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.

该研究提出了一种名为 MetaTL 的新框架，通过元学习来提高冷启动用户的顺序推荐，并形式化了冷启动用户的顺序推荐问题为 few-shot 学习问题，并采用基于翻译的桥接器来提取用户之间的动态转换模式，并采用元转换学习来实现仅具有有限交互的冷启动用户的快速学习，从而实现了对顺序交互的准确推断。

基于元转移学习的冷启动用户顺序推荐

Sequential Recommendation for Cold-start Users with Meta Transitional  Learning

Face aging, which renders aging faces for an input face, has attracted
extensive attention in the multimedia research. Recently, several conditional
Generative Adversarial Nets (GANs) based methods have achieved great success.
They can generate images fitting the real face distributions conditioned on
each individual age group. However, these methods fail to capture the
transition patterns, e.g., the gradual shape and texture changes between
adjacent age groups. In this paper, we propose a novel Contextual Generative
Adversarial Nets (C-GANs) to specifically take it into consideration. The
C-GANs consists of a conditional transformation network and two discriminative
networks. The conditional transformation network imitates the aging procedure
with several specially designed residual blocks. The age discriminative network
guides the synthesized face to fit the real conditional distribution. The
transition pattern discriminative network is novel, aiming to distinguish the
real transition patterns with the fake ones. It serves as an extra
regularization term for the conditional transformation network, ensuring the
generated image pairs to fit the corresponding real transition pattern
distribution. Experimental results demonstrate the proposed framework produces
appealing results by comparing with the state-of-the-art and ground truth. We
also observe performance gain for cross-age face verification.

本文提出一种新颖的 C-GAN 模型，用于生成更贴合真实数据的老化人脸图像，并且该模型能够考虑到相邻年龄组之间的渐变变化，从而提高了跨年龄人脸验证的性能。