Abstract
The recommender systems have been widely applied in numerous applications that support online retailers, video sharing websites, medical systems, etc. Similar measures are essential in providing valuable recommendations to users in such systems. This work presents a novel approach, namely Ball-Sim, with a new similarity metric using a balltree structure for recommender systems. Furthermore, we want to leverage the tree structure to determine the closest k nearby users to improve the recommender systems’ efficiency. The work’s experimental scenarios outlined the steps of building a balltree and identifying nearby users based on the tree structure. Besides, the work also evaluates the implemented recommender system by comparing the recommender system’s results based on the balltree-based spatial partitioning with the recommender system using the default parameters. The data used in the experiments is the Movielens dataset, a web-based film recommender system, and an important data source for evaluating the studies, with 100,000 samples, including ratings from 943 users for 1,664 movies. The results show that the recommender system with a balltree-based similarity metric can improve the accuracy compared to a commonly-used measure such as the cosine metric.
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References
Aggarwal, C.C.: Knowledge-based recommender system. In: Recommender Systems, pp. 15–19. Springer, Heidelberg (2016)
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 191–226. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_6
Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., Stettinger, M.: Basic approaches in recommender systems. In: Robillard, M., Maalej, W., Walker, R., Zimmermann, T. (eds.) Recommendation Systems in Software Engineering, pp. 15–37. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-45135-5_2
Bauman, K., Tuzhilin, A.: Location-based recommender systems. In: Encyclopedia of GIS, pp. 43–92. Springer, Heidelberg (2017)
Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems, Foundations and Trends in Human-Computer Interaction, SIR Ranking of United States, pp. 1–94 (2011)
Aggarwal, C.: Recommender Systems: The Textbook. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-31
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommender systems: principles, methods, and evaluation. Egypt. Inform. J. 16, 261–273 (2015)
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, 734–749 (2005)
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). ISSN 1046–8188
Felfernig, A., Teppan, E., Gula, B.: Knowledge-based recommender technologies for marketing and sales. Int. J. Pattern Recognit. Artif. Intell. 21(02), 333–354 (2007)
Burke, R.: Knowledge-based recommender systems. Encycl. Libr. Inf. Syst. 69, 175–186 (2000)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)
Dolatshah, M., Hadian, A., Minaei-Bidgoli, B.: Ball*-tree: efficient spatial indexing for constrained nearest-neighbor search in metric spaces. Iran University of Science and Technology (2015)
Hua, J., Lianga, J., Kuang, Y., Honavar, V.: A user similarity-based top-N recommendation approach for mobile in-application advertising. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China (2018)
Singh, R.H., Maurya, S., Tripathi, T., Narula, T., Srivastav, G.: Movie recommendation system using cosine similarity and KNN. Int. J. Eng. Adv. Technol. (IJEAT) 9(5), 556–559 (2020). ISSN 2249-8958
Gupta, M., Thakkar, A., Aashish, Gupta, V., Rathore, D.P.S.: Movie recommender system using collaborative filtering. In: 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, pp. 415–420 (2020). https://doi.org/10.1109/ICESC48915.2020.9155879
Periyasamy, K., Jaiganesh, J., Ponnambalam, K., Rajasekar, J., Arputharaj, K.: Soft cosine gradient and gaussian mixture joint probability recommender system for online social networks. Analysis and performance evaluation of cosine neighbourhood recommender system. Int. Arab J. Inf. Technol. (IAJIT) 14(5), 747–754 (2017)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. JMLR 12, 2825–2830 (2011)
Omohundro, S.M.: Five balltree construction algorithms. ICSI Technical Report TR-89-063 (1989)
Acknowledgment
We want to express our great appreciation to Dr. Nghia Trung Duong, Can Tho University of Technology, and Dr. Lan Phuong Phan, Can Tho University, for their valuable and constructive suggestions during the planning development of this research work.
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Huynh, H.X., Mai, N.C.T., Nguyen, H.T. (2023). Balltree Similarity: A Novel Space Partition Approach for Collaborative Recommender Systems. In: Phan, C.V., Nguyen, T.D. (eds) Context-Aware Systems and Applications. ICCASA 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-031-28816-6_9
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