Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach

• URL: http://arxiv.org/abs/2311.04760v3
• Date: Wed, 5 Jun 2024 08:04:32 GMT
• Title: Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach
• Authors: Wujiang Xu, Xuying Ning, Wenfang Lin, Mingming Ha, Qiongxu Ma, Qianqiao Liang, Xuewen Tao, Linxun Chen, Bing Han, Minnan Luo,
• Abstract summary: Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems. In real-world recommender systems, CDSR scenarios usually consist of a majority of long-tailed users with sparse behaviors and cold-start users who only exist in one domain.
• Score: 16.09514981871128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-domain sequential recommendation (CDSR) aims to address the data sparsity problems that exist in traditional sequential recommendation (SR) systems. The existing approaches aim to design a specific cross-domain unit that can transfer and propagate information across multiple domains by relying on overlapping users with abundant behaviors. However, in real-world recommender systems, CDSR scenarios usually consist of a majority of long-tailed users with sparse behaviors and cold-start users who only exist in one domain. This leads to a drop in the performance of existing CDSR methods in the real-world industry platform. Therefore, improving the consistency and effectiveness of models in open-world CDSR scenarios is crucial for constructing CDSR models (\textit{1st} CH). Recently, some SR approaches have utilized auxiliary behaviors to complement the information for long-tailed users. However, these multi-behavior SR methods cannot deliver promising performance in CDSR, as they overlook the semantic gap between target and auxiliary behaviors, as well as user interest deviation across domains (\textit{2nd} CH).

Related papers

• A Zero-Shot Generalization Framework for LLM-Driven Cross-Domain Sequential Recommendation [5.512301280728178]
  Zero-shot cross-domain sequential recommendation (ZCDSR) enables predictions in unseen domains without the need for additional training or fine-tuning. Recent advancements in large language models (LLMs) have greatly improved ZCDSR by leveraging rich pretrained representations to facilitate cross-domain knowledge transfer. We propose a novel framework designed to enhance LLM-based ZCDSR by improving cross-domain alignment at both the item and sequential levels.
  arXiv  Detail & Related papers  (2025-01-31T15:43:21Z)
• Cross-Domain Recommendation Meets Large Language Models [3.1519384727993582]
  Cross-domain recommendation (CDR) has emerged as a promising solution to the cold-start problem. Existing CDR models rely on complex neural architectures, large datasets, and significant computational resources. In this work, we leverage the reasoning capabilities of large language models (LLMs) and explore their performance in the CDR domain.
  arXiv  Detail & Related papers  (2024-11-29T17:25:00Z)
• Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model [66.91323540178739]
  Sequential recommendation (SR) aims to predict items that users may be interested in based on their historical behavior. We revisit SR from a novel information-theoretic perspective and find that sequential modeling methods fail to adequately capture randomness and unpredictability of user behavior. Inspired by fuzzy information processing theory, this paper introduces the fuzzy sets of interaction sequences to overcome the limitations and better capture the evolution of users' real interests.
  arXiv  Detail & Related papers  (2024-10-31T14:52:01Z)
• Cross-Domain Sequential Recommendation via Neural Process [9.01082886458853]
  Cross-Domain Sequential Recommendation (CDSR) is a hot topic in sequence-based user interest modeling. We show how to unleash the potential of non-overlapped users' behaviors to empower CDSR.
  arXiv  Detail & Related papers  (2024-10-17T14:22:57Z)
• Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation [66.72195610471624]
  Cross-Domain Sequential Recommendation aims to mine and transfer users' sequential preferences across different domains. We propose a novel framework named URLLM, which aims to improve the CDSR performance by exploring the User Retrieval approach.
  arXiv  Detail & Related papers  (2024-06-05T09:19:54Z)
• Information Maximization via Variational Autoencoders for Cross-Domain Recommendation [26.099908029810756]
  We introduce a new CDSR framework named Information Maximization Variational Autoencoder (textbftextttIM-VAE) Here, we suggest using a Pseudo-Sequence Generator to enhance the user's interaction history input for downstream fine-grained CDSR models. To the best of our knowledge, this paper is the first CDSR work that considers the information disentanglement and denoising of pseudo-sequences in the open-world recommendation scenario.
  arXiv  Detail & Related papers  (2024-05-31T09:07:03Z)
• FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning [17.497009723665116]
  We propose FedDCSR, a novel cross-domain sequential recommendation framework via disentangled representation learning. We introduce an approach called inter-intra domain sequence representation disentanglement (SRD) to disentangle user sequence features into domain-shared and domain-exclusive features. In addition, we design an intra domain contrastive infomax (CIM) strategy to learn richer domain-exclusive features of users by performing data augmentation on user sequences.
  arXiv  Detail & Related papers  (2023-09-15T14:23:20Z)
• 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)
• Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations [85.6250759280292]
  Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. Existing cross-domain models typically require large number of overlap users, which can be difficult to obtain in some applications. We propose a novel cross-domain recommendation model based on dual learning that transfers information between two related domains in an iterative manner.
  arXiv  Detail & Related papers  (2021-04-17T09:18:59Z)
• Learning Domain Invariant Representations for Generalizable Person Re-Identification [71.35292121563491]
  Generalizable person Re-Identification (ReID) has attracted growing attention in recent computer vision community. We introduce causality into person ReID and propose a novel generalizable framework, named Domain Invariant Representations for generalizable person Re-Identification (DIR-ReID)
  arXiv  Detail & Related papers  (2021-03-29T18:59:48Z)
• Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis [76.46004354572956]
  We introduce an unsupervised domain adaptation approach for person re-identification. Experimental results show that the proposed ktCUDA and SHRED approach achieves an average improvement of +5.7 mAP in re-identification performance.
  arXiv  Detail & Related papers  (2020-01-14T17:43:52Z)

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.