On the effect of data sparsity to the performance of a Collaborative Filtering algorithm on a GPU

Web proceedings papers

Authors

Efthalia Karydi , Konstantinos Margaritis and Eero Vainikko

Abstract

One of the problems that are encountered in recommender systems applications is the high sparsity of the available data. In this paper we investigate the eect of the sparsity of datasets to the performance of a parallel implementation of the Collaborative Filtering Slope One algorithm. To represent the sparse data the Compressed Sparse Row (CSR) format is used and the implementation's performance is evaluated on a Graphics Processing Unit using the MovieLens and articially created datasets.

Keywords

Collaborative Filtering, Slope One, CSR Format, Massively Parallel Computing, GPU, CUDA