Weightless Neural Networks for Continuously Trainable Personalized Recommendation Systems
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918191638249472 |
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| author | Latif, Rafayel Behera, Satwik Al-Ebrahim, Ali |
| author_facet | Latif, Rafayel Behera, Satwik Al-Ebrahim, Ali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05499 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Weightless Neural Networks for Continuously Trainable Personalized Recommendation Systems Latif, Rafayel Behera, Satwik Al-Ebrahim, Ali Information Retrieval Artificial Intelligence Machine Learning 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. |
| title | Weightless Neural Networks for Continuously Trainable Personalized Recommendation Systems |
| topic | Information Retrieval Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2511.05499 |