1 citations · 1 across the 3 of their papers we have counts for
7 papers
When and How Order-Flow Interventions Work After Dealers Adapt
Guanghao Wu, Zhuo Cai, Zhidong Li +1
Learning dealer agents can settle on persistently wide quotes, raising what customers pay to trade. A venue can route more orders to tighter quotes, but a wide quote may reflect re…
Generate What You Can Trust: Content Credibility in Generative Recommenders
Zhuo Cai, Guanghao Wu, Shoujin Wang +2
Generative recommendation (GR) represents items with semantic IDs (i.e., discrete token sequences) and generates target item tokens as recommendations. Despite its promising result…
SPRINT: Single-Step Generative Recommendation via Average Probability Velocity
Zhuo Cai, Shoujin Wang, Peilin Zhou +3
Semantic ID (SID) based generative recommendation represents each item as a sequence of discrete tokens, and recommends by generating the SID of the item a user would like to inter…
DTRec: Learning Dynamic Reasoning Trajectories for Sequential Recommendation
Yifan Shao, Peilin Zhou, Shoujin Wang +3
Inspired by advances in LLMs, reasoning-enhanced sequential recommendation performs multi-step deliberation before making final predictions, unlocking greater potential for capturi…
Structural and Disentangled Adaptation of Large Vision Language Models for Multimodal Recommendation
Zhongtao Rao, Peilin Zhou, Dading Chong +3
Multimodal recommendation enhances accuracy by leveraging visual and textual signals, and its success largely depends on learning high-quality cross-modal representations. Recent a…
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
Peilin Zhou, You-Liang Huang, Yueqi Xie +4
Sequential recommender systems (SRS) are designed to predict users' future behaviors based on their historical interaction data. Recent research has increasingly utilized contrasti…
Equivariant Contrastive Learning for Sequential Recommendation
Peilin Zhou, Jingqi Gao, Yueqi Xie +5
Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augm…