activity
20182024
most citedDeep Interest Evolution Network for Click-Through Rate Prediction

68 citations · 74 across the 8 of their papers we have counts for

collaborators
Showing cs.IRShow all

7 papers · 1 filter

cs.IR2024

RecFlow: An Industrial Full Flow Recommendation Dataset

Qi Liu, Kai Zheng, Rui Huang +15

Industrial recommendation systems (RS) rely on the multi-stage pipeline to balance effectiveness and efficiency when delivering items from a vast corpus to users. Existing RS bench…

cs.IR2024★ 1 cited

DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models

Wuchao Li, Rui Huang, Haijun Zhao +10

Sequential Recommendation (SR) plays a pivotal role in recommender systems by tailoring recommendations to user preferences based on their non-stationary historical interactions. A…

cs.IR2024

Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems

Kai Zheng, Haijun Zhao, Rui Huang +6

The Probability Ranking Principle (PRP) has been considered as the foundational standard in the design of information retrieval (IR) systems. The principle requires an IR module's…

cs.IR2024

End-to-end training of Multimodal Model and ranking Model

Xiuqi Deng, Lu Xu, Xiyao Li +10

Traditional recommender systems heavily rely on ID features, which often encounter challenges related to cold-start and generalization. Modeling pre-extracted content features can…

cs.IR2023

SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems

Beichuan Zhang, Chenggen Sun, Jianchao Tan +7

Increasing the size of embedding layers has shown to be effective in improving the performance of recommendation models, yet gradually causing their sizes to exceed terabytes in in…

cs.IR2023★ 2 cited

PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation

Ziyang Liu, Chaokun Wang, Jingcao Xu +5

Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users. In recent years, there has been a growi…

cs.IR2020★ 3 cited

CAN: Feature Co-Action for Click-Through Rate Prediction

Weijie Bian, Kailun Wu, Lejian Ren +12

Feature interaction has been recognized as an important problem in machine learning, which is also very essential for click-through rate (CTR) prediction tasks. In recent years, De…