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.
翻译:对相继建议者来说,一个基本挑战是捕捉用户的顺序模式,以模拟用户在项目之间的过境方式。然而,在许多实际假设中,有大量冷启动用户,只有最小的登录互动。因此,现有的顺序建议模式将失去预测力,因为难以在互动有限的用户之间学习顺序模式。在这项工作中,我们的目标是改进冷启动用户的顺序建议,采用名为MetaTL的新框架,通过元学习学习学习学习来学习用户的过渡模式模式。具体地说,拟议的MetaTL:(一)为冷启动用户制定顺序建议,将其作为一个少见的学习问题;(二)提取基于翻译结构的用户之间的动态过渡模式;(三)采用元过渡学习,使互动有限的冷启动用户能够快速学习,从而准确推断顺序互动。