Utilizing frequent pattern mining for solving cold-start problem in recommender systems

• Autorzy: Kannout Eyad , Grodzki Michał , Grzegorowski Marek

Identyfikatory

DOI

10.15439/2022F86

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Although several approaches have been proposed throughout the last decade to build recommender systems (RS), most of them suffer from the cold-start problem. This problem occurs when a new item hits the system or a new user signs up. It is generally recognized that the ability to handle cold users and items is one of the key success factors of any new recommender algorithm. This paper introduces a frequent pattern mining framework for recommender systems (FPRS) - a novel approach to address this challenging task. FPRS is a hybrid RS that incorporates collaborative and content-based recommendation algorithms and employs a frequent pattern (FP) growth algorithm. The article proposes several strategies to combine the generated frequent item sets with content-based methods to mitigate the cold-start problem for both new users and new items. The performed empirical evaluation confirmed its usefulness. Furthermore, the developed solution can be easily combined with any other approach to build a recommender system and can be further extended to make up a complete and standalone RS.

Słowa kluczowe

EN

recommendation system; cold-start problem; frequent pattern mining; quality of recommendations

PL

system rekomendacji; problem zimnego startu; eksploracja częstych wzorców; jakość rekomendacji

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 217--226
• Opis fizyczny: Bibliogr. 46 poz., wz., wykr., tab.

