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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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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DOI: https://doi.org/10.1007/978-3-030-94763-7_19
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