# MOSAIC: Multi-Domain Orthogonal Session Adaptive Intent Capture for Prescient Recommendations

arXiv: https://arxiv.org/abs/2604.10147 · HTML: https://mlsanity.com/paper/2604.10147/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 11 Apr 2026.

## Builds on (most cited first, 16 of 16)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [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)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Learning Disentangled Representations for Recommendation](https://mlsanity.com/paper/1910.14238/) (2019)
- [A Survey on LLM-powered Agents for Recommender Systems](https://mlsanity.com/paper/2502.10050/) (2025)
- [Contrastive Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2304.03891/) (2023)
- [Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions](https://mlsanity.com/paper/2311.04590/) (2023)
- [A Survey on Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2401.04971/) (2024)
- [Mixed Attention Network for Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2311.08272/) (2023)
- [Federated Graph Learning for Cross-Domain Recommendation](https://mlsanity.com/paper/2410.08249/) (2024)
- [ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2501.15118/) (2025)
- [CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process](https://mlsanity.com/paper/2401.12732/) (2024)
- [NaviGNN: Multi-Agent Reinforcement Learning and Graph Neural Network for Sustainable Mobility in Futuristic Smart Cities](https://mlsanity.com/paper/2507.15143/) (2025)
- [Graph-Attention Network with Adversarial Domain Alignment for Robust Cross-Domain Facial Expression Recognition](https://mlsanity.com/paper/2512.00641/) (2025)

## Abstract (arXiv)

Capturing user intent across heterogeneous behavioral domains stands as a fundamental challenge in session-based recommender systems. Yet, existing multi-domain approaches frequently fail to isolate the distinct contribution of cross-domain interactions from those arising within individual domains, limiting their ability to build rich and transferable user representations. In this work, we propose MOSAIC, a Multi-Domain Orthogonal Session Adaptive Intent Capture framework that explicitly factorizes user preferences into three orthogonal components: domain-specific, domain-common, and cross-sequence-exclusive representations. Our approach employs a triple-encoder architecture, where each encoder is dedicated to one preference type, enforced through domain masking objectives and adversarial training via a gradient reversal layer. Representational alignment and mutual independence constraints are jointly optimized to ensure clean preference separation. Additionally, a dynamic gating mechanism modulates the relative contribution of each component at every timestep, yielding a unified and temporally adaptive session-level user representation. We conduct extensive experiments on two large-scale real-world benchmarks spanning multiple domains and interaction types. The ablation study validates that each component domain-specific encoding, domain-common modeling, cross-sequence representation, and dynamic gating contributes meaningfully to the overall performance. Experimental results demonstrate that MOSAIC consistently outperforms state-of-the-art baselines in recommendation accuracy, while simultaneously providing interpretable insights into the interplay between domain-specific and cross-domain preference signals. These findings highlight the potential of orthogonal preference decomposition as a principled strategy for next-generation multi-domain recommender systems.