энергетики. Экономика и финансы (Узбекистан), (5).
Published
How to Cite
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
The work simultaneously licensed under a Creative Commons Attribution 4.0 International License
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
