Recommender Systems: Algorithms, Evaluation and Limitations
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Published
May 14, 2020
Page:
121-137
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Mubaraka Sani Ibrahim
Department of Computer Science, Baze University, Abuja, Nigeria.
Charles Isah Saidu
Department of Computer Science, Baze University, Abuja, Nigeria.
Abstract
Aims/ objectives: This paper presents the different types of recommender filtering techniques. The main objective of the study is to provide a review of classical methods used in recommender systems such as collaborative filtering, content-based filtering and hybrid filtering, highlighting the main advantages and limitations. This paper also discusses the state-of-art machine learning based recommendation models including Clustering models and Bayesian Classifiers. Further, we discuss the widespread application of recommender systems to a variety of areas such as e-learning and e-news. Finally, the paper evaluates the performance of matrix factorization-based models, nearest neighbours algorithms and co-clustering algorithms in terms of different metrics.
Keywords:
Recommender system, collaborative filtering, recommendation, content filtering, evaluation
Article Details
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Section
Review Article
References
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Schafer J, Frankowski D, Herlocker J, Sen S. Collaborative filtering. In the Adaptive Web., Springer, Berlin, Heidelberg. 2007;4321.
Pazzani M, Billsus D. Content-based recommendation systems. In the Adaptive Web. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg. 2007;4321.
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Allen R. User models: Theory, method, practice. International Journal Man-Machine Studies; 1990.
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Luo X, Zhou M, Xia Y, Zhu Q. An efficient non-negative matrix-factorization based approach to collaborative filtering for recommender systems. In IEEE Transactions on Industrial Informatics; 2014.
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Bouraga S, Jureta I, Faulkner S, Herssens C. Knowledge-based recommendation systems: A survey. International Journal of Intelligent Information Technologies. 2014;10:1-19.
Burke R. Hybrid web recommender systems. In the Adaptive Web, Springer, Berlin Heidelberg. 2007;377-408.
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Wasid M, Ali R. An improved recommender system based on multi-criteria clustering approach. In 8th International Congress of Information and Communication Technology (ICICT- 2018); 2018.
Ghazanfar M, Prugel-Bennett A. Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Systems with Applications. 2014;41(7):3261-3275.
Shinde SKU. Hybrid personalized recommender system using centering-bunching based clustering algorithm. Expert Systems with Applications. International Journal. 2012;39:1381-1387.
Lee T, Jonghoon C, Shim J, Lee S. An ontology-based product recommender system for B2B market places. International Journal of Electronic Commerce. 2006;11:125-155.
Zimmerman J, Kauapati K, Buczak AL, Schaffer D, Gutta S, Martino J. TV personalization system. In Personalized Digital Television. Human-Computer Interaction Series, Springer, Dordrecht. 2004;6.
Liu J, Dolan P, Pedersen E. Personalized news recommendation based on click behavior. International Conference on Intelligent User Interfaces Proceedings IUI, in Intelligent User Interfaces, Proceedings IUI; 2010.
Billsus D, Pazzani M. Hybrid user model for news story classification. In Seventh International Conference on User Modeling, Banff, Canada; 1999.
Lin J. The neural hype and comparisons against weak baselines. ACM SIGIR Forum. 2019;52:40-51.
Zhang S, et al. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys. 2018;52(1).
Sheikh AGR, Koriagin E, Ho Y, Shirvany R, Vollgraf R, et al. A deep learning system for predicting size and fit in fashion e-commerce. In 13th ACM Conference on Recommender Systems; 2019.
He X, Liao L, Zhang H, Nie L, Hu X, Chua T. Neural collaborative filtering. In 26th International Conference on World Wide Web; 2017.
Porcel C, Lopez-Herrera A, Herrera-Viedma E. Recommender system for research resources based on fuzzy linguistic modeling expert systems with applications. Expert Systems with Applications. 2019;36:5173-5183.
Chen C, Duh L. Personalized web-based tutoring system based on fuzzy item response theory. Expert Systems with Applications. 2009;34:2298-2315.
