AN ANALYTICAL REVIEW OF CONTENT-BASED AND COLLABORATIVE FILTERING IN RECOMMENDER SYSTEMS
DOI:
https://doi.org/10.17721/AIT.2025.1.07Keywords:
collaborative filtering, content-based filtering, hybrid filtering, recommender systems, Rocchio algorithm, vector space model.Abstract
Background. With the rapid growth of digital content, recommender systems are becoming a key tool for providing personalized offers. They contribute to the discovery of new movies, music, and products, maintaining user interest in using platforms. The relevance of researching recommender system algorithms is due to the need to improve their work to satisfy individual user preferences. This paper presents a review and analytical study of recommender system algorithms. The purpose of this paper is to systematize, classify, and critically analyze two main approaches in recommender systems: content-based filtering and collaborative filtering.
Methods. A review of existing recommender system methods, a comparative and analytical assessment.
Results. The work analyzes recommender system algorithms. A formal definition of the recommendation problem is given, where user preferences are modeled as a functional dependence on object properties. Within the framework of content-based filtering, the use of classification algorithms, such as a Naive Bayes classifier and decision trees, as well as the Rocchio algorithm, which uses relevant feedback to update the user profile, is considered. The strengths and weaknesses of different similarity measures between vectors are analyzed. In collaborative filtering, the memory-based approach (user-based and item-based methods) and model-based techniques with an emphasis on the k-NN algorithm are investigated. To overcome the shortcomings of individual methods, a hybrid approach is proposed that combines their advantages. Methods for integrating systems into a hybrid model are presented, which allows improving the accuracy of recommendations.
Conclusions. The results of the work highlight the features of the specified filtering methods, demonstrate the impact of the implementation of algorithms and input data on the accuracy of recommendations and response time. The analysis of shortcomings emphasizes the importance of the combined use of filtering algorithms to improve the efficiency of recommendation systems, which makes the hybrid approach a promising direction for further research and implementation.
Downloads
References
Anisimov, A. V., Marchenko, O. O., & Kysenko, V. K. (2011). A method for the computation of the semantic similarity and relatedness between natural language words. Cybernetics and Systems Analysis, 47, 515–522. https://doi.org/10.1007/s10559-011-9334-2
Anisimov, A. V., Marchenko, O. O., & Vozniuk, T. G. (2014). Determining Semantic Valences of Ontology Concepts by Means of Nonnegative Factorization of Tensors of Large Text Corpora. Cybernetics and Systems Analysis, 50, 327–337. https://doi.org/10.1007/s10559-014-9621-9
Baxla, M. A. (2014). Comparative study of similarity measures for item based top n recommendation [Unpublished thesis, National Institute of Technology Rourkela]. CORE. https://files.core.ac.uk/download/53190130.pdf
Belhaouari, S. B., Fareed, A., Hassan, S., & Halim, Z. (2023). A collaborative filtering recommendation framework utilizing social networks. Machine Learning with Applications, 14, 1–20. https://doi.org/10.1016/j.mlwa.2023.100495
Burke, R. (2007). Hybrid Web Recommender Systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), Lecture Notes in Computer Science, 4321. The Adaptive Web (pp. 377–408). Springer. https://doi.org/10.1007/978-3-540-72079-9_12
Çano, E. (2017). Hybrid Recommender Systems: A Systematic Literature Review. Intelligent Data Analysis, 21, 1487–1524. https://doi.org/10.3233/IDA-163209
