User embeddings (vectorized representations of a user) are essential in recommendation systems. Numerous approaches have been proposed to construct a representation for the user in order to find similar items for retrieval tasks, and they have been proven effective in industrial recommendation systems as well. Recently people have discovered the power of using multiple embeddings to represent a user, with the hope that each embedding represents the user's interest in a certain topic. With multi-interest representation, it's important to model the user's preference over the different topics and how the preference change with time. However, existing approaches either fail to estimate the user's affinity to each interest or unreasonably assume every interest of every user fades with an equal rate with time, thus hurting the recall of candidate retrieval. In this paper, we propose the Multi-Interest Preference (MIP) model, an approach that not only produces multi-interest for users by using the user's sequential engagement more effectively but also automatically learns a set of weights to represent the preference over each embedding so that the candidates can be retrieved from each interest proportionally. Extensive experiments have been done on various industrial-scale datasets to demonstrate the effectiveness of our approach.

翻译：用户嵌入（用户向量化表示）在推荐系统中至关重要。许多方法被提出用于构建用户表示，以在检索任务中寻找相似物品，并已被证明在工业推荐系统中行之有效。近年来，人们发现了使用多重嵌入表示用户的强大能力，期望每个嵌入能代表用户对某一主题的兴趣。在多兴趣表示中，建模用户对不同主题的偏好及其随时间变化的方式至关重要。然而，现有方法要么未能准确估计用户对每个兴趣的倾向，要么不合理地假设每个用户的每个兴趣随时间以相同速率衰减，从而损害候选集的召回效果。在本文中，我们提出多兴趣偏好（MIP）模型，该方法不仅能通过更有效地利用用户的序列交互行为产生多兴趣表示，还能自动学习一组权重代表对每个嵌入的偏好，从而使得候选集能够按比例从各兴趣中检索。我们在多个工业规模的数据集上进行了大量实验，证明了我们方法的有效性。