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:顺序推荐系统中的用户潜在意图建模
顺序推荐模型是现代工业推荐系统的重要组件。这些模型根据用户在平台上的交互历史,学习预测用户下一次可能与之交互的项目。但是,大多数顺序推荐系统缺乏对用户意图的高级理解,而这些意图往往是驱动用户在线行为的关键因素。因此,意图建模对于理解用户并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,并将用户意图作为潜在变量来进行推荐策略调整,这些变量通过变分自编码器(VAE)根据用户行为信号进行推断。我们通过离线分析和在大规模工业推荐平台上的实时实验来展示潜在用户意图建模的有效性。