Infinite Recommendation Networks (∞-AE)

This repository contains the implementation of ∞-AE from the paper “Infinite Recommendation Networks: A Data-Centric Approach” [arXiv] where we leverage the NTK of an infinitely-wide autoencoder for implicit-feedback recommendation. Notably, ∞-AE:

  • Is easy to implement (<50 lines of relevant code)
  • Has a closed-form solution
  • Has only a single hyper-parameter, $\lambda$
  • Even though simplistic, outperforms all complicated SoTA models

The paper also proposes Distill-CF: how to use ∞-AE for data distillation to create terse, high-fidelity, and synthetic data summaries for model training. We provide Distill-CF’s code in a separate GitHub repository.

If you find any module of this repository helpful for your own research, please consider citing the below under-review paper. Thanks!

  title={Infinite Recommendation Networks: A Data-Centric Approach},
  author={Sachdeva, Noveen and Dhaliwal, Mehak Preet and Wu, Carole-Jean and McAuley, Julian},
  journal={arXiv preprint arXiv:2206.02626},

Code Author: Noveen Sachdeva ([email protected])


Environment Setup

pip install -r requirements.txt

Data Setup

Once you’ve correctly setup the python environment, the following script will download the ML-1M dataset and preprocess it for usage:


How to train ∞-AE?

  • Edit the file which lists all config parameters of ∞-AE.
  • Finally, type the following command to train and evaluate ∞-AE:

Results sneak-peak

Below are the nDCG@10 results for the datasets used in the paper:

Dataset PopRec MF NeuMF MVAE LightGCN EASE ∞-AE
Amazon Magazine 8.42 13.1 13.6 12.18 22.57 22.84 23.06
MovieLens-1M 13.84 25.65 24.44 22.14 28.85 29.88 32.82
Douban 11.63 13.21 13.33 16.17 16.68 19.48 24.94
Netflix 12.34 12.04 11.48 20.85 Timed out 26.83 30.59*

Note: ∞-AE’s results on the Netflix dataset (marked with a *) are obtained by training only on 5% of the total users. Note however, all other methods are trained on the full dataset.

MIT License


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