From the 1 of 12 linked papers with an AI index.
12 papers
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.…
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…
FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction
Honghao Li, Yiwen Zhang, Yi Zhang +3
As an important modeling paradigm in click-through rate (CTR) prediction, the Deep & Cross Network (DCN) and its derivative models have gained widespread recognition primarily due…
ProEx: A Unified Framework Leveraging Large Language Model with Profile Extrapolation for Recommendation
Yi Zhang, Yiwen Zhang, Yu Wang +2
The powerful text understanding and generation capabilities of large language models (LLMs) have brought new vitality to general recommendation with implicit feedback. One possible…
Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction
Honghao Li, Yiwen Zhang, Yi Zhang +2
Hadamard Product (HP) has long been a cornerstone in click-through rate (CTR) prediction tasks due to its simplicity, effectiveness, and ability to capture feature interactions wit…