Bobadilla J, Ortega FH, Javier A. Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-Based Systems. 2011;24(8):1310-1316.
Kim K, Ahn H. Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems. In Artificial Intelligence and Simulation. AIS 2004. Lecture Notes in Computer Science, Springer, Berlin Heidelberg. 2005;3397.
Marung U, Theera-Umpon N, Auephanwiriyakul S. Top-N recommender systems using genetic algorithm-based visual-clustering methods. Symmetry. 2016;8:54.
Maxwell Harper F, Konstan JA. The MovieLens datasets: History and context. 2015;5(4).
Baatarjav E, Phithakkitnukoon S, Dantu R. Group recommendation system for Facebook. 2008;211-219.
Goel A, Lin J, Sharma A, Wang D, Zadeh R. WTF: The who-to-follow system at Twitter. Proceedings of the 22nd International Conference on World Wide Web; 2013.
De Bra P, Calvi L. AHA! An open adaptive hypermedia architecture. New Review of Hypermedia and Multimedia. 1998;4(1):115-139.
Resnick P, Lacovou N, Suchak M, Bergstrom P, Riedl J. GroupLens: An open architecture for collaborative filtering of netnews. Proceedings of the 1994 ACM Conference. 1994;175-186.
Lang K. Newsweeder: Learning to filter netnews. In Proceedings of the Twelfth International Conference on Machine Learning; 1995.
Shardanand U, Maes P. Social information filtering: Algorithms for automating. In Proceedings of CHI 95. Denver, CO.; 1995.
Netflix. [Online]
Available:http://www.netflix.com/
Imran H, Hoang Q, Chang T, Graf S, Kinshuk D. A framework to provide personalization in learning management systems through a recommender system approach; 2014.
Ibrahim MS, Hamada M. Adaptive learning framework. In IEEE 15th International Conference on Information Technology Based Higher Education and Training ITHET; 2016.
Schafer J, Frankowski D, Herlocker J, Sen S. Collaborative filtering. In the Adaptive Web., Springer, Berlin, Heidelberg. 2007;4321.
Pazzani M, Billsus D. Content-based recommendation systems. In the Adaptive Web. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg. 2007;4321.
Ricci F, Rokach L, Bracha Shapira B, Paul BK. Recommender systems handbook, 1st Ed. Springer. Berlin, Heidelberg. 2010;151-153.
Lu J, Wu D, Mao M, Wang W, Zhang G. Recommender system application developments: A survey. Decision Support Systems. 2015;74.
Blanda S. Online recommender systems-how does a website know what i want? 2015. [Online]. Available:https://blogs.ams.org/mathgradblog/2015/05/25/online-recommender-systems-website-want/
[Accessed 8 February 2020]
Cacheda F, Carneiro V, Fernandez D, Formoso V. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high performance recommender systems. ACM Transactions, Web 5. 2011;1:Article 2.
Ge F. User-based collaborative filtering recommendation algorithm based on Folksonomy smoothing. In Advances in Computer Science and Education Applications. Communications in Computer and Information Science. Springer, Berlin, Heidelberg. 2011;202.
Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering. In Recommendation Algorithms. Proceedings of ACM World Wide Web Conference; 2001.
Allen R. User models: Theory, method, practice. International Journal Man-Machine Studies; 1990.
Raghuwanshi S, Pateriya R. Recommendation systems: Techniques, challenges, application and evaluation. Soft Computing for Problem Solving SocProS. 2017;2.
Desrosiers C, Karypis G. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook, Springer, Boston, MA; 2011.
Do MT, Nguyen VD, Nguyen L. Model-based approach for collaborative filtering. In 6th International Conference on Information Technology for Education; 2010.
George T, Merugu S. A scalable collaborative filtering framework based on coclustering. In Fifth IEEE International Conference on Data Mining (ICDM’05); 2005.
Banerjee A, Dhillon I, Ghosh J, Merugu S, Modha D. A generalized maximum entropy approach to bregman co-clustering and matrix approximation. In Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2004.
Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer. 2009;42(8):30-37.
Koren Y. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08); 2008.