Deutschman, Z. (2023, August 7). Recommender Systems: Machine Learning Metrics and Business Metrics. Neptune AI. https://neptune.ai/blog/recommender-systems-metrics
Fkih, F. (2022). Similarity measures for Collaborative Filtering-based Recommender Systems: Review and experimental comparison. Journal of King Saud University - Computer and Information Sciences, 34(9), 7645–7669. https://doi.org/10.1016/j.jksuci.2021.09.014
Gaurav, P. (2023, February 14). Step by Step Content-Based Recommender system. Medium. https://medium.com/@prateekgaurav/step-by-step-content-based-recommendation-system-823bbfd0541c
Gershman, A., Meisels, A., Luke, K.-H., Rokach, L., Schclar, A., & Sturm, A. (2010). A Decision Tree Based Recommender System. In G. Eichler, P. Kropf, U. Lechner, P. Meesad, & H. Unger (Eds.), 10th International Conference on Innovative Internet Community Services: Vol. 165. Lecture Notes in Informatics (pp. 170–179). Gesellschaft für Informatik. https://dl.gi.de/server/api/core/bitstreams/ca0e5035-3a82-48a1-8eb8-8f49ee374161/content
Gosh, S., Nahar, N., Wahab, M. A., Biswas, M., Hossain, M. S., & Andersson, K. (2021). Recommendation system for E-commerce Using Alternating Least Squares (ALS) on Apache Spark. In P. Vasant, I. Zelinka, & G. W. Weber (Eds.), Intelligent Computing and Optimization: Vol. 1324. Advances in Intelligent Systems and Computing (pp. 880–893). Springer. https://doi.org/10.1007/978-3-030-68154-8_75
Grover, P. (2017, December 28). Various Implementations of Collaborative Filtering. Medium. https://towardsdatascience.com/various-implementations-of-collaborative-filtering-100385c6dfe0
Gunes, I., Kaleli, C., Bilge, A., & Polat, H. (2014). Shilling attacks against recommender systems: a comprehensive survey. Artificial Intelligence Review, 42, 767–799. https://doi.org/10.1007/s10462-012-9364-9
Herimanto, H, Samosir, K., & Ginting, F. (2024). A Comparative Analysis of Content-Based Filtering and TF-IDF Approaches for Enhancing Sports Recommendation Systems. Innovation in research of informatics, 6(2), 90–97. https://doi.org/10.37058/innovatics.v6i2.12404
Joy, J., & Renumol, V. G. (2020). Comparison of Generic Similarity Measures in E-learning Content Recommender System in Cold-Start Condition. In IEEE Bombay Section Signature Conference (pp. 175–179). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IBSSC51096.2020.9332162
Kumar, S. (2022, September 25). Collaborative Filtering based Recommender Systems for Implicit Feedback Data. Sumit’s Diary. https://blog.reachsumit.com/posts/2022/09/explicit-implicit-cf/
Liu, Y., Wang, S., Khan, M. S., & He, J. (2018). A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Mining and Analytics, 1(3), 211–221. https://doi.org/10.26599/BDMA.2018.9020019
Mandal, S., & Maiti, A. (2018). Explicit Feedbacks Meet with Implicit Feedbacks: A Combined Approach for Recommender system. In L.M. Aiello, H. Cherifi, P. Lió, L.M. Rocha, C. Cherifi, R. Lambiotte (Eds.), 7th International Conference on Complex Networks and their Applications: Vol. 813. Studies in Computational Intelligence (pp. 169–181). Springer. https://doi.org/10.1007/978-3-030-05414-4_14
Marchenko, O. O. (2016). A Method for Automatic Construction of Ontological Knowledge Bases. Development of a Semantic-Syntactic Model of Natural Language. Cybernetics and Systems Analysis, 52, 20–29. https://doi.org/10.1007/s10559-016-9795-4
Marchenko, O., & Shevchenko, M. (2024). Influence of distance measures and data characteristics on time performance in content-based and collaborative filtering datasets. In A. Anisimov, V. Snytyuk, A. Chris, A. Pester, F. Mallet, I. Krak, N. Cogan, O. Chertov, O. Marchenko, S. Bozóki, T. Needham, V. Tsyganok, & V. Vovk (Eds.), Information Technology and Implementation: Vol. 3909. Central Europe University Repository Workshop Proceedings (pp. 99-108). CEUR-WS. https://ceur-ws.org/Vol-3909/Paper_8.pdf
