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 [1].
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. |