Publications (7)
Joint Model Parameter Scaling and Universal-Domain Data Integration for E-commerce Search Ranking
Liren Yu, Caiyuan Li, Feiyi Dong +5
Scaling studies for industrial search, advertising, and recommendation have largely emphasized enlarging model capacity or refining architectures. Yet in real-world systems, perfor…
Device-to-Device Communications Enabled Multicast Scheduling with the Multi-Level Codebook in mmWave Small Cells
Yong Niu, Liren Yu, Yong Li +3
With the exponential growth of mobile data, there are increasing interests to deploy small cells in millimeter wave (mmWave) bands to underlay the conventional homogeneous macrocel…
SeedGNN: Graph Neural Networks for Supervised Seeded Graph Matching
Liren Yu, Jiaming Xu, Xiaojun Lin
There is a growing interest in designing Graph Neural Networks (GNNs) for seeded graph matching, which aims to match two unlabeled graphs using only topological information and a s…
Graph Matching with Partially-Correct Seeds
Liren Yu, Jiaming Xu, Xiaojun Lin
Graph matching aims to find the latent vertex correspondence between two edge-correlated graphs and has found numerous applications across different fields. In this paper, we study…
HHFT: Hierarchical Heterogeneous Feature Transformer for Recommendation Systems
Liren Yu, Wenming Zhang, Silu Zhou +3
We propose HHFT (Hierarchical Heterogeneous Feature Transformer), a Transformer-based architecture tailored for industrial CTR prediction. HHFT addresses the limitations of DNN thr…
KARMA: Knowledge-Action Regularized Multimodal Alignment for Personalized Search at Taobao
Zhi Sun, Wenming Zhang, Yi Wei +4
Large Language Models (LLMs) are equipped with profound semantic knowledge, making them a natural choice for injecting semantic generalization into personalized search systems. How…
The Power of -hops in Matching Power-Law Graphs
Liren Yu, Jiaming Xu, Xiaojun Lin
This paper studies seeded graph matching for power-law graphs. Assume that two edge-correlated graphs are independently edge-sampled from a common parent graph with a power-law deg…