Block based Singular Value Decomposition approach to matrix factorization for recommender systems
This work addresses scalability issues for recommender systems, but it is incremental as it extends existing SVD techniques with parallelism.
The paper tackles scalability and performance challenges in recommender systems by proposing a block-based matrix factorization approach paired with Singular Value Decomposition, enabling parallelism through multi-threading, GPUs, and distributed computation, which demonstrated advantages in performance and memory usage.
With the abundance of data in recent years, interesting challenges are posed in the area of recommender systems. Producing high quality recommendations with scalability and performance is the need of the hour. Singular Value Decomposition(SVD) based recommendation algorithms have been leveraged to produce better results. In this paper, we extend the SVD technique further for scalability and performance in the context of 1) multi-threading 2) multiple computational units (with the use of Graphical Processing Units) and 3) distributed computation. We propose block based matrix factorization (BMF) paired with SVD. This enabled us to take advantage of SVD over basic matrix factorization(MF) while taking advantage of parallelism and scalability through BMF. We used Compute Unified Device Architecture (CUDA) platform and related hardware for leveraging Graphical Processing Unit (GPU) along with block based SVD to demonstrate the advantages in terms of performance and memory.