Weightless Neural Networks for Continuously Trainable Personalized Recommendation Systems
This addresses the need for more adaptable and transparent recommendation systems for users, though it appears incremental as it builds on existing personalization concepts.
The paper tackled the problem of conventional recommenders being slow to adapt to real-time user feedback by using weightless neural networks (WNNs) for per-user models, achieving competitive accuracy on a MovieLens subset.
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.