Contrastive Cross-Domain Sequential Recommendation

Abstract

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C^2DSR to tackle the above problems to capture precise user preferences. The main idea is to simultaneously leverage the intra- and inter- sequence item relationships, and jointly learn the single- and cross- domain user preferences. Specifically, we first utilize a graph neural network to mine inter-sequence item collaborative relationship, and then exploit sequential attentive encoder to capture intra-sequence item sequential relationship. Based on them, we devise two different sequential training objectives to obtain user single-domain and cross-domain representations. Furthermore, we present a novel contrastive cross-domain infomax objective to enhance the correlation between single- and cross- domain user representations by maximizing their mutual information. To validate the effectiveness of C^2DSR, we first re-split four e-comerce datasets, and then conduct extensive experiments to demonstrate the effectiveness of our approach C^2DSR.

arXiv commentThis paper has been accepted by CIKM 2022

References in corpus (16)

• Adam: A Method for Stochastic Optimization
• Representation Learning with Contrastive Predictive Coding
• Prototypical Networks for Few-shot Learning
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Learning deep representations by mutual information estimation and maximization
• Session-based Recommendation with Graph Neural Networks
• Simplifying Graph Convolutional Networks
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
• Translation-based Recommendation
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
• CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
• RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
• RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation
• DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation
• Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations

Cited by in corpus (10)

• Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
• A Comprehensive Survey on Self-Supervised Learning for Recommendation
• FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning
• Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer
• ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation
• Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair Selection
• Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation
• LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation
• Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain Recommendation
• Generative Archetype-Grounded Item Representations for Sequential Recommendation