2 citations · 2 across the 14 of their papers we have counts for
15 papers
IntBMoE: Integrating Block-Level Conditioning into Expert Composition for Full-Participation Mixture-of-Experts
Ran Cheng, Longfei Xu, Zheng Liu +2
Mixture-of-Experts (MoE) scales capacity, but existing designs cannot set three quantities independently. For a single token, participation is how many experts contribute knowledge…
Customizable and Jointly Optimized Route Planning: A Deep Architecture Enabling Differentiable Shortest-Path Search
Rui Zhao, Chao Chen, Longfei Xu +4
With the widespread use of online navigation and ride-hailing services, achieving optimal route planning for diverse user preferences has recently attracted increasing attention. C…
IntHQ: Task-Interactive Hierarchical Query on Dual-Stream Representations for Generative Recommendation
Junjie Sun, Longfei Xu, Huimin Yan +3
Multi-task learning over heterogeneous data is fundamental to modern recommendation, while generative models are emerging as the backbone of next-generation recommenders. However,…
Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild
Mao Zheng, Zheng Li, Tao Chen +10
Hy-MT2 is a family of fast-thinking multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B, and 30B-A3B (MoE), all of wh…
TransitLM: A Large-Scale Dataset and Benchmark for Map-Free Transit Route Generation
Hanyu Guo, Jiedong Yang, Chao Chen +3
Public transit route planning traditionally depends on structured map infrastructure and complex routing engines, and no existing dataset supports training models to bypass this de…
IntRR: A Framework for Integrating SID Redistribution and Length Reduction
Zesheng Wang, Longfei Xu, Weidong Deng +3
Generative Recommendation (GR) has emerged as a transformative paradigm that reformulates the traditional cascade ranking system into a sequence-to-item generation task, facilitate…
IntTravel: A Real-World Dataset and Generative Framework for Integrated Multi-Task Travel Recommendation
Huimin Yan, Longfei Xu, Junjie Sun +4
Next Point of Interest (POI) recommendation is essential for modern mobility and location-based services. To provide a smooth user experience, models must understand several compon…
GenMRP: A Generative Multi-Route Planning Framework for Efficient and Personalized Real-Time Industrial Navigation
Chengzhang Wang, Chao Chen, Jun Tao +6
Existing industrial-scale navigation applications contend with massive road networks, typically employing two main categories of approaches for route planning. The first relies on…
MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios
Zhiheng Song, Jingshuai Zhang, Chuan Qin +6
Route-planning agents powered by large language models (LLMs) have emerged as a promising paradigm for supporting everyday human mobility through natural language interaction and t…
SCASRec: A Self-Correcting and Auto-Stopping Model for Generative Route List Recommendation
Chao Chen, Longfei Xu, Daohan Su +5
Route recommendation systems commonly adopt a multi-stage pipeline involving fine-ranking and re-ranking to produce high-quality ordered recommendations. However, this paradigm fac…
IntSR: An Integrated Generative Framework for Search and Recommendation
Huimin Yan, Longfei Xu, Junjie Sun +6
Generative recommendation has emerged as a promising paradigm, demonstrating remarkable results in both academic benchmarks and industrial applications. However, existing systems p…
Towards Full Candidate Interaction: A Comprehensive Comparison Network for Better Route Recommendation
Hanyu Guo, Chao Chen, Longfei Xu +3
We argue that the decision-making essence of route recommendation is comparative judgment: users choose a route because it is better than alternatives in specific aspects. The deci…
DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking
Jiahao Yu, Yihai Duan, Longfei Xu +6
Multi-scenario route ranking (MSRR) is crucial in many industrial mapping systems. However, the industrial community mainly adopts interactive interfaces to encourage users to sele…
A General Framework for Debiasing in CTR Prediction
Wenjie Chu, Shen Li, Chao Chen +3
Most of the existing methods for debaising in click-through rate (CTR) prediction depend on an oversimplified assumption, i.e., the click probability is the product of observation…
R4: A Framework for Route Representation and Route Recommendation
Ran Cheng, Chao Chen, Longfei Xu +5
Route recommendation is significant in navigation service. Two major challenges for route recommendation are route representation and user representation. Different from items that…