用未来用户偏好指导过去建模，让推荐更前瞻
Oracle-guided Dynamic User Preference Modeling for Sequential Recommendation
- 用双编码器分离提取过往与未来行为信息
- 通过对比未来与过去偏好，减少建模偏差
- 可作为通用模块提升其他推荐模型效果
序列推荐方法可通过用户历史交互捕捉动态偏好以提升性能。然而，现有方法仅依赖历史信息训练模型，导致偏好建模存在偏差。事实上，训练期间未来信息也可获得，其中包含未来的‘黄金标准’偏好，有助于建模动态偏好。为此，我们提出一种基于‘未来之眼’的序列推荐方法（Oracle4Rec），利用未来信息引导对过去信息的建模，旨在学习具有前瞻性的模型。具体地，Oracle4Rec通过两个独立编码器分别提取过去与未来信息，再通过一个引导模块最小化两者之间的差异，从而构建前瞻性模型。此外，我们设计了两阶段训练策略以增强引导效果。大量实验表明，Oracle4Rec优于当前最先进的序列推荐方法。进一步实验显示，该方法可作为通用模块集成到其他序列推荐模型中，显著提升其性能。
原文摘要 · Abstract (English)
Sequential recommendation methods can capture dynamic user preferences from user historical interactions to achieve better performance. However, most existing methods only use past information extracted from user historical interactions to train the models, leading to the deviations of user preference modeling. Besides past information, future information is also available during training, which contains the ``oracle'' user preferences in the future and will be beneficial to model dynamic user preferences. Therefore, we propose an oracle-guided dynamic user preference modeling method for sequential recommendation (Oracle4Rec), which leverages future information to guide model training on past information, aiming to learn ``forward-looking'' models. Specifically, Oracle4Rec first extracts past and future information through two separate encoders, then learns a forward-looking model through an oracle-guiding module which minimizes the discrepancy between past and future information. We also tailor a two-phase model training strategy to make the guiding more effective. Extensive experiments demonstrate that Oracle4Rec is superior to state-of-the-art sequential methods. Further experiments show that Oracle4Rec can be leveraged as a generic module in other sequential recommendation methods to improve their performance with a considerable margin.
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。