Weightless Neural Networks for Continuously Trainable Personalized Recommendation Systems

• URL: http://arxiv.org/abs/2511.05499v1
• Date: Mon, 15 Sep 2025 23:51:12 GMT
• Title: Weightless Neural Networks for Continuously Trainable Personalized Recommendation Systems
• Authors: Rafayel Latif, Satwik Behera, Ali Al-Ebrahim,
• Abstract summary: We explore the performance of smaller personal models trained on per-user data using weightless neural networks (WNNs)<n>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.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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