Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System

• URL: http://arxiv.org/abs/2406.17289v2
• Date: Thu, 4 Jul 2024 14:54:07 GMT
• Title: Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System
• Authors: Xin Yang, Heng Chang, Zhijian Lai, Jinze Yang, Xingrun Li, Yu Lu, Shuaiqiang Wang, Dawei Yin, Erxue Min,
• Abstract summary: Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to alleviate the problem of data sparsity in the target recommendation domain. Most current methods represent users and items in Euclidean space, which is not ideal for handling long-tail distributed data. We introduce a new framework called Hyperbolic Contrastive Learning (HCTS), designed to capture the unique features of each domain.
• Score: 28.003142450569452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to alleviate the problem of data sparsity in the target recommendation domain, and it has been gaining more attention in recent years. Although there have been notable advancements in this area, most current methods represent users and items in Euclidean space, which is not ideal for handling long-tail distributed data in recommendation systems. Additionally, adding data from other domains can worsen the long-tail characteristics of the entire dataset, making it harder to train CDR models effectively. Recent studies have shown that hyperbolic methods are particularly suitable for modeling long-tail distributions, which has led us to explore hyperbolic representations for users and items in CDR scenarios. However, due to the distinct characteristics of the different domains, applying hyperbolic representation learning to CDR tasks is quite challenging. In this paper, we introduce a new framework called Hyperbolic Contrastive Learning (HCTS), designed to capture the unique features of each domain while enabling efficient knowledge transfer between domains. We achieve this by embedding users and items from each domain separately and mapping them onto distinct hyperbolic manifolds with adjustable curvatures for prediction. To improve the representations of users and items in the target domain, we develop a hyperbolic contrastive learning module for knowledge transfer. Extensive experiments on real-world datasets demonstrate that hyperbolic manifolds are a promising alternative to Euclidean space for CDR tasks.

Related papers

• A Unified Framework for Cross-Domain Recommendation [15.987800946057343]
  Cross-Domain Recommendation emerges as a promising methodology. We extend the former SOTA model UniCDR in five different aspects, named as UniCDR+. Our work was successfully deployed on the Kuaishou Living-Room RecSys.
  arXiv  Detail & Related papers  (2024-09-06T18:10:42Z)
• Heterogeneous Graph-based Framework with Disentangled Representations Learning for Multi-target Cross Domain Recommendation [7.247438542823219]
  CDR (Cross-Domain Recommendation) is a critical solution to data sparsity problem in recommendation system. We present HGDR (Heterogeneous Graph-based Framework with Disentangled Representations Learning), an end-to-end heterogeneous network architecture. Experiments on real-world datasets and online A/B tests prove that our proposed model can transmit information among domains effectively.
  arXiv  Detail & Related papers  (2024-07-01T02:27:54Z)
• FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling [15.159012729198619]
  We propose FedHCDR, a novel Cross-Domain Recommendation framework with Hypergraph signal decoupling. In this study, we introduce an approach called hypergraph signal decoupling (HSD) to decouple the user features into domain-exclusive and domain-shared features. Extensive experiments conducted on three real-world scenarios demonstrate that FedHCDR outperforms existing baselines significantly.
  arXiv  Detail & Related papers  (2024-03-05T03:40:39Z)
• Unified Domain Adaptive Semantic Segmentation [96.74199626935294]
  Unsupervised Adaptive Domain Semantic (UDA-SS) aims to transfer the supervision from a labeled source domain to an unlabeled target domain. We propose a Quad-directional Mixup (QuadMix) method, characterized by tackling distinct point attributes and feature inconsistencies. Our method outperforms the state-of-the-art works by large margins on four challenging UDA-SS benchmarks.
  arXiv  Detail & Related papers  (2023-11-22T09:18:49Z)
• Mixed Attention Network for Cross-domain Sequential Recommendation [63.983590953727386]
  We propose a Mixed Attention Network (MAN) with local and global attention modules to extract the domain-specific and cross-domain information. Experimental results on two real-world datasets demonstrate the superiority of our proposed model.
  arXiv  Detail & Related papers  (2023-11-14T16:07:16Z)
• Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data [18.41222232863567]
  We propose a novel end-to-end framework called Universal and joint knowledge distillation (UNI-KD) for cross-domain model compression. In particular, we propose to transfer both the universal feature-level knowledge across source and target domains and the joint logit-level knowledge shared by both domains from the teacher to the student model via an adversarial learning scheme.
  arXiv  Detail & Related papers  (2023-07-07T01:48:02Z)
• Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation [71.45854187886088]
  Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-02-12T19:51:32Z)
• Multi-scale Feature Alignment for Continual Learning of Unlabeled Domains [3.9498537297431167]
  generative feature-driven image replay in conjunction with a dual-purpose discriminator enables the generation of images with realistic features for replay. We present detailed ablation experiments studying our proposed method components and demonstrate a possible use-case of our continual UDA method for an unsupervised patch-based segmentation task.
  arXiv  Detail & Related papers  (2023-02-02T18:19:01Z)
• Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting [75.80116276369694]
  In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset. We resort to the multi-domain joint learning and propose a simple but effective Domain-specific Knowledge Propagating Network (DKPNet) It is mainly achieved by proposing the novel Variational Attention(VA) technique for explicitly modeling the attention distributions for different domains.
  arXiv  Detail & Related papers  (2021-08-18T08:06:37Z)
• Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning [85.6386289476598]
  We develop a novel adversarial graph representation adaptation (AGRA) framework for cross-domain holistic-local feature co-adaptation. We conduct extensive and fair evaluations on several popular benchmarks and show that the proposed AGRA framework outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-03T15:00:31Z)
• Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation [56.694330303488435]
  We propose a Learning to Combine for Multi-Source Domain Adaptation (LtC-MSDA) framework. In the nutshell, a knowledge graph is constructed on the prototypes of various domains to realize the information propagation among semantically adjacent representations. Our approach outperforms existing methods with a remarkable margin.
  arXiv  Detail & Related papers  (2020-07-17T07:52:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.