1 citations · 1 across the 4 of their papers we have counts for
4 papers
Pre-train and Fine-tune: Recommenders as Large Models
Zhenhao Jiang, Chenghao Chen, Hao Feng +5
In reality, users have different interests in different periods, regions, scenes, etc. Such changes in interest are so drastic that they are difficult to be captured by recommender…
DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model
Zhenhao Jiang, Jicong Fan
Although recommenders can ship items to users automatically based on the users' preferences, they often cause unfairness to groups or individuals. For instance, when users can be d…
Mobile Supply: The Last Piece of Jigsaw of Recommender System
Zhenhao Jiang, Biao Zeng, Hao Feng +4
Recommendation system is a fundamental functionality of online platforms. With the development of computing power of mobile phones, some researchers have deployed recommendation al…
ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint
Zhenhao Jiang, Biao Zeng, Hao Feng +7
Large-scale online recommender system spreads all over the Internet being in charge of two basic tasks: Click-Through Rate (CTR) and Post-Click Conversion Rate (CVR) estimations. H…