Twórcy

autor

Kannout Eyad

• Institute of Informatics, University of Warsaw Banacha 2, Warsaw, Poland

autor

Grodzki Michał

• Institute of Informatics, University of Warsaw Banacha 2, Warsaw, Poland

autor

Grzegorowski Marek

• Institute of Informatics, University of Warsaw Banacha 2, Warsaw, Poland

Bibliografia

• 1. Z. Batmaz, A. Yürekli, A. Bilge, and C. Kaleli, “A review on deep learning for recommender systems: challenges and remedies,” Artif. Intell. Rev., vol. 52, no. 1, pp. 1–37, 2019. http://dx.doi.org/10.1007/s10462-018-9654-y
• 2. M. Kawai, H. Sato, and T. Shiohama, “Topic model-based recommender systems and their applications to cold-start problems,” Expert Systems with Applications, vol. 202, p. 117129, 2022. http://dx.doi.org/10.1016/j.eswa.2022.117129
• 3. M. Pulis and J. Bajada, Siamese Neural Networks for Content-Based Cold-Start Music Recommendation. New York, NY, USA: Association for Computing Machinery, 2021, p. 719723. ISBN 9781450384582
• 4. E. Kannout, “Context clustering-based recommender systems,” in 2020 15th Conference on Computer Science and Information Systems (FedCSIS), 2020. http://dx.doi.org/10.15439/2020F54 pp. 85–91.
• 5. M. Pondel and J. Korczak, “Collective clustering of marketing data - recommendation system upsaily,” in Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018, Poznań, Poland, September 9-12, 2018, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., vol. 15, 2018. http://dx.doi.org/10.15439/2018F217 pp. 801–810.
• 6. B. Yi, X. Shen, H. Liu, Z. Zhang, W. Zhang, S. Liu, and N. Xiong, “Deep Matrix Factorization With Implicit Feedback Embedding for Recommendation System,” IEEE Transactions on Industrial Informatics, vol. 15, no. 8, pp. 4591–4601, 2019. http://dx.doi.org/10.1109/TII.2019.2893714
• 7. M. Jalili, S. Ahmadian, M. Izadi, P. Moradi, and M. Salehi, “Evaluating Collaborative Filtering Recommender Algorithms: A Survey,” IEEE Access, vol. 6, pp. 74 003–74 024, 2018. http://dx.doi.org/10.1109/ACCESS.2018.2883742
• 8. R. Chen, Q. Hua, Y.-S. Chang, B. Wang, L. Zhang, and X. Kong, “A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks,” IEEE Access, vol. 6, pp. 64 301–64 320, 2018. http://dx.doi.org/10.1109/ACCESS.2018.2877208
• 9. B. Walek and V. Fojtik, “A hybrid recommender system for recommending relevant movies using an expert system,” Expert Systems with Applications, vol. 158, p. 113452, 2020. http://dx.doi.org/10.1016/j.eswa.2020.113452
• 10. Y. Pérez-Almaguer, R. Yera, A. A. Alzahrani, and L. Martínez, “Content-based group recommender systems: A general taxonomy and further improvements,” Expert Systems with Applications, vol. 184, p. 115444, 2021. http://dx.doi.org/10.1016/j.eswa.2021.115444
• 11. Y. Afoudi, M. Lazaar, and M. Al Achhab, “Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network,” Simulation Modelling Practice and Theory, vol. 113, p. 102375, 2021. http://dx.doi.org/10.1016/j.simpat.2021.102375
• 12. R. Pasricha and J. McAuley, “Translation-Based Factorization Machines for Sequential Recommendation,” in Proceedings of the 12th ACM Conference on Recommender Systems, ser. RecSys’18. New York, NY, USA: Association for Computing Machinery, 2018. http://dx.doi.org/10.1145/3240323.3240356. ISBN 9781450359016 p. 6371.
• 13. H. Wu, Z. Zhang, K. Yue, B. Zhang, J. He, and L. Sun, “Dualregularized matrix factorization with deep neural networks for recommender systems,” Knowledge-Based Systems, vol. 145, pp. 46–58, 2018. http://dx.doi.org/10.1016/j.knosys.2018.01.003
• 14. F. Liu, R. Tang, X. Li, W. Zhang, Y. Ye, H. Chen, H. Guo, and Y. Zhang, “Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling,” 2018.
• 15. M. K. Najafabadi, A. H. Mohamed, and M. N. Mahrin, “A survey on data mining techniques in recommender systems,” Soft Comput., vol. 23, no. 2, pp. 627–654, 2019. http://dx.doi.org/10.1007/s00500-017-2918-7
• 16. M. Grzegorowski, A. Janusz, S. Lazewski, M. Swiechowski, and M. Jankowska, “Prescriptive analytics for optimization of FMCG delivery plans,” in Information Processing and Management of Uncertainty in Knowledge-Based Systems - 19th International Conference, IPMU 2022, Milan, Italy, July 11-15, 2022, Proceedings, Part II, ser. Communications in Computer and Information Science, D. Ciucci, I. Couso, J. Medina, D. Ślęzak, D. Petturiti, B. Bouchon-Meunier, and R. R. Yager, Eds., vol. 1602. Springer, 2022. http://dx.doi.org/10.1007/978-3-031-08974-9_4 pp. 44–53.
