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.

翻译：序列推荐方法能够从用户历史交互中捕捉动态用户偏好以获得更优性能。然而，现有方法大多仅利用从用户历史交互中提取的过去信息训练模型，导致用户偏好建模存在偏差。除过去信息外，训练过程中同样可获取未来信息，其中包含未来“先知式”用户偏好，这将有助于动态用户偏好的建模。为此，我们提出一种用于序列推荐的Oracle引导动态用户偏好建模方法（Oracle4Rec），该方法利用未来信息指导基于过去信息的模型训练，旨在学习具有“前瞻性”的模型。具体而言，Oracle4Rec首先通过两个独立编码器分别提取过去与未来信息，随后通过Oracle引导模块最小化过去与未来信息间的差异，从而学习前瞻性模型。我们还定制了双阶段模型训练策略以提升引导效能。大量实验表明Oracle4Rec优于当前最先进的序列推荐方法。进一步实验证明，Oracle4Rec可作为通用模块集成至其他序列推荐方法中，显著提升其性能。