Abstract
Recommendation systems based on collaborative filtering are widely used in many fields. Alternating Least Squares (ALS) in the Mlib Library is a distributed and parallel algorithm in Spark framework, which can solve the problems of scalability and speedup in a limited hardware resources of stand-alone systems. However, it does not consider the influence of the factor of time on the recommendation accuracy. Taking restaurant ratings as an example, this month ratings are more reliable than those from last year. Thus, the motivation in our proposal re-scores ratings with different time weights. We improve ALS in its process of data preparation according to requirements on the structure of data input. Consequently, our improvement does not need to modify the main body of ALS. Experimental results validate effectiveness that our proposal outperforms the original ALS in recommendation accuracy.
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References
Yang, X., Guo, Y., Liu, Y., Steck, H.: A survey of collaborative filtering based social recommender systems. Comput. Commun. 41, 1–10 (2014). https://doi.org/10.1016/j.comcom.2013.06.009
Karydi, E., Margaritis, K.: Parallel and distributed collaborative filtering: a survey. ACM Comput. Surv. (CSUR) 49(2), 37 (2016)
Gousios, G.: Big data software analytics with apache spark. In: Proceedings of the 40th International Conference on Software Engineering: Companion, pp. 542–543. Association for Computing Machinery, New York (2018)
Meng, X., et al.: MLlib: machine learning in apache spark. J. Mach. Learn. Res. 17(1), 1235–1241 (2016)
Gao, F., Bhowmick, C., Liu, J.: Performance analysis using apriori algorithm along with spark and python. In: Proceedings of the 2018 International Conference on Computing and Big Data, pp. 28–31. ACM (2018)
Winlaw, M., Hynes, M.B., Caterini, A., De Sterck, H.: Algorithmic acceleration of parallel ALS for collaborative filtering: speeding up distributed big data recommendation in spark. In: 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS), pp. 682–691. IEEE (2015)
Kupisz, B., Unold, O.: Collaborative filtering recommendation algorithm based on hadoop and spark. In: 2015 IEEE International Conference on Industrial Technology (ICIT), pp. 1510–1514. IEEE (2015)
Verma, A., Kumar, D.: Evaluating and enhancing efficiency of recommendation system using big data analytics (2017)
Panigrahi, S., Lenka, R.K., Stitipragyan, A.: A hybrid distributed collaborative filtering recommender engine using apache spark. Procedia Comput. Sci. 83, 1000–1006 (2016)
Xie, L., Zhou, W., Li, Y.: Application of improved recommendation system based on spark platform in big data analysis. Cybern. Inf. Technol. 16(6), 245–255 (2016)
Lenka, R.K., Barik, R.K., Panigrahi, S., Panda, S.S.: An improved hybrid distributed collaborative filtering model for recommender engine using apache spark. Int. J. Intell. Syst. Appl. 10(7), 74 (2018)
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19 (2016)
Zhang, P., Zhang, Z., Tian, T., Wang, Y.: Collaborative filtering recommendation algorithm integrating time windows and rating predictions. Appl. Intell. 49(8), 3146–3157 (2019). https://doi.org/10.1007/s10489-019-01443-2
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 61672038 and 61702006, in part by the Major Technologies R&D Special Program of Anhui under Grant 16030901060. And in part, the Program for Synergy Innovation in the Anhui Higher Education Institutions of China (Grant No. GXXT-2020-012).
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Li, Q. et al. (2022). Time-Based Distributed Collaborative Filtering Recommendation Algorithm. In: Calafate, C.T., Chen, X., Wu, Y. (eds) Mobile Networks and Management. MONAMI 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-94763-7_19
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