Infinite Recommendation Networks: A Data-Centric Approach
Authors: Noveen Sachdeva, Mehak Dhaliwal, Carole-Jean Wu, Julian Mcauley
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both of our proposed approaches significantly outperform their respective state-of-the-art and when used together, we observe 96-105% of -AE’s performance on the full dataset with as little as 0.1% of the original dataset size, leading us to explore the counter-intuitive question: Is more data what you need for better recommendation? 5 Experiments |
| Researcher Affiliation | Collaboration | Noveen Sachdeva Mehak Preet Dhaliwal Carole-Jean Wu Julian Mc Auley University of California, San Diego Meta AI {nosachde,mdhaliwal,jmcauley}@ucsd.edu carolejeanwu@meta.com |
| Pseudocode | Yes | We also provide -AE’s training and inference pseudo-codes in Appendix A, Algorithms 1 and 2. We also provide DISTILL-CF’s pseudo-code in Appendix A, Algorithm 3. |
| Open Source Code | Yes | Our implementation for -AE is available at https://github.com/noveens/infinite_ae_cf Our implementation for DISTILL-CF is available at https://github.com/noveens/distill_cf |
| Open Datasets | Yes | We use four recommendation datasets with varying sizes and sparsity characteristics. A brief set of data statistics can be found in Appendix B.3, Table 2. For each user in the dataset, we randomly split their interaction history into 80/10/10% train-test-validation splits. Following recent warnings against unrealistic dense preprocessing of recommender system datasets [55, 57], we only prune users that have fewer than 3 interactions to enforce at least one interaction per user in the train, test, and validation sets. No such preprocessing is followed for items. |
| Dataset Splits | Yes | For each user in the dataset, we randomly split their interaction history into 80/10/10% train-test-validation splits. |
| Hardware Specification | Yes | All of our experiments are performed on a single RTX 3090 GPU, with a random-seed initialization of 42. |
| Software Dependencies | No | We implement both -AE and DISTILL-CF using JAX [8] along with the Neural Tangents package [44] for the relevant NTK computations. While JAX and Neural Tangents are mentioned, specific version numbers for these software dependencies are not provided. |
| Experiment Setup | Yes | To ensure a fair comparison, we conduct a hyper-parameter search for all competitors on the validation set. More details on the hyper-parameters for -AE, DISTILL-CF, and all competitors can be found in Appendix B.3, Table 3. |