ANALYSIS OF CONTENT RECOMMENDATION METHODS IN INFORMATION SERVICES

Oleksandr Necheporuk


Sumy State University (Ukraine)
https://orcid.org/0000-0002-9905-031X

Svitlana Vashchenko


Sumy State University (Ukraine)

Nataliia Fedotova

n.fedotova@cs.sumdu.edu.ua
Sumy State University (Ukraine)
https://orcid.org/0000-0001-9304-1693

Iryna Baranova


Sumy State University (Ukraine)
https://orcid.org/0000-0002-3767-8099

Yaroslava Dehtiarenko


Lublin University of Technology (Poland)

Abstract

The object of the research is the process of selecting a content recommendation method in information services. The study's relevance stems from the rapid development of informational and entertainment resources and the increasing volume of data they operate on, thus prompting the utilisation of recommendation systems to maintain user engagement. Considering the different types of content, it is necessary to address the problem of data filtration based on their characteristics and user preferences. To solve this task, we analysed content-based and collaborative filtering methods using various techniques (model-based, memory-based, and hybrid collaborative filtering techniques), knowledge-based filtering, and hybrid filtering methods. Considering each method's advantages and disadvantages, we chose a hybrid method using model-based collaborative filtering and content-based filtering for the future development of our universal recommendation system.


Keywords:

content-based recommender system, collaborative recommender system, hybrid recommender system

[1] Akbar A., Agarwal P., Obaid A. J.: Recommendation engines-neural embedding to graph-based: Techniques and evaluations. International Journal of Nonlinear Analysis and Applications 13(1), 2022, 2411–2423 [https://doi.org/10.22075/IJNAA.2022.5941].
  Google Scholar

[2] Aldossary A.: Recommender Systems Principles and methods in Web 2 Applications An analytical view of the filters used in YouTube. Multi-Knowledge Electronic Comprehensive Journal For Education And Science Publication (MECSJ), 37, 2020.
  Google Scholar

[3] Ameen A.: Knowledge based Recommendation System in Semantic Web-A Survey. International Journal of Computer Applications 182(43), 2019, 20–25.
DOI: https://doi.org/10.5120/ijca2019918538   Google Scholar

[4] Bertini M. et al.: Keeping up with the Influencers: Improving User Recommendation in Instagram using Visual Content. UMAP 2020 Adjunct – Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 2020, 29–34 [https://doi.org/10.1145/3386392.3397594].
DOI: https://doi.org/10.1145/3386392.3397594   Google Scholar

[5] Chu W. T., Tsai Y. L.: A hybrid recommendation system considering visual information for predicting favorite restaurants. World Wide Web 20(6), 2017, 1313–1331 [https://doi.org/10.1007/S11280-017-0437-1/METRICS].
DOI: https://doi.org/10.1007/s11280-017-0437-1   Google Scholar

[6] Gohari F. S., Tarokh M. J.: Classification and Comparison of the Hybrid Collaborative Filtering Systems. International Journal of Research in Industrial Engineering 6(2), 2017, 129–148 [https://doi.org/10.22105/RIEJ.2017.49158].
  Google Scholar

[7] Korotaev A., Lyadova L.: Method for the development of recommendation systems, customizable to domains, with deep GRU network. Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management – IC3K 2018, 2018, 231–236 [https://doi.org/10.5220/0006933302310236].
DOI: https://doi.org/10.5220/0006933302310236   Google Scholar

[8] Kwan C., Koh’s M. Q., Jasser M. B.: A Comparison Study Between Content-Based and Popularity-Based Filtering via Implementing a Book Recommendation System. International Journal Of Advanced Research In Engineering & Technology 11(12), 2020, 1121–1135 [https://www.researchgate.net/publication/348190964_A_Comparison_Study_Between_Content-Based_and_Popularity-Based_Filtering_via_Implementing_a_ Book_Recommendation_System] (available: 10.05.2016)
  Google Scholar

[9] Madathil M.: Music Recommendation System Spotify-Collaborative Filtering Mithun Madathil, 2017.
  Google Scholar

[10] Mohammed Al Mani I. A.: Collaborative filtering recommendation system: Comparison study (M.Sc. Thesis, Altinbaş University). 2018. [http://openaccess.altinbas.edu.tr/xmlui/handle/20.500.12939/1724] (available: 10.05.2016).
  Google Scholar

[11] Mustafa N. et al.: Collaborative filtering: Techniques and applications. Proceedings of International Conference on Communication, Control, Computing and Electronics Engineering – ICCCCEE 2017 [https://doi.org/10.1109/ICCCCEE.2017.7867668].
DOI: https://doi.org/10.1109/ICCCCEE.2017.7867668   Google Scholar

