The Effect of random seeds for data splitting on recommendation accuracy [Reproduced]

This study replicates and extends the research paper "The Effect of random seeds for data splitting on recommendation accuracy" which analysed the impact of different random seeds used for data splitting and algorithms and found a significant variance between the resulting performance. This would indicate a need for multiple runs with different random seeds and averaging the results to get a more trustworthy picture when presenting new algorithms and results. We extend their efforts by including two more algorithms and datasets to substantiate their claims. We compare the results with the original work and find similar accuracy variances for the new algorithms tested.