5 citations · 14 across the 6 of their papers we have counts for
6 papers
Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation
Yizhou Dang, Enneng Yang, Guibing Guo +5
Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transiti…
Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction
Xiaoxiao Xu, Zhiwei Fang, Qian Yu +7
The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction. However, the existing methods for exposure sequence modeling brin…
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
Xiaoxiao Xu, Chen Yang, Qian Yu +7
We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via alle…
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling
Bei Yang, Jie Gu, Ke Liu +4
User Modeling plays an essential role in industry. In this field, task-agnostic approaches, which generate general-purpose representation applicable to diverse downstream user cogn…
Interest-oriented Universal User Representation via Contrastive Learning
Qinghui Sun, Jie Gu, Bei Yang +5
User representation is essential for providing high-quality commercial services in industry. Universal user representation has received many interests recently, with which we can b…
Exploiting Behavioral Consistence for Universal User Representation
Jie Gu, Feng Wang, Qinghui Sun +4
User modeling is critical for developing personalized services in industry. A common way for user modeling is to learn user representations that can be distinguished by their inter…