activity
20182026
collaborators

6 papers

cs.AI2026

Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation

Ziyun Xu, Bosen Ding, Yue Zhang +10

Industrial recommendation systems predominantly adopt a passive ranking paradigm that infers user preferences from implicit behavioral signals (e.g., clicks, dwell time) rather tha…

cs.IR2026

Bumblebee: Interleaved Mixed-Layer Building Blocks for Large-Scale Recommendation Systems

David Bauer, Cancan Zhang, Wenshun Liu +11

Recommendation systems have undergone significant transformations in the past years. The transition from traditional feature interaction modules to generative next-action predictio…

cs.IR2026

A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems

Yiming Zhu, Xu Liu, Ziyun Xu +9

Conventional recommendation systems frequently fail to fully exploit the high-dimensional semantic signals inherent in multimedia content, thereby limiting the fidelity of user pre…

cs.IR2026

Objective Shaping with Hard Negatives: Windowed Partial AUC Optimization for RL-based LLM Recommenders

Wentao Shi, Qifan Wang, Chen Chen +7

Reinforcement learning (RL) effectively optimizes Large Language Model (LLM)-based recommenders by contrasting positive and negative items. Empirically, training with beam-search n…

cs.LG2019

Deep Reinforcement Learning for Personalized Search Story Recommendation

Jason, Zhang, Junming Yin +2

In recent years, \emph{search story}, a combined display with other organic channels, has become a major source of user traffic on platforms such as e-commerce search platforms, ne…

cs.IR2018

Demystifying Core Ranking in Pinterest Image Search

Linhong Zhu

Pinterest Image Search Engine helps hundreds of millions of users discover interesting content everyday. This motivates us to improve the image search quality by evolving our ranki…