• 17. A. Janusz, M. Grzegorowski, M. Michalak, Ł. Wróbel, M. Sikora, and D. Ślęzak, “Predicting Seismic Events in Coal Mines Based on Underground Sensor Measurements,” Engineering Applications of Artificial Intelligence, vol. 64, pp. 83–94, 2017.
• 18. Z. Zhu, J. Kim, T. Nguyen, A. Fenton, and J. Caverlee, Fairness among New Items in Cold Start Recommender Systems. New York, NY, USA: Association for Computing Machinery, 2021, p. 767776. ISBN 9781450380379
• 19. Z. Cui, X. Xu, F. XUE, X. Cai, Y. Cao, W. Zhang, and J. Chen, “Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios,” IEEE Transactions on Services Computing, vol. 13, no. 4, pp. 685–695, 2020. http://dx.doi.org/10.1109/TSC.2020.2964552
• 20. A. Karpus, I. Vagliano, K. Goczyla, and M. Morisio, “An ontology-based contextual pre-filtering technique for recommender systems,” in Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016, Gdańsk, Poland, September 11-14, 2016, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., vol. 8. IEEE, 2016. http://dx.doi.org/10.15439/2016F428 pp. 411–420.
• 21. S. Natarajan, S. Vairavasundaram, S. Natarajan, and A. H. Gandomi, “Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data,” Expert Systems with Applications, vol. 149, p. 113248, 2020. http://dx.doi.org/10.1016/j.eswa.2020.113248
• 22. A. Karpus, I. Vagliano, and K. Goczyla, “Serendipitous recommendations through ontology-based contextual pre-filtering,” in Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation - 13th International Conference, BDAS 2017, Ustroń, Poland, May 30 - June 2, 2017, Proceedings, ser. Communications in Computer and Information Science, S. Kozielski, D. Mrozek, P. Kasprowski, B. Malysiak-Mrozek, and D. Kostrzewa, Eds., vol. 716, 2017. http://dx.doi.org/10.1007/978-3-319-58274-0_21 pp. 246–259.
• 23. A. Pawlicka, M. Pawlicki, R. Kozik, and R. S. Chora, “A systematic review of recommender systems and their applications in cybersecurity,” Sensors, vol. 21, no. 15, 2021. http://dx.doi.org/10.3390/s21155248. [Online]. Available: https://www.mdpi.com/1424-8220/21/15/5248
• 24. K. Ghazinour, S. Matwin, and M. Sokolova, “Monitoring and recommending privacy settings in social networks,” in Joint 2013 EDBT/ICDT Conferences, EDBT/ICDT ’13, Genoa, Italy, March 22, 2013, Workshop Proceedings, G. Guerrini, Ed. ACM, 2013. doi: 10.1145/2457317.2457344 pp. 164–168.
• 25. A. L. Vizine Pereira and E. R. Hruschka, “Simultaneous co-clustering and learning to address the cold start problem in recommender systems,” Knowledge-Based Systems, vol. 82, pp. 11–19, 2015. http://dx.doi.org/10.1016/j.knosys.2015.02.016
• 26. M. Nilashi, O. Ibrahim, and K. Bagherifard, “A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques,” Expert Systems with Applications, vol. 92, pp. 507–520, 2018. http://dx.doi.org/10.1016/j.eswa.2017.09.058
• 27. M. Grzegorowski, A. Janusz, D. Ślęzak, and M. S. Szczuka, “On the role of feature space granulation in feature selection processes,” in 2017 IEEE International Conference on Big Data (IEEE BigData 2017), Boston, MA, USA, December 11-14, 2017, J. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, and M. Toyoda, Eds. IEEE Computer Society, 2017. http://dx.doi.org/10.1109/BigData.2017.8258124 pp. 1806–1815.
• 28. M. Grzegorowski and D. Ślęzak, “On resilient feature selection: Computational foundations of r-C-reducts,” Inf. Sci., vol. 499, pp. 25–44, 2019. http://dx.doi.org/10.1016/j.ins.2019.05.041
• 29. M. Grzegorowski, “Governance of the Redundancy in the Feature Selection Based on Rough Sets’ Reducts,” in Rough Sets - International Joint Conference, IJCRS 2016, Santiago de Chile, Chile, October 7-11, 2016, Proceedings, ser. Lecture Notes in Computer Science, V. Flores, F. A. C. Gomide, A. Janusz, C. Meneses, D. Miao, G. Peters, D. Ślęzak, G. Wang, R. Weber, and Y. Yao, Eds., vol. 9920, 2016. http://dx.doi.org/10.1007/978-3-319-47160-0_50 pp. 548–557. [Online]. Available: https://doi.org/10.1007/978-3-319-47160-0_50
• 30. M. asid and R. Ali, Use of Soft Computing Techniques for Recommender Systems: An Overview. Singapore: Springer Singapore, 2017, pp. 61–80. ISBN 978-981-10-7098-3
• 31. I. Viktoratos, A. Tsadiras, and N. Bassiliades, “Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems,” Expert Systems with Applications, vol. 101, pp. 78–90, 2018. http://dx.doi.org/doi.org/10.1016/j.eswa.2018.01.044
• 32. H. Steck, “Collaborative filtering via high-dimensional regression,” CoRR, vol. abs/1904.13033, 2019. http://dx.doi.org/10.48550/ARXIV.1904.13033
