The Effect of Third Party Implementations on Reproducibility

Code

• github.com/hidasib/GRU4Rec
  OfficialIn papertf★ 801
• github.com/paxcema/KerasGRU4Rec
  OfficialIn papertf★ 106
• github.com/hidasib/gru4rec_pytorch_official
  OfficialIn paperpytorch★ 75
• github.com/hidasib/gru4rec_tensorflow_official
  OfficialIn papertf★ 6
• github.com/pcerdam/KerasGRU4Rec
  tf★ 106

Abstract

Reproducibility of recommender systems research has come under scrutiny during recent years. Along with works focusing on repeating experiments with certain algorithms, the research community has also started discussing various aspects of evaluation and how these affect reproducibility. We add a novel angle to this discussion by examining how unofficial third-party implementations could benefit or hinder reproducibility. Besides giving a general overview, we thoroughly examine six third-party implementations of a popular recommender algorithm and compare them to the official version on five public datasets. In the light of our alarming findings we aim to draw the attention of the research community to this neglected aspect of reproducibility.

Tasks

Reproductions