# Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations

arXiv: https://arxiv.org/abs/1910.02448 · HTML: https://mlsanity.com/paper/1910.02448/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2019.

## Built on by (most cited first, 21 of 21)

- [Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2406.03085/) (2024)
- [Image Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2502.15694/) (2024)
- [DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2209.10163/) (2022)
- [LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2506.17966/) (2025)
- [Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2206.08050/) (2022)
- [Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2206.08088/) (2022)
- [LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation](https://mlsanity.com/paper/2501.19232/) (2025)
- [Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach](https://mlsanity.com/paper/2311.04760/) (2023)
- [Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2504.15085/) (2025)
- [Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2405.12473/) (2024)
- [Lightweight yet Fine-grained: A Graph Capsule Convolutional Network with Subspace Alignment for Shared-account Sequential Recommendation](https://mlsanity.com/paper/2412.13408/) (2024)
- [Beyond Negative Transfer: Disentangled Preference-Guided Diffusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2509.00389/) (2025)
- [Gaussian Mixture Flow Matching with Domain Alignment for Multi-Domain Sequential Recommendation](https://mlsanity.com/paper/2510.21021/) (2025)
- [Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2607.25366/) (2026)
- [Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network](https://mlsanity.com/paper/2302.03221/) (2023)
- [GIST: Cross-Domain Click-Through Rate Prediction via Guided Content-Behavior Distillation](https://mlsanity.com/paper/2507.05142/) (2025)
- [Efficient Large-Scale Cross-Domain Sequential Recommendation with Dynamic State Representations](https://mlsanity.com/paper/2508.20945/) (2025)
- [Tag-Enriched Multi-Attention with Large Language Models for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2510.09224/) (2025)
- [Generative Data Transformation: From Mixed to Unified Data](https://mlsanity.com/paper/2602.22743/) (2026)
- [DisenReason: Behavior Disentanglement and Latent Reasoning for Shared-Account Sequential Recommendation](https://mlsanity.com/paper/2603.03782/) (2026)
- [CoRCi: Cross-Reconstruction of Coherent Interests Modeling in Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2608.09580/) (2026)

## Abstract (arXiv)

Sequential recommendation is a task in which one models and uses sequential information about user behavior for recommendation purposes. We study sequential recommendation in a particularly challenging context, in which multiple individual users share asingle account (i.e., they have a shared account) and in which user behavior is available in multiple domains (i.e., recommendations are cross-domain). These two characteristics bring new challenges on top of those of the traditional sequential recommendation task. First, we need to identify the behavior associated with different users and different user roles under the same account in order to recommend the right item to the right user role at the right time. Second, we need to identify behavior in one domain that might be helpful to improve recommendations in other domains. In this work, we study shared account cross-domain sequential recommendation and propose Parallel Split-Join Network (PSJNet), a parallel modeling network to address the two challenges above. We present two variants of PSJNet, PSJNet-I and PSJNet-II. PSJNet-I is a "split-by-join" framework that splits the mixed representations to get role-specific representations and joins them to obtain cross-domain representations at each timestamp simultaneously. PSJNet-II is a "split-and-join" framework that first splits role-specific representations at each timestamp, and then the representations from all timestamps and all roles are joined to obtain cross-domain representations. We use two datasets to assess the effectiveness of PSJNet. Our experimental results demonstrate that PSJNet outperforms state-of-the-art sequential recommendation baselines in terms of MRR and Recall.