# Weightless Neural Networks for Continuously Trainable Personalized Recommendation Systems

arXiv: https://arxiv.org/abs/2511.05499 · HTML: https://mlsanity.com/paper/2511.05499/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 1 of 1)

- [Monolith: Real Time Recommendation System With Collisionless Embedding Table](https://mlsanity.com/paper/2209.07663/) (2022)

## Abstract (arXiv)

Given that conventional recommenders, while deeply effective, rely on large distributed systems pre-trained on aggregate user data, incorporating new data necessitates large training cycles, making them slow to adapt to real-time user feedback and often lacking transparency in recommendation rationale. We explore the performance of smaller personal models trained on per-user data using weightless neural networks (WNNs), an alternative to neural backpropagation that enable continuous learning by using neural networks as a state machine rather than a system with pretrained weights. We contrast our approach against a classic weighted system, also on a per-user level, and standard collaborative filtering, achieving competitive levels of accuracy on a subset of the MovieLens dataset. We close with a discussion of how weightless systems can be developed to augment centralized systems to achieve higher subjective accuracy through recommenders more directly tunable by end-users.