787 citations · 1.8k across the 39 of their papers we have counts for
14 papers · 1 filter
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
Zeyu Cui, Jianxin Ma, Chang Zhou +2
Industrial recommender systems have been growing increasingly complex, may involve \emph{diverse domains} such as e-commerce products and user-generated contents, and can comprise…
Deep Unified Representation for Heterogeneous Recommendation
Chengqiang Lu, Mingyang Yin, Shuheng Shen +3
Recommendation system has been a widely studied task both in academia and industry. Previous works mainly focus on homogeneous recommendation and little progress has been made for…
Click-through Rate Prediction with Auto-Quantized Contrastive Learning
Yujie Pan, Jiangchao Yao, Bo Han +3
Click-through rate (CTR) prediction becomes indispensable in ubiquitous web recommendation applications. Nevertheless, the current methods are struggling under the cold-start scena…
Dynamic Sequential Graph Learning for Click-Through Rate Prediction
Yunfei Chu, Xiaofu Chang, Kunyang Jia +2
Click-through rate prediction plays an important role in the field of recommender system and many other applications. Existing methods mainly extract user interests from user histo…
MC-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation
Zeyuan Chen, Jiangchao Yao, Feng Wang +4
With the hardware development of mobile devices, it is possible to build the recommendation models on the mobile side to utilize the fine-grained features and the real-time feedbac…
Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation
Luo Ji, Qin Qi, Bingqing Han +1
Recommender system plays a crucial role in modern E-commerce platform. Due to the lack of historical interactions between users and items, cold-start recommendation is a challengin…