13 citations · 20 across the 7 of their papers we have counts for
6 papers · 1 filter
PushDualGen: Enabling LLMs to Generate Semantic IDs with Interpretable Copy for Industrial Push Recommendation
Manjia Lin, Da Li, Yan Wang +9
Push recommendation in KuaiShou proactively delivers personalized content to nearly one billion users to facilitate their engagement. Recently, generative recommendation has achiev…
HGenPush: A Heterogeneous Generative Recommendation Architecture for Industrial Push Notification Systems
Xiao Liang, Jiali Feng, Xin Feng +10
With the explosive growth of content platforms, recommendation systems need to better satisfy user demands to enhance user satisfaction and retention. Taking short-video platforms…
PushGen: Push Notifications Generation with LLM
Shifu Bie, Jiangxia Cao, Zixiao Luo +9
We present PushGen, an automated framework for generating high-quality push notifications comparable to human-crafted content. With the rise of generative models, there is growing…
TIM: Temporal Interaction Model in Notification System
Huxiao Ji, Haitao Yang, Linchuan Li +4
Modern mobile applications heavily rely on the notification system to acquire daily active users and enhance user engagement. Being able to proactively reach users, the system has…
Query-dominant User Interest Network for Large-Scale Search Ranking
Tong Guo, Xuanping Li, Haitao Yang +9
Historical behaviors have shown great effect and potential in various prediction tasks, including recommendation and information retrieval. The overall historical behaviors are var…
Contrastive Learning for Cold-Start Recommendation
Yinwei Wei, Xiang Wang, Qi Li +4
Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use co…