# Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation arXiv: https://arxiv.org/abs/2407.11245 ยท HTML: https://mlsanity.com/paper/2407.11245/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 37 references that are in the map. - First posted on arXiv 15 Jul 2024. ## Builds on (most cited first, 24 of 24) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018) - [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016) - [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018) - [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019) - [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015) - [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015) - [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020) - [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017) - [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018) - [RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms](https://mlsanity.com/paper/2011.01731/) (2020) - [A Simple Convolutional Generative Network for Next Item Recommendation](https://mlsanity.com/paper/1808.05163/) (2018) - [A Mutual Information Maximization Perspective of Language Representation Learning](https://mlsanity.com/paper/1910.08350/) (2019) - [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021) - [Translation-based Recommendation](https://mlsanity.com/paper/1707.02410/) (2017) - [RecBole 2.0: Towards a More Up-to-Date Recommendation Library](https://mlsanity.com/paper/2206.07351/) (2022) - [Sparse-Interest Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09267/) (2021) - [Contrastive Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2304.03891/) (2023) - [Mixed Information Flow for Cross-domain Sequential Recommendations](https://mlsanity.com/paper/2012.00485/) (2020) - [CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention](https://mlsanity.com/paper/2204.06519/) (2022) - [Mixed Attention Network for Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2311.08272/) (2023) - [One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation](https://mlsanity.com/paper/2211.11964/) (2022) ## Built on by (most cited first, 13 of 13) - [Mixture of Experts (MoE): A Big Data Perspective](https://mlsanity.com/paper/2501.16352/) (2025) - [Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2504.18383/) (2025) - [Revisiting Self-attention for Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2505.21811/) (2025) - [Align-for-Fusion: Harmonizing Triple Preferences via Dual-oriented Diffusion for Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2508.05074/) (2025) - [Gaussian Mixture Flow Matching with Domain Alignment for Multi-Domain Sequential Recommendation](https://mlsanity.com/paper/2510.21021/) (2025) - [LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training](https://mlsanity.com/paper/2511.19931/) (2025) - [Towards Trustworthy LLM-Based Recommendation via Rationale Integration](https://mlsanity.com/paper/2601.02364/) (2025) - [SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2604.09551/) (2026) - [Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2607.25366/) (2026) - [A Systematic Reproducibility Study of BSARec for Sequential Recommendation](https://mlsanity.com/paper/2512.17442/) (2025) - [MergeRec: Model Merging for Data-Isolated Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2601.01753/) (2026) - [Generative Data Transformation: From Mixed to Unified Data](https://mlsanity.com/paper/2602.22743/) (2026) - [CoRCi: Cross-Reconstruction of Coherent Interests Modeling in Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2608.09580/) (2026) ## Abstract (arXiv) Cross-Domain Sequential Recommendation (CDSR) improves recommendation performance by utilizing information from multiple domains, which contrasts with Single-Domain Sequential Recommendation (SDSR) that relies on a historical interaction within a specific domain. However, CDSR may underperform compared to the SDSR approach in certain domains due to negative transfer, which occurs when there is a lack of relation between domains or different levels of data sparsity. To address the issue of negative transfer, our proposed CDSR model estimates the degree of negative transfer of each domain and adaptively assigns it as a weight factor to the prediction loss, to control gradient flows through domains with significant negative transfer. To this end, our model compares the performance of a model trained on multiple domains (CDSR) with a model trained solely on the specific domain (SDSR) to evaluate the negative transfer of each domain using our asymmetric cooperative network. In addition, to facilitate the transfer of valuable cues between the SDSR and CDSR tasks, we developed an auxiliary loss that maximizes the mutual information between the representation pairs from both tasks on a per-domain basis. This cooperative learning between SDSR and CDSR tasks is similar to the collaborative dynamics between pacers and runners in a marathon. Our model outperformed numerous previous works in extensive experiments on two real-world industrial datasets across ten service domains. We also have deployed our model in the recommendation system of our personal assistant app service, resulting in 21.4% increase in click-through rate compared to existing models, which is valuable to real-world business.