https://github.com/reczoo/critical-review-papers

A awesome list of critical review papers that criticize existing work
https://github.com/reczoo/critical-review-papers

critical-review recommender-systems

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A awesome list of critical review papers that criticize existing work

• Host: GitHub
• URL: https://github.com/reczoo/critical-review-papers
• Owner: reczoo
• Created: 2020-09-30T02:24:24.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-12-26T05:03:58.000Z (almost 5 years ago)
• Last Synced: 2023-11-30T06:24:49.331Z (almost 2 years ago)
• Topics: critical-review, recommender-systems
• Homepage:
• Size: 10.7 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Open-Critical-Reviews

## Recommendation

+ [**Arxiv'2020**] [FuxiCTR: An Open Benchmark for Click-Through Rate Prediction](https://arxiv.org/abs/2009.05794), by Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, Xiuqiang He.

  > Argument: Many new models for CTR prediction do not perform as expected compared to some classic ones.

+ [**RecSys'2020**] [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://arxiv.org/abs/2005.09683), by Steffen Rendle, Walid Krichene, Li Zhang, John Anderson.

  > Argument: MLP in NCF does not outperform inner products in MF as reported.

+ [**Arxiv'2020**] [Empirical Analysis of Session-Based Recommendation Algorithms](https://arxiv.org/abs/1910.12781), by Malte Ludewig, Noemi Mauro, Sara Latifi, Dietmar Jannach.

  > Argument: The progress in terms of prediction accuracy that is achieved with neural methods is still limited. In most cases, our experiments show that simple heuristic methods based on nearest-neighbors schemes are preferable.

+ [**Arxiv'2020**] [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research](https://arxiv.org/abs/1911.07698), by Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, Dietmar Jannach.

+ [**CIKM'2020**] [Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems](https://arxiv.org/abs/2007.11893), by Maurizio Ferrari Dacrema, Federico Parroni, Paolo Cremonesi, Dietmar Jannach.

  > Argument: Convolutions over user-item embedding maps do not outperform traditional baselines as reported.

+ [**SIGIR'2020**] [How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements](https://arxiv.org/abs/2005.12210), by Noveen Sachdeva, Julian McAuley.

  > Argument: Whether reviews are helpful for recommendation is questionable. Some state-of-the-art methods fail to outperform existing baselines.

+ [**RecSys'2019**] [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://arxiv.org/abs/1907.06902), by Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach.

  > Argument: Many deep models, including CMN, MCRec, CVAE, CDL, NCF and MVAE, do not perform better than simple baselines such as MostPopular, KNN baselines and SLIM.

## NLP

+ [**AAAI'2020**] A Critique of the Smooth Inverse Frequency Sentence Embeddings

+ [**ACL'2018**] Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

+ [**ICLR'2017**] A Simple but Tough-to-Beat Baseline for Sentence Embeddings

## CV

+ [**Arxiv'2020**] A Metric Learning Reality Check

+ [**ECCV'2020**] GDumb: A Simple Approach that Questions Our Progress in Continual Learning