6 papers
Action-Aware Generative Sequence Modeling for Short Video Recommendation
Wenhao Li, Zihan Lin, Zhengxiao Guo +7
With the rapid development of the Internet, users have increasingly higher expectations for the recommendation accuracy of online content consumption platforms. However, short vide…
Climber-Pilot: A Non-Myopic Generative Recommendation Model Towards Better Instruction-Following
Da Guo, Shijia Wang, Qiang Xiao +7
Generative retrieval has emerged as a promising paradigm in recommender systems, offering superior sequence modeling capabilities over traditional dual-tower architectures. However…
Hi-SAM: A Hierarchical Structure-Aware Multi-modal Framework for Large-Scale Recommendation
Pingjun Pan, Tingting Zhou, Peiyao Lu +3
Multi-modal recommendation has gained traction as items possess rich attributes like text and images. Semantic ID-based approaches effectively discretize this information into comp…
Scene-wise Adaptive Network for Dynamic Cold-start Scenes Optimization in CTR Prediction
Wenhao Li, Jie Zhou, Chuan Luo +3
In the realm of modern mobile E-commerce, providing users with nearby commercial service recommendations through location-based online services has become increasingly vital. While…
Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
Yinqiu Huang, Shuli Wang, Min Gao +6
Uplift modeling, vital in online marketing, seeks to accurately measure the impact of various strategies, such as coupons or discounts, on different users by predicting the Individ…
HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction
Jie Zhou, Xianshuai Cao, Wenhao Li +4
Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out…