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Toward Recommender Systems Scalability and Efficacy

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Concurrency, Specification and Programming

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1091))

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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Notes

  1. 1.

    https://grouplens.org/datasets/movielens/.

References

  1. 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

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. 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

  4. 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)

    Google Scholar 

  5. 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

  6. 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

    Article  Google Scholar 

  7. Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997). https://doi.org/10.1145/245108.245124

    Article  Google Scholar 

  8. 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

  9. 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)

    Google Scholar 

  10. Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12 (2002). https://doi.org/10.1023/A:1021240730564

  11. 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

    Article  Google Scholar 

  12. 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

  13. 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

  14. 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

  15. 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

    Article  Google Scholar 

  16. 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

  17. Freudenthaler, C., Schmidt-thieme, L., Rendle, S.: Bayesian factorization machines (2010)

    Google Scholar 

  18. Freudenthaler, C., Schmidt-Thieme, L., Rendle, S.: Factorization machines factorized polynomial regression models (2011)

    Google Scholar 

  19. 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

  20. 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

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. 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

  23. 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

  24. 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

    Article  Google Scholar 

  25. 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

  26. 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

    Article  Google Scholar 

  27. 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

    Article  Google Scholar 

  28. 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

    Article  Google Scholar 

  29. 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

    Article  Google Scholar 

  30. 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

  31. 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

    Article  Google Scholar 

  32. 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

  33. 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

  34. 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

  35. 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)

    Chapter  Google Scholar 

  36. 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

  37. 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

  38. 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

    Article  Google Scholar 

  39. 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

  40. 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

  41. 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)

    Chapter  Google Scholar 

  42. 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

  43. 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

  44. 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

    Article  Google Scholar 

  45. 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

    Article  Google Scholar 

  46. 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)

    Google Scholar 

  47. 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

  48. Quadrana, M., Cremonesi, P., Jannach, D.: Sequence-aware recommender systems. ACM Comput. Surv. 51(4), 1–36 (2019). https://doi.org/10.1145/3190616

    Article  Google Scholar 

  49. 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

    Article  Google Scholar 

  50. Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp. 995–1000 (2010). https://doi.org/10.1109/ICDM.2010.127

  51. Rendle, S.: Factorization machines with libfm. ACM Trans. Intell. Syst. Technol. 3(3) (2012). https://doi.org/10.1145/2168752.2168771

  52. 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

  53. 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

  54. 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

  55. 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

    Article  Google Scholar 

  56. 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

    Article  Google Scholar 

  57. 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)

    Chapter  Google Scholar 

  58. 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)

    Google Scholar 

  59. 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

  60. 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

  61. 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

    Article  Google Scholar 

  62. 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

    Article  Google Scholar 

  63. 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

  64. 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

  65. 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

    Article  Google Scholar 

  66. 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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Acknowledgements

Research supported by Polish National Science Centre (NCN) grant no. 2018/31/N/ST6/00610.

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Authors and Affiliations

  1. Institute of Informatics, University of Warsaw, ul. Banacha 2, 02-097, Warsaw, Poland

    Eyad Kannout, Marek Grzegorowski & Hung Son Nguyen

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  1. Eyad Kannout
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  2. Marek Grzegorowski
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  3. Hung Son Nguyen
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Correspondence to Hung Son Nguyen .

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Editors and Affiliations

  1. Institut für Informatik, Humboldt Universität zu Berlin, Berlin, Germany

    Bernd-Holger Schlingloff

  2. Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany

    Thomas Vogel

  3. System Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Andrzej Skowron

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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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