ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation

2024-07-02 · Chaoqun Hou, Yuanhang Zhou, Yi Cao, Tong Liu

In industrial recommendation systems, there are several mini-apps designed to meet the diverse interests and needs of users. The sample space of them is merely a small subset of the entire space, making it challenging to train an efficient model. In recent years, there have been many excellent studies related to cross-domain recommendation aimed at mitigating the problem of data sparsity. However, few of them have simultaneously considered the adaptability of both sample and representation continual transfer setting to the target task. To overcome the above issue, we propose a Entire space Continual and Adaptive Transfer learning framework called ECAT which includes two core components: First, as for sample transfer, we propose a two-stage method that realizes a coarse-to-fine process. Specifically, we perform an initial selection through a graph-guided method, followed by a fine-grained selection using domain adaptation method. Second, we propose an adaptive knowledge distillation method for continually transferring the representations from a model that is well-trained on the entire space dataset. ECAT enables full utilization of the entire space samples and representations under the supervision of the target task, while avoiding negative migration. Comprehensive experiments on real-world industrial datasets from Taobao show that ECAT advances state-of-the-art performance on offline metrics, and brings +13.6% CVR and +8.6% orders for Baiyibutie, a famous mini-app of Taobao.

📄 PDF Abstract BibTeX arXiv:2407.02542

Code (0)

등록된 구현이 없습니다.

Tasks

Domain Adaptation Knowledge Distillation Recommendation Systems Transfer Learning

Methods 이 논문이 사용한 방법론

Knowledge Distillation A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions.…

eCat: An End-to-End Model for Multi-Speaker TTS & Many-to-Many Fine-Grained Prosody Transfer

2023-06-20 · Ammar Abbas, Sri Karlapati, Bastian Schnell, Penny Karanasou 외

We present eCat, a novel end-to-end multispeaker model capable of: a) generating long-context speech with expressive and contextually appropriate prosody, and b) performing fine-grained prosody transfer between any pair …

Addressing Client Drift in Federated Continual Learning with Adaptive Optimization

2022-03-24 · Yeshwanth Venkatesha, Youngeun Kim, Hyoungseob Park, Yuhang Li 외

Federated learning has been extensively studied and is the prevalent method for privacy-preserving distributed learning in edge devices. Correspondingly, continual learning is an emerging field targeted towards learning …

Continual Learning Federated Learning Privacy Preserving

CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection

2026-07-03 · Wen Dong, Zhao Wang, Shuangqing Zhang, Kai Sun 외 arxiv

Multimodal Large Language Models (MLLMs) excel in diverse vision tasks, but full-parameter retraining is computationally expensive as real-world knowledge evolves. Existing continual learning methods often suffer from se…

parameter-efficient fine-tuning Continual Learning Anomaly Detection

CLoPA: Continual Low Parameter Adaptation of Interactive Segmentation for Medical Image Annotation

2026-03-06 · Parhom Esmaeili, Chayanin Tangwiriyasakul, Eli Gibson, Sebastien Ourselin 외 arxiv

Interactive segmentation enables clinicians to guide annotation, but existing zero-shot models like nnInteractive fail to consistently reach expert-level performance across diverse medical imaging tasks. Because annotati…

Interactive Segmentation

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization

2020-03-30 · NeurIPS 2020 12 · Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon

We propose a novel regularization-based continual learning method, dubbed as Adaptive Group Sparsity based Continual Learning (AGS-CL), using two group sparsity-based penalties. Our method selectively employs the two pen…

Continual Learning Reinforcement Learning