12 citations · 16 across the 10 of their papers we have counts for
10 papers
Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems
Pengfei Tong, Siyuan Chen, Chenwei Zhang +4
Most large-scale recommender systems follow a multi-stage cascade of retrieval, pre-ranking, ranking, and re-ranking. A key challenge at the pre-ranking stage arises from the heter…
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…
RankMixer: Scaling Up Ranking Models in Industrial Recommenders
Jie Zhu, Zhifang Fan, Xiaoxie Zhu +18
Recent progress on large language models (LLMs) has spurred interest in scaling up recommendation systems, yet two practical obstacles remain. First, training and serving cost on i…
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…
ContentV: Efficient Training of Video Generation Models with Limited Compute
Wenfeng Lin, Renjie Chen, Boyuan Liu +10
Recent advances in video generation demand increasingly efficient training recipes to mitigate escalating computational costs. In this report, we present ContentV, an 8B-parameter…
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…