7 citations · 14 across the 9 of their papers we have counts for
4 papers · 1 filter
Cross Domain LifeLong Sequential Modeling for Online Click-Through Rate Prediction
Ruijie Hou, Zhaoyang Yang, Yu Ming +5
Deep neural networks (DNNs) that incorporated lifelong sequential modeling (LSM) have brought great success to recommendation systems in various social media platforms. While conti…
Scaling Law of Large Sequential Recommendation Models
Gaowei Zhang, Yupeng Hou, Hongyu Lu +3
Scaling of neural networks has recently shown great potential to improve the model capacity in various fields. Specifically, model performance has a power-law relationship with mod…
Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
Bowen Zheng, Yupeng Hou, Hongyu Lu +4
Recently, large language models (LLMs) have shown great potential in recommender systems, either improving existing recommendation models or serving as the backbone. However, there…
Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data
Qi-Wei Wang, Hongyu Lu, Yu Chen +4
The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. S…