Sequential recommender models are essential components of modern industrial recommender systems. These models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience. We propose a probabilistic modeling approach and formulate user intent as latent variables, which are inferred based on user behavior signals using variational autoencoders (VAE). The recommendation policy is then adjusted accordingly given the inferred user intent. We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.

翻译：序列推荐模型是现代工业推荐系统的核心组件。这类模型基于用户与平台的交互历史，学习预测用户下一步可能互动的物品。然而，大多数序列推荐器缺乏对用户意图的深层理解，而用户意图往往是驱动在线行为的关键因素。因此，意图建模对于理解用户及优化长期用户体验至关重要。我们提出了一种概率建模方法，将用户意图形式化为隐变量，通过基于用户行为信号的变分自编码器（VAE）进行推断。推荐策略随后根据推断出的用户意图进行调整。我们通过离线分析以及在大规模工业推荐平台上的线上实验，验证了隐式用户意图建模的有效性。