Cold-start issues have been more and more challenging for providing accurate
recommendations with the fast increase of users and items. Most existing
approaches attempt to solve the intractable problems via content-aware
recommendations based on auxiliary information and/or cross-domain
recommendations with transfer learning. Their performances are often
constrained by the extremely sparse user-item interactions, unavailable side
information, or very limited domain-shared users. Recently, meta-learners with
meta-augmentation by adding noises to labels have been proven to be effective
to avoid overfitting and shown good performance on new tasks. Motivated by the
idea of meta-augmentation, in this paper, by treating a user's preference over
items as a task, we propose a so-called Diverse Preference Augmentation
framework with multiple source domains based on meta-learning (referred to as
MetaDPA) to i) generate diverse ratings in a new domain of interest (known as
target domain) to handle overfitting on the case of sparse interactions, and to
ii) learn a preference model in the target domain via a meta-learning scheme to
alleviate cold-start issues. Specifically, we first conduct multi-source domain
adaptation by dual conditional variational autoencoders and impose a
Multi-domain InfoMax (MDI) constraint on the latent representations to learn
domain-shared and domain-specific preference properties. To avoid overfitting,
we add a Mutually-Exclusive (ME) constraint on the output of decoders to
generate diverse ratings given content data. Finally, these generated diverse
ratings and the original ratings are introduced into the meta-training
procedure to learn a preference meta-learner, which produces good
generalization ability on cold-start recommendation tasks. Experiments on
real-world datasets show our proposed MetaDPA clearly outperforms the current
state-of-the-art baselines.

本文提出了一种基于元学习和多源领域自适应的多元偏好增强框架（MetaDPA），以解决稀疏交互和冷启动问题，并通过将多领域信息最大化约束（MDI）添加到变分自编码器的潜在表示上来学习领域共享和特定的偏好属性，同时通过互斥约束（ME）生成内容数据的多样化评分。实验结果表明，MetaDPA 明显优于当前最优秀的基线。

基于多个领域的多样化偏好增强冷启动推荐

Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations

While Natural Language Processing (NLP) algorithms keep reaching
unprecedented milestones, out-of-distribution generalization is still
challenging. In this paper we address the problem of multi-source adaptation to
unknown domains: Given labeled data from multiple source domains, we aim to
generalize to data drawn from target domains that are unknown to the algorithm
at training time. We present an algorithmic framework based on example-based
Hypernetwork adaptation: Given an input example, a T5 encoder-decoder first
generates a unique signature which embeds this example in the semantic space of
the source domains, and this signature is then fed into a Hypernetwork which
generates the weights of the task classifier. In an advanced version of our
model, the learned signature also serves for improving the representation of
the input example. In experiments with two tasks, sentiment classification and
natural language inference, across 29 adaptation settings, our algorithms
substantially outperform existing algorithms for this adaptation setup. To the
best of our knowledge, this is the first time Hypernetworks are applied to
domain adaptation or in example-based manner in NLP.

本文介绍了一种基于示例的超网络适应算法，主要解决多源适应问题，以及如何应用于自然语言处理领域的领域自适应，实现超网络在情感分类方面的应用。