collaborators

7 papers

cs.AI2026

PeopleSearchBench: A Multi-Dimensional Benchmark for Evaluating AI-Powered People Search Platforms

Wei Wang, Tianyu Shi, Shuai Zhang +9

AI-powered people search platforms are increasingly used in recruiting, sales prospecting, and professional networking, yet no widely accepted benchmark exists for evaluating their…

cs.IR2026

Stop Treating Collisions Equally: Qualification-Aware Semantic ID Learning for Recommendation at Industrial Scale

Zheng Hu, Yuxin Chen, Yongsen Pan +13

Semantic IDs (SIDs) are compact discrete representations derived from multimodal item features, serving as a unified abstraction for ID-based and generative recommendation. However…

cs.IR2026

Rethinking Multi-objective Ranking Ensemble in Recommender System: From Score Fusion to Rank Consistency

Boyang Xia, Zhou Yu, Zhiliang Zhu +5

The industrial recommender systems always pursue more than one business goals. The inherent intensions between objectives pose significant challenges for ranking stage. A popular s…

cs.LG2026

Implicit Strategic Optimization: Rethinking Long-Horizon Decision-Making in Adversarial Poker Environments

Boyang Xia, Weiyou Tian, Qingnan Ren +7

Training large language model (LLM) agents for adversarial games is often driven by episodic objectives such as win rate. In long-horizon settings, however, payoffs are shaped by l…

cs.IR2026

OneMall: One Architecture, More Scenarios -- End-to-End Generative Recommender Family at Kuaishou E-Commerce

Kun Zhang, Jingming Zhang, Wei Cheng +29

In the wave of generative recommendation, we present OneMall, an end-to-end generative recommendation framework tailored for e-commerce services at Kuaishou. Our OneMall systematic…

cs.IR2026

STCRank: Spatio-temporal Collaborative Ranking for Interactive Recommender System at Kuaishou E-shop

Boyang Xia, Ruilin Bao, Hanjun Jiang +2

As a popular e-commerce platform, Kuaishou E-shop provides precise personalized product recommendations to tens of millions of users every day. To better respond real-time user fee…