EXIT: An EXplicit Interest Transfer Framework for Cross-Domain Recommendation

• URL: http://arxiv.org/abs/2407.20121v1
• Date: Mon, 29 Jul 2024 15:52:09 GMT
• Title: EXIT: An EXplicit Interest Transfer Framework for Cross-Domain Recommendation
• Authors: Lei Huang, Weitao Li, Chenrui Zhang, Jinpeng Wang, Xianchun Yi, Sheng Chen,
• Abstract summary: Cross-domain recommendation has attracted substantial interest in industrial apps such as Meituan. We propose a simple and effective EXplicit Interest Transfer framework named EXIT to address the stated challenge.
• Score: 20.402006751823322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-domain recommendation has attracted substantial interest in industrial apps such as Meituan, which serves multiple business domains via knowledge transfer and meets the diverse interests of users. However, existing methods typically follow an implicit modeling paradigm that blends the knowledge from both the source and target domains, and design intricate network structures to share learned embeddings or patterns between domains to improve recommendation accuracy. Since the transfer of interest signals is unsupervised, these implicit paradigms often struggle with the negative transfer resulting from differences in service functions and presentation forms across different domains. In this paper, we propose a simple and effective EXplicit Interest Transfer framework named EXIT to address the stated challenge. Specifically, we propose a novel label combination approach that enables the model to directly learn beneficial source domain interests through supervised learning, while excluding inappropriate interest signals. Moreover, we introduce a scene selector network to model the interest transfer intensity under fine-grained scenes. Offline experiments conducted on the industrial production dataset and online A/B tests validate the superiority and effectiveness of our proposed framework. Without complex network structures or training processes, EXIT can be easily deployed in the industrial recommendation system. EXIT has been successfully deployed in the online homepage recommendation system of Meituan App, serving the main traffic.

Related papers

• Federated Graph Learning for Cross-Domain Recommendation [33.33321213257222]
  Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer across source and target domains. We propose FedGCDR, a novel graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-10T12:19:51Z)
• xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing [21.37585797507323]
  Cross-domain policy transfer methods mostly aim at learning domain correspondences or corrections to facilitate policy learning. We propose the Cross-Domain Trajectory EDiting framework that employs a specially designed diffusion model for cross-domain trajectory adaptation. Our proposed model architecture effectively captures the intricate dependencies among states, actions, and rewards, as well as the dynamics patterns within target data.
  arXiv  Detail & Related papers  (2024-09-13T10:07:28Z)
• Domain-Aware Cross-Attention for Cross-domain Recommendation [4.602115311495822]
  Cross-domain recommendation (CDR) is an important method to improve recommender system performance. We introduce a two-step domain-aware cross-attention, extracting transferable features of the source domain from different granularity. We conduct experiments on both public datasets and industrial datasets, and the experimental results demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-01-22T06:12:48Z)
• Self-Supervised Interest Transfer Network via Prototypical Contrastive Learning for Recommendation [32.565226710636615]
  Cross-domain recommendation has attracted increasing attention from industry and academia recently. We propose a cross-domain recommendation method: Self-supervised Interest Transfer Network (SITN) We perform two levels of cross-domain contrastive learning: 1) instance-to-instance contrastive learning, 2) instance-to-cluster contrastive learning. We conducted extensive experiments on a public dataset and a large-scale industrial dataset collected from one of the world's leading e-commerce corporations.
  arXiv  Detail & Related papers  (2023-02-28T09:30:24Z)
• Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain [52.783709712318405]
  Unsupervised domain adaptation (UDA) has attracted considerable attention, which transfers knowledge from a label-rich source domain to a related but unlabeled target domain. We propose a novel style-aware feature fusion method (SAFF) to bridge the large domain gap and transfer knowledge while alleviating the loss of class-discnative information.
  arXiv  Detail & Related papers  (2022-09-05T10:06:03Z)
• Variational Transfer Learning using Cross-Domain Latent Modulation [1.9662978733004601]
  We introduce a novel cross-domain latent modulation mechanism to a variational autoencoder framework so as to achieve effective transfer learning. Deep representations of the source and target domains are first extracted by a unified inference model and aligned by employing gradient reversal. The learned deep representations are then cross-modulated to the latent encoding of the alternative domain, where consistency constraints are also applied.
  arXiv  Detail & Related papers  (2022-05-31T03:47:08Z)
• Adaptive Trajectory Prediction via Transferable GNN [74.09424229172781]
  We propose a novel Transferable Graph Neural Network (T-GNN) framework, which jointly conducts trajectory prediction as well as domain alignment in a unified framework. Specifically, a domain invariant GNN is proposed to explore the structural motion knowledge where the domain specific knowledge is reduced. An attention-based adaptive knowledge learning module is further proposed to explore fine-grained individual-level feature representation for knowledge transfer.
  arXiv  Detail & Related papers  (2022-03-09T21:08:47Z)
• A cross-domain recommender system using deep coupled autoencoders [77.86290991564829]
  Two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors.
  arXiv  Detail & Related papers  (2021-12-08T15:14:26Z)
• Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network [58.05473757538834]
  This paper proposes a novel adversarial scoring network (ASNet) to bridge the gap across domains from coarse to fine granularity. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance.
  arXiv  Detail & Related papers  (2021-07-27T14:47:24Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction [76.98616102965023]
  Cross domain recommender system constitutes a powerful method to tackle the cold-start and sparsity problem. We propose a novel approach to cross-domain sequential recommendations based on the dual learning mechanism.
  arXiv  Detail & Related papers  (2021-06-05T01:21:21Z)

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.