Abstract
Recommender systems play a key role in many branches of the digital economy. Their primary function is to select the most relevant services or products to users’ preferences. The article presents selected recommender algorithms and their most popular taxonomy. We review the evaluation techniques and the most important challenges and limitations of the discussed methods. We also introduce Factorization Machines and Association Rules-based recommender system (FMAR) that addresses the problem of efficiency in generating recommendations while maintaining quality.
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Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005). https://doi.org/10.1109/TKDE.2005.99
Afoudi, Y., Lazaar, M., Al Achhab, M.: Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network. Simul. Model. Pract. Theory 113, 102375 (2021). https://doi.org/10.1016/j.simpat.2021.102375
Aggarwal, C.C.: Ensemble-Based and Hybrid Recommender Systems, pp. 199–224. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3_6
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)
Anelli, V.W., Bellogin, A., Ferrara, A., Malitesta, D., Merra, F.A., Pomo, C., Donini, F.M., Di Noia, T.: Elliot: A comprehensive and rigorous framework for reproducible recommender systems evaluation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’21, pp. 2405–2414. Association for Computing Machinery, New York (2021). https://doi.org/10.1145/3404835.3463245
Bai, X., Wang, M., Lee, I., Yang, Z., Kong, X., Xia, F.: Scientific paper recommendation: a survey. IEEE Access 7, 9324–9339 (2019). https://doi.org/10.1109/ACCESS.2018.2890388
Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997). https://doi.org/10.1145/245108.245124
Bendouch, M.M., Frasincar, F., Robal, T.: Addressing scalability issues in semantics-driven recommender systems. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT ’21, pp. 56–63. Association for Computing Machinery, New York (2021). https://doi.org/10.1145/3486622.3493963
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, UAI’98, pp. 43–52. Morgan Kaufmann Publishers Inc., San Francisco (1998)
Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12 (2002). https://doi.org/10.1023/A:1021240730564
Chen, R., Hua, Q., Chang, Y.S., Wang, B., Zhang, L., Kong, X.: A survey of collaborative filtering-based recommender systems: from traditional methods to hybrid methods based on social networks. IEEE Access 6, 64301–64320 (2018). https://doi.org/10.1109/ACCESS.2018.2877208
Cunha, T., Soares, C., de Carvalho, A.C.: Metalearning and recommender systems: a literature review and empirical study on the algorithm selection problem for collaborative filtering. Inf. Sci. 423, 128–144 (2018). https://doi.org/10.1016/j.ins.2017.09.050
Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum.-Comput. Interact. 4(2), 81–173 (2011). https://doi.org/10.1561/1100000009
Fayyad, U.: Knowledge Discovery in Databases: An Overview, pp. 28–47. Springer, Berlin (2001). https://doi.org/10.1007/978-3-662-04599-2_2
Feng, J., Xia, Z., Feng, X., Peng, J.: RBPR: a hybrid model for the new user cold start problem in recommender systems. Knowl.-Based Syst. 214, 106732 (2021). https://doi.org/10.1016/j.knosys.2020.106732
Fouss, F., Fernandes, E.: A closer-to-reality model for comparing relevant dimensions of recommender systems, with application to novelty. Information 12(12) (2021). https://www.mdpi.com/2078-2489/12/12/500
Freudenthaler, C., Schmidt-thieme, L., Rendle, S.: Bayesian factorization machines (2010)
Freudenthaler, C., Schmidt-Thieme, L., Rendle, S.: Factorization machines factorized polynomial regression models (2011)
de Gemmis, M., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Semantics-Aware Content-Based Recommender Systems, pp. 119–159. Springer US, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_4
Grzegorowski, M., Litwin, J., Wnuk, M., Pabis, M., Marcinowski, L.: Survival-based feature extraction - application in supply management for dispersed vending machines. IEEE Trans. Industr. Inf. (2022). https://doi.org/10.1109/TII.2022.3178547
Grzegorowski, M., Zdravevski, E., Janusz, A., Lameski, P., Apanowicz, C., Ślȩzak, D.: Cost optimization for big data workloads based on dynamic scheduling and cluster-size tuning. Big Data Res. 25, 100203 (2021). https://doi.org/10.1016/j.bdr.2021.100203
Gunawardana, A., Shani, G.: Evaluating Recommender Systems, pp. 265–308. Springer US, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_8
Han, J., Kamber, M., Pei, J.: 6 - mining frequent patterns, associations, and correlations: basic concepts and methods. In:  Han, J., Kamber, M., Pei, J. (eds.), Data Mining (Third Edition), The Morgan Kaufmann Series in Data Management Systems, 3rd edn., pp. 243–278. Morgan Kaufmann, Boston (2012). https://www.sciencedirect.com/science/article/pii/B978012381479100006X
