用无权重神经网络实现用户级持续学习推荐，响应快且可解释。
Weightless Neural Networks for Continuously Trainable Personalized Recommendation Systems
- 用无权重神经网络替代传统反向传播，将模型当作状态机持续更新。
- 在MovieLens子集上达到与主流方法相当的准确率，且支持实时反馈。
- 适合需要快速个性化调整和透明推荐逻辑的场景，如个人助手。
传统推荐系统依赖大规模分布式系统和聚合用户数据预训练，新增数据需长时间重训，难以实时响应用户反馈，且推荐理由不透明。本文探索基于单个用户数据训练的小型个人模型，采用无权重神经网络（WNN），通过将神经网络作为状态机而非带预训练权重的系统，实现连续学习。与经典加权系统及标准协同过滤相比，在MovieLens数据集子集上达到了具有竞争力的准确率。最后讨论了如何将无权重系统与中心化系统结合，通过更易由终端用户直接调控的推荐器提升主观准确性。
原文摘要 · Abstract (English)
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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