Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing behaviors. Unlike the standard autoregressive training strategy, future data (also available during training) has been used to facilitate model training as it provides richer signals about user's current interests and can be used to improve the recommendation quality. However, these methods suffer from a severe training-inference gap, i.e., both past and future contexts are modeled by the same encoder when training, while only historical behaviors are available during inference. This discrepancy leads to potential performance degradation. To alleviate the training-inference gap, we propose a new framework DualRec, which achieves past-future disentanglement and past-future mutual enhancement by a novel dual network. Specifically, a dual network structure is exploited to model the past and future context separately. And a bi-directional knowledge transferring mechanism enhances the knowledge learnt by the dual network. Extensive experiments on four real-world datasets demonstrate the superiority of our approach over baseline methods. Besides, we demonstrate the compatibility of DualRec by instantiating using RNN, Transformer, and filter-MLP as backbones. Further empirical analysis verifies the high utility of modeling future contexts under our DualRec framework. The code of DualRec is publicly available at https://github.com/zhy99426/DualRec.
翻译:序列建议(SR)在个性化建议系统中发挥了重要作用,因为它从用户实时增加的行为中捕捉到动态和不同偏好,因此在个人化建议系统中具有重要作用。与标准的自我递减培训战略不同,未来数据(培训期间也提供)被用于促进示范培训,因为它为用户当前利益提供了更丰富的信号,并可用于改进建议质量。但是,这些方法存在严重的培训-推断差距,即:培训时,过去和今后的情况都由同一个编码器模拟,而在推断期间只有历史行为。这种差异导致潜在的性能退化。为缓解培训-推导差距,我们提出了一个新的框架“双重Rec”,通过新的双重网络实现过去-未来混乱和过去-前景的相互加强。具体地说,利用双向网络结构来模拟过去和今后的背景。双向传输机制加强了双向网络所学到的知识。关于四个真实世界数据设置的广泛实验表明我们的方法优于基线方法的优越性。此外,我们利用IMFI-ML的高级基础分析,我们用IMFIFS-CFIFI 来展示了我们现在的硬质-BIFIFIFIL 。