Abstract
Collaborative filtering are recommender systems algorithms that provide personalized recommendations to users in various online environments such as movies, music, books, jokes and others. There are many such recommendation algorithms and, regarding experimental evaluations to find which algorithm performs better a lengthy process needs to take place and the time required depends on the size of the dataset and the evaluation metrics used. In this paper we present a novel method that is based on a series of steps that include random subset selections, ensemble learning and the use of well-known evaluation metrics Mean Absolute Error and Precision to identify, in a fast and accurate way, which algorithm performs the best for a given dataset. The proposed method has been experimentally evaluated using two publicly available datasets with the experimental results showing that the time required for the evaluation is significantly reduced, while the results are accurate when compared to a full evaluation cycle.
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
Similar content being viewed by others
References
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)
Polatidis, N., Georgiadis, C.K.: A multi-level collaborative filtering method that improves recommendations. Expert Syst. Appl. 48, 100–110 (2016)
Shojaei, M., Saneifar, H.: MFSR: a novel multi-level fuzzy similarity measure for recommender systems. Expert Syst. Appl. 177, 114969 (2021)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Polatidis, N., Georgiadis, C.K.: A dynamic multi-level collaborative filtering method for improved recommendations. Comput. Stand. Interf. 51, 14–21 (2017)
Anand, D., Bharadwaj, K.K.: Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities. Expert Syst. Appl. 38(5), 5101–5109 (2011)
Bobadilla, J., Serradilla, F., Bernal, J.: A new collaborative filtering metric that improves the behavior of recommender systems. Knowl. Based Syst. 23(6), 520–528 (2010)
Bobadilla, J., Ortega, F., Hernando, A.: A collaborative filtering similarity measure based on singularities. Inf. Process. Manage. 48(2), 204–217 (2012)
Gedikli, F., Bagdat, F., Ge, M., Jannach, D.: RF-REC: Fast and accurate computation of recommendations based on rating frequencies. In: 2011 IEEE 13th Conference on Commerce and Enterprise Computing, pp. 50–57. IEEE (2011)
Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowl. Based Syst. 56, 156–166 (2014)
Najafabadi, M.K., Mahrin, M.N.R., Chuprat, S., Sarkan, H.M.: Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data. Comput. Hum. Behav. 67, 113–128 (2017)
Nilashi, M., Ibrahim, O., Bagherifard, K.: A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst. Appl. 92, 507–520 (2018)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Fifth International Conference on Computer and Information Science, vol. 1, no. 012002, pp. 27–8 (2002)
Son, L.H.: HU-FCF: a hybrid user-based fuzzy collaborative filtering method in recommender systems. Expert Syst. Appl. Int. J. 41(15), 6861–6870 (2014)
Wang, W., Zhang, G., Lu, J.: Collaborative filtering with entropy-driven user similarity in recommender systems. Int. J. Intell. Syst. 30(8), 854–870 (2015)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval, pp. 165–174 (2019)
**aojun, L.: An improved clustering-based collaborative filtering recommendation algorithm. Clust. Comput. 20(2), 1281–1288 (2017). https://doi.org/10.1007/s10586-017-0807-6
Zarzour, H., Al-Sharif, Z., Al-Ayyoub, M., Jararweh, Y.: A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In: 2018 9th International Conference on Information and Communication Systems (ICICS), pp. 102–106. IEEE (2018)
Zhang, S., Yao, L., Xu, X.: AutoSVD++ an efficient hybrid collaborative filtering model via contractive auto-encoders. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 957–960 (2017)
Massa, P., Souren, K., Salvetti, M., Tomasoni, D.: Trustlet, open research on trust metrics. Scalable Comput. Pract. Exper. 9(4) (2008)
Dooms, S., De Pessemier, T., Martens, L.: Movietweetings: a movie rating dataset collected from twitter. In: Workshop on Crowdsourcing and human computation for recommender systems. CrowdRec at RecSys, vol. 2013, p. 43 (2013)
Funk, S. (2006). https://sifter.org/~simon/journal/20061211.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Polatidis, N., Kapetanakis, S., Pimenidis, E., Manolopoulos, Y. (2022). Fast and Accurate Evaluation of Collaborative Filtering Recommendation Algorithms. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_50
Download citation
DOI: https://doi.org/10.1007/978-3-031-21743-2_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21742-5
Online ISBN: 978-3-031-21743-2
eBook Packages: Computer ScienceComputer Science (R0)