12 citations · 15 across the 10 of their papers we have counts for
10 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…
Make It Long, Keep It Fast: End-to-End 10K Long User Behavior Sequence Modeling for Billion-Scale Douyin Recommendation
Lin Guan, Jia-Qi Yang, Zhishan Zhao +12
Short-video recommenders such as Douyin must exploit extremely long user behavior histories without breaking latency or cost budgets. We present an end-to-end industrial recommende…
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…
Pyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation
Zhen Gong, Zhifang Fan, Hui Lu +7
Sequential recommendation, a critical task in recommendation systems, predicts the next user action based on the understanding of the user's historical behaviors. Conventional stud…
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…
Real-time Indexing for Large-scale Recommendation by Streaming Vector Quantization Retriever
Xingyan Bin, Jianfei Cui, Wujie Yan +7
Retrievers, which form one of the most important recommendation stages, are responsible for efficiently selecting possible positive samples to the later stages under strict latency…
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…
Trinity: Syncretizing Multi-/Long-tail/Long-term Interests All in One
Jing Yan, Liu Jiang, Jianfei Cui +4
Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e.g. multi-interest, long-tail interest and lo…