Luo X, Zhou M, Xia Y, Zhu Q. An efficient non-negative matrix-factorization based approach to collaborative filtering for recommender systems. In IEEE Transactions on Industrial Informatics; 2014.
Ghazanfar MA, Prugel-Bennett A. An improved switching hybrid recommender system using Naive Bayes classifier and collaborative filtering. In International MultiConference of Engineers and Computer Scientists IMECS. 2010;1.
Mooney RJ, Roy L. Content-based book recommending using learning for text categorization. In DL-00, 5th ACM Conference on Digital Libraries, San Antonio, US.
Jain A, Gupta C. Fuzzy logic in recommender systems. In Fuzzy Logic Augmentation of Neural and Optimization Algorithms, Springer. 2018;255-273.
Aggarwal C. Recommender systems: The textbook, 1st Ed. Springer, Switzerland; 2016.
Burke R. Knowledge-based recommender systems. Encyclopedia of Library and Information Science. 2000;69(32):180-200.
Bouraga S, Jureta I, Faulkner S, Herssens C. Knowledge-based recommendation systems: A survey. International Journal of Intelligent Information Technologies. 2014;10:1-19.
Burke R. Hybrid web recommender systems. In the Adaptive Web, Springer, Berlin Heidelberg. 2007;377-408.
Cano E. Hybrid recommender systems: A systematic literature review. Intelligent Data Analysis. 2017;21:1487-1524.
Wasid M, Ali R. An improved recommender system based on multi-criteria clustering approach. In 8th International Congress of Information and Communication Technology (ICICT- 2018); 2018.
Ghazanfar M, Prugel-Bennett A. Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Systems with Applications. 2014;41(7):3261-3275.
Shinde SKU. Hybrid personalized recommender system using centering-bunching based clustering algorithm. Expert Systems with Applications. International Journal. 2012;39:1381-1387.
Lee T, Jonghoon C, Shim J, Lee S. An ontology-based product recommender system for B2B market places. International Journal of Electronic Commerce. 2006;11:125-155.
Zimmerman J, Kauapati K, Buczak AL, Schaffer D, Gutta S, Martino J. TV personalization system. In Personalized Digital Television. Human-Computer Interaction Series, Springer, Dordrecht. 2004;6.
Liu J, Dolan P, Pedersen E. Personalized news recommendation based on click behavior. International Conference on Intelligent User Interfaces Proceedings IUI, in Intelligent User Interfaces, Proceedings IUI; 2010.
Billsus D, Pazzani M. Hybrid user model for news story classification. In Seventh International Conference on User Modeling, Banff, Canada; 1999.
Lin J. The neural hype and comparisons against weak baselines. ACM SIGIR Forum. 2019;52:40-51.
Zhang S, et al. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys. 2018;52(1).
Sheikh AGR, Koriagin E, Ho Y, Shirvany R, Vollgraf R, et al. A deep learning system for predicting size and fit in fashion e-commerce. In 13th ACM Conference on Recommender Systems; 2019.
He X, Liao L, Zhang H, Nie L, Hu X, Chua T. Neural collaborative filtering. In 26th International Conference on World Wide Web; 2017.
Porcel C, Lopez-Herrera A, Herrera-Viedma E. Recommender system for research resources based on fuzzy linguistic modeling expert systems with applications. Expert Systems with Applications. 2019;36:5173-5183.
Chen C, Duh L. Personalized web-based tutoring system based on fuzzy item response theory. Expert Systems with Applications. 2009;34:2298-2315.
Bobadilla J, Ortega FH, Javier A. Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-Based Systems. 2011;24(8):1310-1316.
Kim K, Ahn H. Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems. In Artificial Intelligence and Simulation. AIS 2004. Lecture Notes in Computer Science, Springer, Berlin Heidelberg. 2005;3397.
Marung U, Theera-Umpon N, Auephanwiriyakul S. Top-N recommender systems using genetic algorithm-based visual-clustering methods. Symmetry. 2016;8:54.
Maxwell Harper F, Konstan JA. The MovieLens datasets: History and context. 2015;5(4).