从零样本学习到冷启动推荐
From Zero-Shot Learning to Cold-Start Recommendation
摘要：
零样本学习和冷启动推荐分别是计算机视觉和推荐系统中的两个具有挑战性的问题。一般而言，这些案件在不同社区得到独立调查。本文揭示了企业社会责任与企业社会责任是同一内涵的两种延伸。例如，它们都试图预测看不见的类，并涉及两个空间，一个用于直接特征表示，另一个用于补充描述。然而，目前还没有从企业社会责任的角度来处理企业社会责任问题的办法。这项工作，首次，制定CSR作为一个CNOL问题，并提出了一个定制的CNOL方法来处理CSR。具体来说，我们提出了一种低秩线性自动编码器（LLAE），它挑战了三个关键，即，域偏移、伪相关和计算效率等问题。LLAE由两部分组成，低秩编码器将用户行为映射到用户属性，对称解码器从用户属性重构用户行为。在CSCL和CSR任务上的大量实验验证了所提出的方法是一个双赢的公式，即，与几种传统的最先进方法相比，ZSL模型不仅可以处理CSR，并且性能显着提高，而且考虑CSR也可以使ZSL受益。
英文摘要：
Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same intension. Both of them, for instance, attempt to predict unseen classes and involve two spaces, one for direct feature representation and the other for supplementary description. Yet there is no existing approach which addresses CSR from the ZSL perspective. This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR. Specifically, we propose a Low-rank Linear Auto-Encoder (LLAE), which challenges three cruxes, i.e., domain shift, spurious correlations and computing efficiency, in this paper. LLAE consists of two parts, a low-rank encoder maps user behavior into user attributes and a symmetric decoder reconstructs user behavior from user attributes. Extensive experiments on both ZSL and CSR tasks verify that the proposed method is a win-win formulation, i.e., not only can CSR be handled by ZSL models with a significant performance improvement compared with several conventional state-of-the-art methods, but the consideration of CSR can benefit ZSL as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