[12] Naumov M. et al.: Deep Learning Recommendation Model for Personalization and Recommendation Systems. 2019.
  Google Scholar

[13] Ni X. et al.: Feature selection for Facebook feed ranking system via a group-sparsity-regularized training algorithm. Proceedings of International Conference on Information and Knowledge Management, 2019, 2085–2088 [https://doi.org/10.1145/3357384.3358114].
DOI: https://doi.org/10.1145/3357384.3358114   Google Scholar

[14] Nilashi M., Ibrahim O., Bagherifard K.: A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications 92, 2018, 507–520 [https://doi.org/10.1016/J.ESWA.2017.09.058].
DOI: https://doi.org/10.1016/j.eswa.2017.09.058   Google Scholar

[15] Pajkovic N.: Algorithms and taste-making: Exposing the Netflix Recommender System’s operational logics. Convergence 28(1), 2022, 214–235 [https://doi.org/10.1177/13548565211014464].
DOI: https://doi.org/10.1177/13548565211014464   Google Scholar

[16] Parfenenko Yu., Kovtun A., Verbytska A.: Recommended information system for video search. Scientific journal "Transactions of Kremenchuk Mykhailo Ostrohradskyi National University" 5(118), 2019, 97–102 [https://doi.org/10.30929/1995-0519.2019.5.97-102].
DOI: https://doi.org/10.30929/1995-0519.2019.5.97-102   Google Scholar

[17] Patil M., Brid S., Dhebar S.: Comparison Of Different Music Recommendation System Algorithms. International Journal of Engineering Applied Sciences and Technology 5 (6), 2020 [https://doi.org/10.33564/IJEAST.2020.v05i06.036].
DOI: https://doi.org/10.33564/IJEAST.2020.v05i06.036   Google Scholar

[18] Raghuwanshi S. K., Pateriya R. K.: Recommendation systems: Techniques, challenges, application, and evaluation. Advances in Intelligent Systems and Computing 817, 2019, 151–164 [https://doi.org/10.1007/978-981-13-1595-4_12/COVER].
DOI: https://doi.org/10.1007/978-981-13-1595-4_12   Google Scholar

[19] Ranjan A. A. et al.: An Approach for Netflix Recommendation System using Singular Value Decomposition. An International Research Journal 10(4), 2019, 774–779 [https://doi.org/10.29055/jcms/1063].
DOI: https://doi.org/10.29055/jcms/1063   Google Scholar

[20] Shahbazi Z., Byun Y.-C.: Improving the Product Recommendation System based-on Customer Interest for Online Shopping Using Deep Reinforcement Learning. Soft Computing and Machine Intelligence Journal 1(1), 2021 [https://doi.org/10.22995/scmi.2021.1.1.02].
  Google Scholar

[21] Shuaibi A.: Predicting the Popularity of Reddit Posts. 2019.
  Google Scholar

[22] Wilhelm M. et al.: Practical Diversified Recommendations on YouTube with Determinantal Point Processes. ACM Reference Format: Gillenwater, 2018. [https://doi.org/10.1145/3269206.3272018].
DOI: https://doi.org/10.1145/3269206.3272018   Google Scholar

[23] Zarzour H. et al.: A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. 9th International Conference on Information and Communication Systems – ICICS 2018, January 2018, 102–106 [https://doi.org/10.1109/IACS.2018.8355449].
DOI: https://doi.org/10.1109/IACS.2018.8355449   Google Scholar

[24] Mashkovskyi S.: Latent-semantic analysis, social networks and non-structured data: interaction method. Visnyk Universytetu "Ukraina" 2(23), 2019 [https://doi.org/10.36994/2707-4110-2019-2-23-29].
DOI: https://doi.org/10.36994/2707-4110-2019-2-23-29   Google Scholar

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Published
2024-09-30

Cited by

Necheporuk, O., Vashchenko, S., Fedotova, N., Baranova, I., & Dehtiarenko, Y. (2024). ANALYSIS OF CONTENT RECOMMENDATION METHODS IN INFORMATION SERVICES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(3), 105–108. https://doi.org/10.35784/iapgos.6203

Authors

Oleksandr Necheporuk 

Sumy State University Ukraine
https://orcid.org/0000-0002-9905-031X

Authors

Svitlana Vashchenko 

Sumy State University Ukraine

Authors

Nataliia Fedotova 
n.fedotova@cs.sumdu.edu.ua
Sumy State University Ukraine
https://orcid.org/0000-0001-9304-1693

Authors

Iryna Baranova 

Sumy State University Ukraine
https://orcid.org/0000-0002-3767-8099

Authors

Yaroslava Dehtiarenko 

Lublin University of Technology Poland

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