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. SIGMOD Rec. 29(2), 1–12 (2000). https://doi.org/10.1145/335191.335372
Himabindu, T., Padmanabhan, V., Pujari, A.K.: Conformal matrix factorization based recommender system. Inf. Sci. 467 (2018). https://doi.org/10.1016/j.ins.2018.04.004
Himeur, Y., Alsalemi, A., Al-Kababji, A., Bensaali, F., Amira, A., Sardianos, C., Dimitrakopoulos, G., Varlamis, I.: A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects. Inf. Fus. 72, 1–21 (2021). https://doi.org/10.1016/j.inffus.2021.02.002
Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining - a general survey and comparison. SIGKDD Explor. Newsl. 2(1), 58–64 (2000). https://doi.org/10.1145/360402.360421
Idrissi, N., Zellou, A.: A systematic literature review of sparsity issues in recommender systems. Soc. Netw. Anal. Min. 10(1), 15 (2020). https://doi.org/10.1007/s13278-020-0626-2
Jalili, M., Ahmadian, S., Izadi, M., Moradi, P., Salehi, M.: Evaluating collaborative filtering recommender algorithms: a survey. IEEE Access 6, 74003–74024 (2018). https://doi.org/10.1109/ACCESS.2018.2883742
Kannout, E.: Context clustering-based recommender systems. In: 2020 15th Conference on Computer Science and Information Systems (FedCSIS), vol. 21, pp. 85–91 (2020). https://doi.org/10.15439/2020F54
Karimi, M., Jannach, D., Jugovac, M.: News recommender systems - survey and roads ahead. Inf. Process. Manag. 54(6), 1203–1227 (2018). https://doi.org/10.1016/j.ipm.2018.04.008
Ko, H., Lee, S., Park, Y., Choi, A.: A survey of recommendation systems: recommendation models, techniques, and application fields. Electronics 11(1) (2022). https://www.mdpi.com/2079-9292/11/1/141
Koren, Y., Bell, R.: Advances in Collaborative Filtering, pp. 77–118. Springer US, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_3
Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41(4, Part 2), 2065–2073 (2014). https://doi.org/10.1016/j.eswa.2013.09.005
Lin, C.J., Kuo, T.T., Lin, S.D.: A content-based matrix factorization model for recipe recommendation. In: Tseng, V.S., Ho, T.B., Zhou, Z.H., Chen, A.L.P., Kao, H.Y. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 560–571. Springer International Publishing, Cham (2014)
Miyahara, K., Pazzani, M.: Collaborative filtering with the simple Bayesian classifier. Inf. Process. Soc. Japan 43 (2002). https://doi.org/10.1007/3-540-44533-1_68
Mohamed, M.H., Khafagy, M.H., Ibrahim, M.H.: Recommender systems challenges and solutions survey. In: 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), pp. 149–155 (2019). https://doi.org/10.1109/ITCE.2019.8646645
Movahedian, H., Khayyambashi, M.R.: Folksonomy-based user interest and disinterest profiling for improved recommendations: an ontological approach. J. Inf. Sci. 40(5), 594–610 (2014). https://doi.org/10.1177/0165551514539870
Navgaran, D.Z., Moradi, P., Akhlaghian, F.: Evolutionary based matrix factorization method for collaborative filtering systems. In: 2013 21st Iranian Conference on Electrical Engineering (ICEE), pp. 1–5 (2013). https://doi.org/10.1109/IranianCEE.2013.6599844
Nguyen, H.S.: Efficient machine learning methods over pairwise space (keynote). In:  Schlingloff, H., Vogel, T., (eds.) Proceedings of the 29th International Workshop on Concurrency, Specification and Programming (CS &P 2021), Berlin, Germany, September 27–28, 2021, CEUR Workshop Proceedings, vol. 2951, pp. 117–119. CEUR-WS.org (2021). http://ceur-ws.org/Vol-2951/keynote2.pdf
Park, M.H., Hong, J.H., Cho, S.B.: Location-based recommendation system using Bayesian user’s preference model in mobile devices. In: Indulska, J., Ma, J., Yang, L.T., Ungerer, T., Cao, J. (eds.) Ubiquit. Intell. Comput., pp. 1130–1139. Springer, Berlin (2007)
Pawlicka, A., Pawlicki, M., Kozik, R., ChoraĹ›, R.S.: A systematic review of recommender systems and their applications in cybersecurity. Sensors 21(15) (2021). https://www.mdpi.com/1424-8220/21/15/5248
Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems, pp. 325–341. Springer, Berlin (2007). https://doi.org/10.1007/978-3-540-72079-9_10
PĂ©rez-Almaguer, Y., Yera, R., Alzahrani, A.A., MartĂnez, L.: Content-based group recommender systems: a general taxonomy and further improvements. Expert Syst. Appl. 184, 115444 (2021). https://doi.org/10.1016/j.eswa.2021.115444
PĂ©rez-Almaguer, Y., Yera, R., Alzahrani, A.A., MartĂnez, L.: Content-based group recommender systems: a general taxonomy and further improvements. Expert Syst. Appl. 184, 115444 (2021). https://doi.org/10.1016/j.eswa.2021.115444. www.sciencedirect.com/science/article/pii/S0957417421008587
