User Representation via Cross Multi-source Behavior Pre-training for Mobile Games
cs.AI
Submitted: 2026-09-01
Updated: 2026-09-01
Comments: Accepted by IEEE ICDM 2026, regular paper, 10 pages
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- SimCSE: Simple Contrastive Learning of Sentence Embeddings
- PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision
- Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
- AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Deep contextualized word representations
- RoBERTa: A Robustly Optimized BERT Pretraining Approach
- UPRec: User-Aware Pre-training for Recommender Systems
- Joint Modeling in Recommendations: A Survey
- A Comprehensive Survey on Cross-Domain Recommendation: Taxonomy, Progress, and Prospects
- DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation
- Federated Graph Learning for Cross-Domain Recommendation
- Adam: A Method for Stochastic Optimization
- DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems