Abstract
Recommendation systems recommender systems are a subcategory of information filtering that is utilized to determine the preferences of users towards certain items. These systems emerged in the 1990’s and they have since changed the intelligence of both the web and humans. Vast amounts of research papers have been published in various domains. Recommendation systems suggest items to users and their principal purpose is to recommend items that are predicted to be suitable for users. Some of the most popular domains where recommendation systems are used include movies, music, jokes, restaurants, financial services, life insurance, Instagram Facebook and twitter followers. This paper explores different collaborative filtering algorithms. In so doing, the paper looks at the strengths and challenges (open issues) faced by this technique. The open issues give direction of future research work to researchers and also provide information of where to use collaborative filtering recommender systems applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Su, X., Khoshgoftaar, M.T.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 1–20 (2009)
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16, 261–273 (2015)
Breese, J., Heckerma, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, San Francisco, CA (1998)
Mustafa, N., Osman, A., Ahmed, A., Abdullah, A.: Collaborative filtering: techniques and applications. In: Conference: 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE) (2017)
Lee, J., Sun, M., Lebanon, G.: A comparative study of collaborative filtering algorithms. arXiv:1205.3193v1 [cs.IR] (2012)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)
Al-Barznji, K., Atanassov, A.: Comparison of memory based filtering techniques for generating recommendations on large data. Eng. Autom. 1(1), 44–50 (2018)
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2011)
Xiaoyuan, S., Taghi, M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–20 (2009)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. Madison, Wisconsin (1998)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: ACM 1-58113-348-0/01/0005, Hong Kong (2001)
Schafer, B.J., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, pp. 291–324. Springer, Heidelberg (2007)
Nagpal, D., Kaur, S., Gujral, S., Singh, A.: FR: A Recommender for Finding Faculty Based on CF Technique (2015)
Bahadorpour, M., Neysiani, B.S., Shahraki, M.N.: Determining optimal number of neighbors in item-based kNN collaborative filtering algorithm for learning preferences of new users. J. Telecommun. 9(3), 163–167 (2017)
Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative Filtering Recommender Systems. Now Publishers Inc., Boston (2011)
Saptono, R.: User-Item Based Collaborative Filtering for Improved Recommendation (2010)
Nakamura, A., Abe, N.: Collaborative filtering using weighted majority prediction algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning, San Francisco, CA, USA (1998)
Kim, H.-N., Ji, A.-T., Ha, I., Jo, G.-S.: Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electron. Commer. Res. Appl. 9(1), 73–83 (2010)
Al-Bashiri, H., Abdulgabber, M.A., Romli, A., Kahtan, H.: An Improved Memory-Based Collaborative Filtering Method Based on The TOPSIS (2018)
Do, T., Phung, M., Nguyen, V.: Model-based approach for collaborative filtering. In: The 6th International Conference on Information Technology for Education, Ho Chi Minh city, Vietnam (2010)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Deerwester, S., Dumais, S.T., Furnas, G., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)
Gorrell, G.: Generalized Hebbian algorithm for incremental singular value decomposition in natural language processing. In: EACL, pp. 97–104 (2006)
Kurucz, M., Benczúr, A.A., Csalogány, A.: Methods for large scale SVD with missing values. In: KDD Cup and Workshop (2007)
