用自然语言描述用户兴趣，让推荐结果可读可编辑。
TEARS: Textual Representations for Scrutable Recommendations
- 用大模型生成用户兴趣的自然语言摘要，替代传统隐向量。
- 融合最优传输对齐技术，性能超过3种主流VAE模型。
- 支持用户修改摘要，实现对推荐结果的直接控制。
传统推荐系统依赖高维隐向量建模用户-物品交互，导致表示不透明且难以解释。为解决此问题，我们提出文本可读推荐框架TEARS，不再使用隐向量表示用户兴趣，而是通过现代大模型生成自然语言形式的用户摘要，提升透明性并允许用户编辑。实验表明，这些摘要能独特捕捉用户偏好。在此基础上，采用混合方法：利用最优传输将摘要表示与标准变分自编码器（VAE）的协同过滤表示对齐。该方法在性能上超越三种主流VAE模型，同时支持用户可控推荐。通过三项模拟用户任务评估了用户编辑摘要的有效性，验证了系统的可操控性。
原文摘要 · Abstract (English)
Traditional recommender systems rely on high-dimensional (latent) embeddings for modeling user-item interactions, often resulting in opaque representations that lack interpretability. Moreover, these systems offer limited control to users over their recommendations. Inspired by recent work, we introduce TExtuAl Representations for Scrutable recommendations (TEARS) to address these challenges. Instead of representing a user's interests through a latent embedding, TEARS encodes them in natural text, providing transparency and allowing users to edit them. To do so, TEARS uses a modern LLM to generate user summaries based on user preferences. We find the summaries capture user preferences uniquely. Using these summaries, we take a hybrid approach where we use an optimal transport procedure to align the summaries' representation with the learned representation of a standard VAE for collaborative filtering. We find this approach can surpass the performance of three popular VAE models while providing user-controllable recommendations. We also analyze the controllability of TEARS through three simulated user tasks to evaluate the effectiveness of a user editing its summary.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。