Research area

User reactivation, generative recommendation, Semantic-ID interface diagnostics, dynamic retrieval, graph embedding, counterfactual watch-time prediction, and efficient CTR modeling form the main technical line.

Publication record

Authors

Mabiao Long, Jiaxi Liu, Yufeng Li, Hao Xiong, Junchi Yan, Kefan Wang, Yi Cao, Jiandong Ding

Venue

IEEE Transactions on Knowledge and Data Engineering

Year

2026

Area

Recommender Systems

Problem

Production graph embeddings must balance billion-scale training, data freshness, and cold-start behavior without repeatedly rebuilding the entire representation system.

Approach

The paper combines HetSGFormer for scalable global graph learning with Incremental Locally Linear Embedding for lightweight CPU-based updates when new graph data arrives.

Public evidence

The public abstract reports an advertiser-value lift of up to 6.11% for HetSGFormer, a further 3.22% lift from the incremental module, and an 83.2% improvement in embedding refresh timeliness.

Takeaway

The two-stage design separates expensive global representation learning from targeted incremental refresh, supporting practical cold-start resilience under changing graph data.