The Effect of Third Party Implementations on Reproducibility
Abstract
Domain fit: Niche / domain-specific · No strong AI-core implementation/artifact signals were detected from current providers.
Reproducibility of recommender systems research has come under scrutiny\nduring recent years. Along with works focusing on repeating experiments with\ncertain algorithms, the research community has also started discussing various\naspects of evaluation and how these affect reproducibility. We add a novel\nangle to this discussion by examining how unofficial third-party\nimplementations could benefit or hinder reproducibility. Besides giving a\ngeneral overview, we thoroughly examine six third-party implementations of a\npopular recommender algorithm and compare them to the official version on five\npublic datasets. In the light of our alarming findings we aim to draw the\nattention of the research community to this neglected aspect of\nreproducibility.\n
Results and benchmarks
Reproducibility of recommender systems research has come under scrutiny\nduring recent years.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
Maintained implementation evidence is not confirmed for this paper yet.
Use the implementation status and reproduction sections for the current action plan.
No verified maintained repo yet
There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.
- No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
- Start from related paper: The reproducibility of reported height and body weight in repeated questionnaire surveys..
- Track assumptions and missing details in an experiment log before coding.
Time to first repro: a few days
Recommendation evidence is currently too limited for a maintained-repo choice. Use Implementation Status and Reproduction Path for a practical baseline plan.
- Estimate is based on paper-only reproduction flow
Reproduction readiness
No repo
No verified implementation available
- No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.
Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet. Use targeted searches to quickly locate candidate models, datasets, and demos.
Datasets
Spaces
Tip: start with models, then check datasets and spaces if you need evaluation data or demos.
Research context
20
Citations
43
References
Tasks
Implementation, Reproducibility, Scrutiny, Computer science, Recommender system, Data science, Information Systems
Methods
Information retrieval
Domains
None detected
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