From the 1 of 20 linked papers with an AI index.
20 papers
UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction
Honghao Li, Xianquan Wang, Zibin Zhang +3
Ranking is a core stage in online advertising and recommender systems. Modern ranking models increasingly unify sequential modeling and feature interaction, yet many advances rely…
Adaptive Fusion Self-supervised Learning for Recommendation
Yu Zhang, Lei Sang, Yi Zhang +2
The paper proposes Adaptive Fusion Graph Contrastive Learning (AFGCL), a self‑supervised recommendation method that avoids costly graph augmentations by fusing representations from…
ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems
Yi Zhang, Yiwen Zhang, Kai Zheng +2
The remarkable text understanding and generation capabilities of large language models (LLMs) have revitalized the field of general recommendation based on implicit user feedback.…
FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost
Chenhao Feng, Haoli Zhang, Shakhzod Ali-Zade +17
Modern industrial Deep Learning Recommendation Models typically extract user preferences through the analysis of sequential interaction histories, subsequently generating predictio…
Disagreement as Signals: Dual-view Calibration for Sequential Recommendation Denoising
Sijia Li, Min Gao, Zongwei Wang +3
Sequential recommendation seeks to model the evolution of user interests by capturing temporal user intent and item-level transition patterns. Transformer-based recommenders demons…
DIAURec: Dual-Intent Space Representation Optimization for Recommendation
Yu Zhang, Yiwen Zhang, Yi Zhang +1
General recommender systems deliver personalized services by learning user and item representations, with the central challenge being how to capture latent user preferences. Howeve…