Data sparsity and cold-start problems are persistent challenges in
recommendation systems. Cross-domain recommendation (CDR) is a promising
solution that utilizes knowledge from the source domain to improve the
recommendation performance in the target domain. Previous CDR approaches have
mainly followed the Embedding and Mapping (EMCDR) framework, which involves
learning a mapping function to facilitate knowledge transfer. However, these
approaches necessitate re-engineering and re-training the network structure to
incorporate transferrable knowledge, which can be computationally expensive and
may result in catastrophic forgetting of the original knowledge. In this paper,
we present a scalable and efficient paradigm to address data sparsity and
cold-start issues in CDR, named CDR-Adapter, by decoupling the original
recommendation model from the mapping function, without requiring
re-engineering the network structure. Specifically, CDR-Adapter is a novel
plug-and-play module that employs adapter modules to align feature
representations, allowing for flexible knowledge transfer across different
domains and efficient fine-tuning with minimal training costs. We conducted
extensive experiments on the benchmark dataset, which demonstrated the
effectiveness of our approach over several state-of-the-art CDR approaches.

本研究提出了一种名为 CDR-Adapter 的可扩展和高效的跨领域推荐模型，通过解耦原始推荐模型和映射函数的方式，有效解决了数据稀疏性和冷启动问题，并且无需重新设计网络结构。CDR-Adapter 采用适配器模块对特征表示进行对齐，实现了不同领域的灵活知识传递和以最小训练成本进行高效微调。在基准数据集上进行的大量实验证明了我们方法在多个最先进的 CDR 方法上的优越性。

CDR-Adapter：学习适配器以提取跨领域推荐模型更多的迁移能力

CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for  Cross-Domain Recommendation Models

Cold-start problems are enormous challenges in practical recommender systems.
One promising solution for this problem is cross-domain recommendation (CDR)
which leverages rich information from an auxiliary (source) domain to improve
the performance of recommender system in the target domain. In these CDR
approaches, the family of Embedding and Mapping methods for CDR (EMCDR) is very
effective, which explicitly learn a mapping function from source embeddings to
target embeddings with overlapping users. However, these approaches suffer from
one serious problem: the mapping function is only learned on limited
overlapping users, and the function would be biased to the limited overlapping
users, which leads to unsatisfying generalization ability and degrades the
performance on cold-start users in the target domain. With the advantage of
meta learning which has good generalization ability to novel tasks, we propose
a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta
stage. In the transfer (pre-training) stage, a source model and a target model
are trained on source and target domains, respectively. In the meta stage, a
task-oriented meta network is learned to implicitly transform the user
embedding in the source domain to the target feature space. In addition, the
TMCDR is a general framework that can be applied upon various base models,
e.g., MF, BPR, CML. By utilizing data from Amazon and Douban, we conduct
extensive experiments on 6 cross-domain tasks to demonstrate the superior
performance and compatibility of TMCDR.

本文介绍了一种基于元学习的跨域推荐系统解决方案，采用 transfer-meta 架构，可以在感知用户洞察和推荐推广等方面获得更好的性能表现。

跨领域推荐的迁移元框架：针对冷启动用户

Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start  Users

Cold-start problems are long-standing challenges for practical
recommendations. Most existing recommendation algorithms rely on extensive
observed data and are brittle to recommendation scenarios with few
interactions. This paper addresses such problems using few-shot learning and
meta learning. Our approach is based on the insight that having a good
generalization from a few examples relies on both a generic model
initialization and an effective strategy for adapting this model to newly
arising tasks. To accomplish this, we combine the scenario-specific learning
with a model-agnostic sequential meta-learning and unify them into an
integrated end-to-end framework, namely Scenario-specific Sequential Meta
learner (or s^2 meta). By doing so, our meta-learner produces a generic initial
model through aggregating contextual information from a variety of prediction
tasks while effectively adapting to specific tasks by leveraging
learning-to-learn knowledge. Extensive experiments on various real-world
datasets demonstrate that our proposed model can achieve significant gains over
the state-of-the-arts for cold-start problems in online recommendation.
Deployment is at the Guess You Like session, the front page of the Mobile
Taobao.

通过少次学习和元学习，将场景特定学习与模型无关的顺序元学习相结合，提出了一种名为 s^2meta 的综合端对端框架，可显著提高在线推荐的冷启动问题的状态艺术水平。