Philip, S., Shola, P.B., John, A.O.: Application of content-based approach in research paper recommendation system for a digital library. Int. J. Adv. Comput. Sci. Appl. 5 (2014)
Porcel, C., López-Herrera, A., Herrera-Viedma, E.: A recommender system for research resources based on fuzzy linguistic modeling. Expert Syst. Appl. 36(3, Part 1), 5173–5183 (2009). https://doi.org/10.1016/j.eswa.2008.06.038. https://www.sciencedirect.com/science/article/pii/S0957417408003126
Quadrana, M., Cremonesi, P., Jannach, D.: Sequence-aware recommender systems. ACM Comput. Surv. 51(4), 1–36 (2019). https://doi.org/10.1145/3190616
Ranjbar, M., Moradi, P., Azami, M., Jalili, M.: An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems. Eng. Appl. Artif. Intell. 46, 58–66 (2015). https://doi.org/10.1016/j.engappai.2015.08.010. www.sciencedirect.com/science/article/pii/S0952197615001888
Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp. 995–1000 (2010). https://doi.org/10.1109/ICDM.2010.127
Rendle, S.: Factorization machines with libfm. ACM Trans. Intell. Syst. Technol. 3(3) (2012). https://doi.org/10.1145/2168752.2168771
Rendle, S., Gantner, Z., Freudenthaler, C., Schmidt-Thieme, L.: Fast context-aware recommendations with factorization machines. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’11, pp. 635–644. Association for Computing Machinery, New York (2011). https://doi.org/10.1145/2009916.2010002
Ricci, F., Rokach, L., Shapira, B.: Recommender Systems: Introduction and Challenges, pp. 1–34. Springer US, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_1
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender system – a case study. In: ACM WebKDD’00 (Web-mining for ECommerce Workshop (2000). https://doi.org/10.21236/ada439541
Silveira, T., Zhang, M., Lin, X., Liu, Y., Ma, S.: How good your recommender system is? A survey on evaluations in recommendation. Int. J. Mach. Learn. Cybern. 10(5), 813–831 (2019). https://doi.org/10.1007/s13042-017-0762-9
Singh, M.: Scalability and sparsity issues in recommender datasets: a survey. Knowl. Inf. Syst. 62(1), 1–43 (2020). https://doi.org/10.1007/s10115-018-1254-2
Terán, L., Meier, A.: A fuzzy recommender system for elections. In: Andersen, K.N., Francesconi, E., Grönlund, Å., van Engers, T.M. (eds.) Electronic Government and the Information Systems Perspective, pp. 62–76. Springer, Berlin (2010)
Thorat, P.B., Goudar, R.M., Barve, S.: Article: Survey on collaborative filtering, content-based filtering and hybrid recommendation system. Int. J. Comput. Appl. 110(4), 31–36 (2015)
Tilahun, Z., Jun, H., Oad, A.: Solving cold-start problem by combining personality traits and demographic attributes in a user based recommender system. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 7(5), 231–239 (2017). https://doi.org/10.23956/ijarcsse/v7i4/01420
Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys ’11, pp. 109–116. Association for Computing Machinery, New York (2011). https://doi.org/10.1145/2043932.2043955
Walek, B., Fojtik, V.: A hybrid recommender system for recommending relevant movies using an expert system. Expert Syst. Appl. 158, 113452 (2020). https://doi.org/10.1016/j.eswa.2020.113452
Wang, D., Liang, Y., Xu, D., Feng, X., Guan, R.: A content-based recommender system for computer science publications. Knowl.-Based Syst. 157, 1–9 (2018). https://doi.org/10.1016/j.knosys.2018.05.001. www.sciencedirect.com/science/article/pii/S0950705118302107
Wu, Z., Li, C., Cao, J., Ge, Y.: On scalability of association-rule-based recommendation: a unified distributed-computing framework. ACM Trans. Web 14(3) (2020). https://doi.org/10.1145/3398202
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD, KDD ’18, pp. 974–983. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3219819.3219890
Zhou, T., Kuscsik, Z., Liu, J.G., Medo, M., Wakeling, J., Zhang, Y.C.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Natl. Acad. Sci. U.S.A. 107, 4511–5 (2010). https://doi.org/10.1073/pnas.1000488107
Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th international conference on World Wide Web, WWW ’05. Association for Computing Machinery, New York (2005). https://doi.org/10.1145/1060745.1060754
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Research supported by Polish National Science Centre (NCN) grant no. 2018/31/N/ST6/00610.
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Kannout, E., Grzegorowski, M., Son Nguyen, H. (2023). Toward Recommender Systems Scalability and Efficacy. In: Schlingloff, BH., Vogel, T., Skowron, A. (eds) Concurrency, Specification and Programming. Studies in Computational Intelligence, vol 1091. Springer, Cham. https://doi.org/10.1007/978-3-031-26651-5_5
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