Weightless Neural Networks for Continuously Trainable Personalized Recommendation Systems

TL;DR

This paper investigates weightless neural networks for personalized recommendation systems, enabling real-time learning and transparency, and demonstrates competitive accuracy with traditional methods on MovieLens data.

Contribution

It introduces the use of weightless neural networks for continuous, personalized recommendation, offering an alternative to traditional backpropagation-based models.

Findings

Competitive accuracy on MovieLens dataset

Supports real-time, continuous learning for personalized models

Potential for increased transparency and user control

Abstract

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.