• 33. H. Kwasnicka and T. Ovedenski, “Pix2trips - a system supporting small groups of urban tourists,” in Proceedings of the 16th Conference on Computer Science and Intelligence Systems, Online, September 2-5, 2021, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, M. Paprzycki, and D. Ślęzak, Eds., vol. 25, 2021. http://dx.doi.org/10.15439/2021F130 pp. 141–145.
• 34. H. Sobhanam and A. K. Mariappan, “Addressing cold start problem in recommender systems using association rules and clustering technique,” in 2013 International Conference on Computer Communication and Informatics, 2013. http://dx.doi.org/10.1109/ICCCI.2013.6466121 pp. 1–5.
• 35. W. Feng, Q. Zhu, J. Zhuang, and S. Yu, “An expert recommendation algorithm based on pearson correlation coefficient and fp-growth,” Clust. Comput., vol. 22, no. Supplement, pp. 7401–7412, 2019. http://dx.doi.org/10.1007/s10586-017-1576-y
• 36. J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, ser. UAI’98. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1998. ISBN 155860555X p. 4352.
• 37. M. D. Ekstrand, J. T. Riedl, and J. A. Konstan, “Collaborative filtering recommender systems,” Found. Trends Hum.-Comput. Interact., vol. 4, no. 2, p. 81173, feb 2011. http://dx.doi.org/10.1561/1100000009. [Online]. Available: https://doi.org/10.1561/1100000009
• 38. J. Han, M. Kamber, and J. Pei, “6 - mining frequent patterns, associations, and correlations: Basic concepts and methods,” in Data Mining (Third Edition), third edition ed., ser. The Morgan Kaufmann Series in Data Management Systems, J. Han, M. Kamber, and J. Pei, Eds. Boston: Morgan Kaufmann, 2012, pp. 243–278. ISBN 978-0-12-381479-1. [Online]. Available: https://www.sciencedirect.com/science/article/pii/B978012381479100006X
• 39. J. Hipp, U. Güntzer, and G. Nakhaeizadeh, “Algorithms for association rule mining a general survey and comparison,” SIGKDD Explor. Newsl., vol. 2, no. 1, p. 5864, jun 2000. http://dx.doi.org/10.1145/360402.360421. [Online]. Available: https://doi.org/10.1145/360402.360421
• 40. U. Fayyad, Knowledge Discovery in Databases: An Overview. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001, pp. 28–47. ISBN 978-3-662-04599-2. [Online]. Available: https://doi.org/10.1007/978-3-662-04599-2_2
• 41. R. Agrawal and R. Srikant, “Fast algorithms for mining association rules in large databases,” in Proceedings of the 20th International Conference on Very Large Data Bases, ser. VLDB ’94. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1994. ISBN 1558601538 p. 487499.
• 42. J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” SIGMOD Rec., vol. 29, no. 2, p. 112, may 2000. http://dx.doi.org/10.1145/335191.335372. [Online]. Available: https://doi.org/10.1145/335191.335372
• 43. T. Silveira, M. Zhang, X. Lin, Y. Liu, and S. Ma, “How good your recommender system is? A survey on evaluations in recommendation,” International Journal of Machine Learning and Cybernetics, vol. 10, pp. 813–831, 2019. http://dx.doi.org/10.1007/s13042-017-0762-9
• 44. M. Grzegorowski, “Massively Parallel Feature Extraction Framework Application in Predicting Dangerous Seismic Events,” in Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016, Gdańsk, Poland, September 11-14, 2016, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., vol. 8. IEEE, 2016. http://dx.doi.org/10.15439/2016F90 pp. 225–229. [Online]. Available: https://doi.org/10.15439/2016F90
• 45. E. Zdravevski, P. Lameski, R. Mingov, A. Kulakov, and D. Gjorgjevikj, “Robust histogram-based feature engineering of time series data,” in 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódz, Poland, September 13-16, 2015, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., vol. 5. IEEE, 2015. http://dx.doi.org/10.15439/2015F420 pp. 381–388. [Online]. Available: https://doi.org/10.15439/2015F420
• 46. M. Grzegorowski and S. Stawicki, “Window-based feature extraction framework for multi-sensor data: A posture recognition case study,” in 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódz, Poland, September 13-16, 2015, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., vol. 5. IEEE, 2015. http://dx.doi.org/10.15439/2015F425 pp. 397–405.

Uwagi

1. Research co-funded by Polish National Science Centre (NCN) grant no. 2018/31/N/ST6/00610.

2. Track 4: 1st Workshop on Personalization and Recommender Systems

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).