Sanger, T.D.: Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Netw. 2(6), 459–473 (1989)
Miller, B.N., Konstan, J.A., Riedl, J.: PocketLens: toward a personal recommender system. ACM Trans. Inf. Syst. 22(3), 437–476 (2004)
Funk, S. (2006). http://sifter.org/simon/journal/20061211.html
Funk, S.: Netflix (2006). http://sifter.org/˜simon/journal/20061211.html
Sarwar, B., Karypis, G., Konstan, J.A., Riedl, J.: Application of dimensionality reduction in recommender system, 02 November 2000. Accessed 2019
Berry, M.W., Dumais, S.T., O’Brien, G.W.: Using linear algebra for intelligent information retrieval. SIAM Rev. 37, 573–595 (1995)
Sarwar, B., Karypis, G., Konstan, A.J., Riedl, J.: Incremental SVD-based algorithms for highly scalable recommender systems (2002)
Brand, M.E.: Incremental Singular Value Decomposition of Incomplete Data (2003)
Rajput, A., Aharwal, R.P., Dubey, M., Saxena, S., Raghuvanshi, M.: J48 and JRIP rules for e-governance data. Int. J. Comput. Sci. Secur. (IJCSS) 5(2), 201 (2011)
Hastie, T., Tibshirani, T., Friedman, R.: Unsupervised learning. In: The Elements of Statistical Learning. Springer, New York (2009)
Kavzoglu, T., Mather, P.M.: The use of backpropagating artificial neural networks in land cover classification. Int. J. Remote Sens. 24(23), 4907–4938 (2003)
Park, D.C., El-Sharkawi, M.A., Marks, R.J., Atlas, L.E., Damborg, M.J.: Electric load forecasting using artificial neural network. IEEE Trans. Power Syst. 6(2), 442–449 (1991)
Jung, Y.G., Kang, M.S., Heo, J.: Clustering performance comparison using K-means and expectation maximization algorithms. Biotechnol. Biotechnol. Equip. 28, 44–48 (2014)
Shepperd, M., Kadoda, G.: Comparing software prediction techniques using simulation. IEEE Trans. Software Eng. 27(11), 1014–1022 (2001)
Jadhav, S.D., Channe, H.P.: Efficient recommendation system using decision tree classifier and collaborative filtering. Int. Res. J. Eng. Technol. 3(8), 2114–2118 (2016)
Ungar, H.L., Foster, D.P.: Clustering methods for collaborative filtering. In: AAAI Workshop on Recommender Systems (1998)
Shrkhorshidi, A.S., Aghabozorgi, S., Wah, T.Y.: A Comparison Study on Similarity and Dissimilarity Measure in Clastering Continuous Data (2015)
Jeyasekar, A., Akshay, K., Karan: Collaborative filtering using Euclidean distance in recommendation engine. Indian J. Sci. Technol. 9(37) (2016)
Zheng, M., Min, F., Zhang, H.-R., Chen, W.-B.: Fast Recommendations With the M-Distance (2016)
Torres, R.D.: Combining Collaborative and Content-based Filtering to Recommend Research Paper (2004)
Keenan, T.: Upwork Global Inc., 28 March 2019. https://www.upwork.com/hiring/data/how-collaborative-filtering-works/
Anand, S.S., Mobasher, B.: Intelligent techniques for web personalization. In: IJCAI Workshop on Intelligent Techniques for Web Personalization (2003)
Lü, L., Medo, M., Yeung, C.H., Zhang, C.Y., Zhang, Z.K., Zhou, T.: Recommender systems. Phys. Rep. 519(1), 1–49 (2012)
Madhukar, M.: Challenges & limitation in recommender systems. Int. J. Latest Trends Eng. Technol. (IJLTET) 4(3), 138–142 (2014)
Park, S.-T., Chu, W.: Pairwise preference regression for cold-start recommendation. In: Proceedings of the 2009 ACM Conference on Recommender Systems, New York (2009)
Shinde, U., Shedge, R.: Comparative analysis of collaborative filtering technique. IOSR J. Comput. Eng. (IOSR-JCE) 10, 77–82 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Manamolela, L., Zuva, T., Appiah, M. (2020). Collaborative Filtering Recommendation Systems Algorithms, Strengths and Open Issues. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives in Intelligent Systems. CoMeSySo 2020. Advances in Intelligent Systems and Computing, vol 1295. Springer, Cham. https://doi.org/10.1007/978-3-030-63319-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-63319-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63318-9
Online ISBN: 978-3-030-63319-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)