  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.
