Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Revisiting Injective Attacks on Recommender Systems
Authors: Haoyang LI, Shimin DI, Lei Chen
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed attacker. |
| Researcher Affiliation | Academia | 1Computer Science and Engineering, HKUST, Hong Kong, China 2Data Science and Analytics, HKUST(GZ), Guangzhou, China |
| Pseudocode | Yes | Thus, we propose a gradient-based greedy algorithm to maximize the objective in Alg. 1 in Appx. A.3. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | Yes | We evaluate our attacker on widely used three real-world datasets: Movie Lens-100K (ML-100K) [14], Movie Lens-1M (ML-1M) [14], and Gowalla [5]. |
| Dataset Splits | No | The paper discusses training of the target RecSys and the attacker, and mentions hyperparameter settings, but does not explicitly provide specific training/validation/test dataset splits with percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not explicitly mention specific hardware details such as GPU or CPU models used for the experiments within its main text or appendices. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names like PyTorch with a version number). |
| Experiment Setup | Yes | For default, we set β1 = 0.01 for all datasets. For unnoticeable, we set the default to be similar to the average number of interacted items of real users as shown in Tab. 4, i.e., β2 is {0.05, 0.05, 0.002} for ML-100K, ML-1M, and Gowalla, respectively. The details about hyper-parameter settings are listed in Appx. B.2. |