activity
20222025
most citedPersonalizing Intervened Network for Long-tailed Sequential User Behavior Modeling

3 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2025

InfiFusion: A Unified Framework for Enhanced Cross-Model Reasoning via LLM Fusion

Zhaoyi Yan, Yiming Zhang, Baoyi He +7

We introduce InfiFusion, an efficient training pipeline designed to integrate multiple domain-specialized Large Language Models (LLMs) into a single pivot model, effectively harnes…

cs.CV2024

MOSS: Motion-based 3D Clothed Human Synthesis from Monocular Video

Hongsheng Wang, Xiang Cai, Xi Sun +5

Single-view clothed human reconstruction holds a central position in virtual reality applications, especially in contexts involving intricate human motions. It presents notable cha…

cs.IR2023

DisCover: Disentangled Music Representation Learning for Cover Song Identification

Jiahao Xun, Shengyu Zhang, Yanting Yang +7

In the field of music information retrieval (MIR), cover song identification (CSI) is a challenging task that aims to identify cover versions of a query song from a massive collect…

cs.IR2023

Denoising Multi-modal Sequential Recommenders with Contrastive Learning

Dong Yao, Shengyu Zhang, Zhou Zhao +5

There is a rapidly-growing research interest in engaging users with multi-modal data for accurate user modeling on recommender systems. Existing multimedia recommenders have achiev…

cs.IR20223 cited

Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling

Zheqi Lv, Feng Wang, Shengyu Zhang +3

In an era of information explosion, recommendation systems play an important role in people's daily life by facilitating content exploration. It is known that user activeness, i.e.…

cs.IR2022

CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation

Shengyu Zhang, Bofang Li, Dong Yao +7

Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial reco…