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), 115. https://doi.org/10.1145/3457607
Meteren, R., & Someren, M. (2000). Using Content-Based Filtering for Recommendation. In Proceedings of the machine learning in the new information age: Vol. 30. MLnet/ECML2000 workshop (pp. 47–56). ICS. https://users.ics.forth.gr/~potamias/mlnia/paper_6.pdf
Analytics Vidhya. (2024, October 14). Movie Recommendation and Rating Prediction using K-Nearest Neighbors. https://www.analyticsvidhya.com/blog/2020/08/recommendation-system-k-nearest-neighbors/
Nguyen, A. (2016). Singular Value Decomposition in Recommender Systems [Honors project, Texas Christian University]. TCU Digital Repository. https://repository.tcu.edu/server/api/core/bitstreams/7483e691-6fc0-4a82-9185-3adeb00cde44/content
Qazi, M., Fung, G. M., Meissner, K. J., & Fontes, E. R. (2017). An insurance recommendation system using Bayesian networks. In 11th ACM Conference on Recommender Systems (pp. 274–278). Association for Computing Machinery. https://doi.org/10.1145/3109859.3109907
Ricci, F. (2002). Content-Based Filtering and Hybrid Methods. EIA. http://eia.udg.es/arl/Agentsoftware/3-ContentBasedHybrid.pdf
Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9, 59. https://doi.org/10.1186/s40537-022-00592-5
Sabiri, B., Khtira, A., El Asri, B., & Rhanoui, M. (2025). Hybrid Quality-Based Recommender Systems: A Systematic Literature Review. Journal of Imaging, 11(1), 12. https://doi.org/10.3390/jimaging11010012
Saranya, K. G., Sadasivam, G. S., & Chandralekha, M. (2016). Performance Comparison of Different Similarity Measures for Collaborative Filtering Technique. Indian Journal of Science and Technology, 9(29), 1–8. https://doi.org/10.17485/ijst/2016/v9i29/91060
Steck, H. (2019). Markov Random Fields for Collaborative Filtering. Advances in Neural Information Processing Systems, 32, 5473–5484. https://doi.org/10.48550/arXiv.1910.09645
Sun, S.-B., Zhang, Z.-H., Dong, X.-L., Zhang, H.-R., Li, T.-J., Zhang, L., & Min, F. (2017). Integrating Triangle and Jaccard similarities for recommendation. PLoS ONE, 12(8), e0183570. https://doi.org/10.1371/journal.pone.0183570
Vijay, H. (2020, April 11). Recommendation System using kNN. Auriga. https://aurigait.com/blog/recommendation-system-using-knn/
Wayesa, F., Betalo, M. L., Asefa, G. & Kedir, A. (2023). Pattern-based hybrid book recommender system using semantic relationships. Scientific Report, 13, 3693. https://doi.org/10.1038/s41598-023-30987-0
Wijewickrema, M., Petras, V., & Dias, N. (2019). Selecting a text similarity measure for a content-based recommender system: A comparison in two corpora. The Electronic Library, 37(3), 506–527. https://doi.org/10.1108/EL-08-2018-0165
Xia, Z., Sun, A., Xu, J., Peng, Y., Ma, R., & Cheng, M. (2024). Contemporary Recommendation Systems on Big Data and Their Applications: A Survey. IEEE Access, 12, 196914–196928. https://doi.org/10.1109/ACCESS.2024.3517492
Yadav, V., Shukla, R., Tripathi, A., & Maurya, A. (2021). A New Approach for Movie Recommender System using K-means Clustering and PCA. Journal of Scientific & Industrial Research, 80(2), 159–165. https://doi.org/10.56042/JSIR.V80I02.40102
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 12. https://doi.org/10.48550/arXiv.1902.04885
Zisopoulos, Z., Karagiannidis, S., Demirtsoglou, G., & Antaris, S. (2008, October). Content-Based Recommender systems. ResearchGate. https://www.researchgate.net/publication/236895069_Content-Based_Recommendation_Systems
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2025 Advanced Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License
