12 citations · 13 across the 7 of their papers we have counts for
7 papers
Bridging Passive and Active: Enhancing Conversation Starter Recommendation via Active Expression Modeling
Yiqing Wu, Haoming Li, Guanyu Jiang +4
Large Language Model (LLM)-driven conversational search is shifting information retrieval from reactive keyword matching to proactive, open-ended dialogues. In this context, Conver…
MuChator: Enabling Active Music Discovery via Conversational Music LLMs in Douyin Music
Jiahao Liang, Linzhi Huang, Xuannan Liu +6
Douyin Music, a large-scale platform with millions of daily users, adopts an immersive, feed-based discovery paradigm, where users passively explore music through continuous recomm…
IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters
Hongwei Zheng, Weiqi Wu, Zhengjia Wang +6
Conversational agents, such as ChatGPT and Doubao, have become essential daily assistants for billions of users. To further enhance engagement, these systems are evolving from pass…
Asymmetric Diffusion Recommendation Model
Yongchun Zhu, Guanyu Jiang, Jingwu Chen +3
Recently, motivated by the outstanding achievements of diffusion models, the diffusion process has been employed to strengthen representation learning in recommendation systems. Mo…
Long-Term Interest Clock: Fine-Grained Time Perception in Streaming Recommendation System
Yongchun Zhu, Guanyu Jiang, Jingwu Chen +3
User interests manifest a dynamic pattern within the course of a day, e.g., a user usually favors soft music at 8 a.m. but may turn to ambient music at 10 p.m. To model dynamic int…
AdaF^2M^2: Comprehensive Learning and Responsive Leveraging Features in Recommendation System
Yongchun Zhu, Jingwu Chen, Ling Chen +4
Feature modeling, which involves feature representation learning and leveraging, plays an essential role in industrial recommendation systems. However, the data distribution in rea…
Interest Clock: Time Perception in Real-Time Streaming Recommendation System
Yongchun Zhu, Jingwu Chen, Ling Chen +3
User preferences follow a dynamic pattern over a day, e.g., at 8 am, a user might prefer to read news, while at 8 pm, they might prefer to watch movies. Time modeling aims to enabl…