A fundamental challenge for sequential recommenders is to capture the sequential patterns of users toward modeling how users transit among items. In many practical scenarios, however, there are a great number of cold-start users with only minimal logged interactions. As a result, existing sequential recommendation models will lose their predictive power due to the difficulties in learning sequential patterns over users with only limited interactions. In this work, we aim to improve sequential recommendation for cold-start users with a novel framework named MetaTL, which learns to model the transition patterns of users through meta-learning. Specifically, the proposed MetaTL: (i) formulates sequential recommendation for cold-start users as a few-shot learning problem; (ii) extracts the dynamic transition patterns among users with a translation-based architecture; and (iii) adopts meta transitional learning to enable fast learning for cold-start users with only limited interactions, leading to accurate inference of sequential interactions.

中文翻译：

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具有元过渡学习的冷启动用户的顺序推荐

顺序推荐的一个基本挑战是捕捉用户的顺序模式，以对用户如何在项目之间转换进行建模。然而，在许多实际场景中，有大量的冷启动用户只记录了最少的交互。因此，现有的顺序推荐模型将失去其预测能力，因为很难通过有限的交互来学习用户的顺序模式。在这项工作中，我们旨在通过一种名为 MetaTL 的新颖框架改进冷启动用户的顺序推荐，该框架通过元学习来学习对用户的转换模式进行建模。具体来说，提出的 MetaTL：（i）将冷启动用户的顺序推荐制定为少数学习问题；(ii) 使用基于翻译的架构提取用户之间的动态转换模式；(iii) 采用元过渡学习，为只有有限交互的冷启动用户提供快速学习，从而准确推断顺序交互。