On Symmetric and Asymmetric LSHs for Inner Product Search
Authors: Behnam Neyshabur, Nathan Srebro
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also compared the hash functions empirically, following the exact same protocol as Shrivastava and Li (2014a), using two collaborative filtering datasets, Netflix and Movielens 10M. |
| Researcher Affiliation | Academia | Toyota Technological Institute at Chicago, Chicago, IL 60637, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is openly available. |
| Open Datasets | Yes | We also compared the hash functions empirically, following the exact same protocol as Shrivastava and Li (2014a), using two collaborative filtering datasets, Netflix and Movielens 10M. |
| Dataset Splits | No | The paper describes how the data is used for queries and database points in the collaborative filtering setup, but does not provide specific train/validation/test dataset splits (e.g., percentages or sample counts) for the overall datasets. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For L2-ALSH(SL) we used m = 3, U = 0.83, r = 2.5, suggested by the authors and used in their empirical evaluation. For SIGN-ALSH(SL) we used two different settings of the parameters suggested by Shrivastava and Li (2014b): m = 2, U = 0.75 and m = 3, U = 0.85. SIMPLE-LSH does not require any parameters. |