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..
Infinite Recommendation Networks: A Data-Centric Approach
Authors: Noveen Sachdeva, Mehak Dhaliwal, Carole-Jean Wu, Julian Mcauley
NeurIPS 2022 | Venue PDF | 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 EMAIL EMAIL |
| 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. |