Improving Re-rankCCP with rules quality measures

• Autorzy: Jezusek Piotr , Karpus Aleksandra

Identyfikatory

DOI

10.15439/2022F127

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

Abstrakty

EN

Recommender Systems are software tools and techniques which aim at suggesting new items that may possibly be of interest to a user. Context-Aware Recommender Systems exploit contextual information to provide more adequate recommendations. In this paper we described a modification of an existing contextual post-filtering algorithm which uses rules-like user representation called Contextual Conditional Preferences. We extended the algorithm by taking into account rules quality measures while recommending items to a user. We proved that this modification increases the quality of recommendations, measured with precision, recall and nDCG, and has no impact on the execution time of the original algorithm.

Słowa kluczowe

EN

computer science; aggregate; filtering algorithm; time measurement; software tool; recommender system

PL

informatyka; agregat; algorytm filtrowania; pomiar czasu; narzędzia programowe; system rekomendacji

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 61--71
• Opis fizyczny: Bibliogr. 23 poz., tab., wz.

Twórcy

autor

Jezusek Piotr

• Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland

autor

Karpus Aleksandra

• Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland

Bibliografia

• 1. D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich, Recommender Systems: An Introduction, 1st ed. New York, NY, USA: Cambridge University Press, 2010.
• 2. G. Adomavicius and A. Tuzhilin, in Handbook on Recommender Systems, S. B. Ricci F., Rokach L. and K. P. B., Eds. Springer, 2011, ch. Context-Aware Recommender Systems, pp. 217–256.
• 3. M. Kristoffersen, S. Shepstone, and Z.-H. Tan, “The importance of context when recommending tv content: Dataset and algorithms,” IEEE Transactions on Multimedia, vol. 22, no. 6, pp. 1531–1541, 2020.
• 4. A. Karpus, T. di Noia, and K. Goczyla, “Top k recommendations using contextual conditional preferences model,” in Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, Prague, Czech Republic, September 3-6, 2017., M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., 2017, pp. 19–28. [Online]. Available: https://doi.org/10.15439/2017F258
• 5. L. Baltrunas and X. Amatriain, “Towards time-dependant recommendation based on implicit feedback,” in Proceedings of 1st Workshop on Context-Aware Recommender Systems, 2009.
• 6. A. Karpus, I. Vagliano, and K. Goczyła, “Serendipitous recommendations through ontology-based contextual pre-filtering,” Communications in Computer and Information Science, vol. 716, pp. 246–259, 2017.
• 7. Z. V. Ferdousi, D. Colazzo, and E. Negre, “Correlation-based prefiltering for context-aware recommendation,” in 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), March 2018, pp. 89–94.
• 8. Z. V. Ferdousi, D. Colazzo, and E. Negre, “Cbpf: Leveraging context and content information for better recommendations,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11323 LNAI, pp. 381–391, 2018.
• 9. Z. Bahramian, R. Abbaspour, and C. Claramunt, “A context-aware tourism recommender system based on a spreading activation method,” K. F. Samadzadegan F., Ed., vol. 42, no. 4W4. International Society for Photogrammetry and Remote Sensing, 2017, pp. 333–339.
• 10. E. Negre, F. Ravat, and O. Teste, “Olap queries context-aware recommender system,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11030 LNCS, pp. 127–137, 2018.
• 11. M. Iqbal, M. Ghazanfar, A. Sattar, M. Maqsood, S. Khan, I. Mehmood, and S. Baik, “Kernel context recommender system (kcr): A scalable context-aware recommender system algorithm,” IEEE Access, vol. 7, pp. 24 719–24 737, 2019.
• 12. Y. Zheng, S. Shekhar, A. A. Jose, and S. K. Rai, “Integrating contextawareness and multi-criteria decision making in educational learning,” in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, ser. SAC ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 2453–2460.
• 13. J. Manotumruksa, “Deep collaborative filtering approaches for context-aware venue recommendation,” in Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, ser. SIGIR ’17. New York, NY, USA: Association for Computing Machinery, 2017, p. 1383. [Online]. Available: https://doi.org/10.1145/3077136.3084159
• 14. K. Kala and M. Nandhini, “Gated recurrent unit architecture for context-aware recommendations with improved similarity measures,” KSII Transactions on Internet and Information Systems, vol. 14, no. 2, pp. 538–561, 2020.
• 15. M. Hildebrandt, S. Sunder, S. Mogoreanu, M. Joblin, A. Mehta, I. Thon, and V. Tresp, “A recommender system for complex real-world applications with nonlinear dependencies and knowledge graph context,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11503 LNCS, pp. 179–193, 2019.
• 16. A. Karpus, T. di Noia, P. Tomeo, and K. Goczyla, “Rating prediction with contextual conditional preferences,” in Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 1: KDIR, Porto - Portugal, November 9 - 11, 2016, A. L. N. Fred, J. L. G. Dietz, D. Aveiro, K. Liu, J. Bernardino, and J. Filipe, Eds. SciTePress, 2016, pp. 419–424. [Online]. Available: http://dx.doi.org/10.5220/0006083904190424
• 17. A. Karpus, “Context-aware user modelling and generation of recommendations in recommender systems,” Ph.D. dissertation, Gdańsk University of Technology, 2018.
• 18. J. Cendrowska, “PRISM: an algorithm for inducing modular rules,” International Journal of Man-Machine Studies, vol. 27, no. 4, pp. 349–370, 1987.
• 19. J. M. Luna, M. Ondra, H. M. Fardoun, and S. Ventura, “Optimization of quality measures in association rule mining: an empirical study,” International Journal of Computational Intelligence Systems, vol. 12, pp. 59–78, 2018. [Online]. Available: https://doi.org/10.2991/ijcis.2018.25905182
• 20. A. Kosir, A. Odic, M. Kunaver, M. Tkalcic, and J. F. Tasic, “Database for contextual personalization,” Elektrotehniski vestnik [English print ed.], vol. 78, no. 5, pp. 270–274, 2011.
• 21. S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “Bpr: Bayesian personalized ranking from implicit feedback,” in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, ser. UAI ’09. Arlington, Virginia, United States: AUAI Press, 2009, pp. 452–461. [Online]. Available: http://dl.acm.org/citation.cfm?id=1795114.1795167
• 22. J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl, “Grouplens: Applying collaborative filtering to usenet news,” Commun. ACM, vol. 40, no. 3, pp. 77–87, Mar. 1997. [Online]. Available: http://doi.acm.org/10.1145/245108.245126
• 23. G. Shani and A. Gunawardana, “Handbook on recommender systems,” S. B. K. P. B. Ricci F., Rokach L., Ed. Springer, 2011, ch. Evaluating Recommendation Systems, pp. 257–298.

Uwagi

1. Track 1: 17th International Symposium on Advanced Artificial Intelligence in Applications

2. 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).