Abstract
Collaborative Filtering (CF) is an important building block of recommendation systems. Alternating Least Squares (ALS) is the most popular algorithm used in CF models to calculate the latent factor matrix factorization. Parallel ALS on Hadoop is widely used in the era of big data. However, existing work on the computational efficiency of parallel ALS on Hadoop have two defects. One is the imbalance of data distribution, the other is lacking the fine-grained parallel processing on the rating data. Aiming on these issues, we propose an integrated optimized solution. The solution first optimizes the rating data partition with the consideration of both the number of involved data records and the partitioned data size. Then, the multithread-based fine-grained parallelism is introduced to process rating data records within a map task concurrently. Experimental results demonstrate that our solution can reduce the overall runtime of Hadoop ALS by 82.17% by maximum.
Supported by 2019 BenchCouncil AI System and Algorithm Challenge.
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Liang, Y., Zeng, S., Liang, Y., Chen, K. (2020). Accelerating Parallel ALS for Collaborative Filtering on Hadoop. In: Gao, W., Zhan, J., Fox, G., Lu, X., Stanzione, D. (eds) Benchmarking, Measuring, and Optimizing. Bench 2019. Lecture Notes in Computer Science(), vol 12093. Springer, Cham. https://doi.org/10.1007/978-3-030-49556-5_13
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