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15 papers · 1 filter
CMSL: Constructive Multi-Sequence Learning for Recommendation Systems
Zikun Cui, Renzhi Wu, Junjie Yang +10
Sequence learning has emerged as the promising paradigm in recommendation systems, surpassing traditional Deep Learning Recommendation Models (DLRM) by capturing the temporal nuanc…
A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems
Yiming Zhu, Xu Liu, Ziyun Xu +9
Conventional recommendation systems frequently fail to fully exploit the high-dimensional semantic signals inherent in multimedia content, thereby limiting the fidelity of user pre…
SilverTorch: A Unified Model-based System to Democratize Large-Scale Recommendation on GPUs
Bi Xue, Hong Wu, Lei Chen +29
Serving deep learning based recommendation models (DLRM) at scale is challenging. Existing approaches rely on dedicated ANN indexing and filtering services on CPUs, suffering from…
Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation
Jiang Zhang, Sumit Kumar, Wei Chang +7
The task of item-to-item (I2I) retrieval is to identify a set of relevant and highly engaging items based on a given trigger item. It is a crucial component in modern recommendatio…
HyperZero: A Customized End-to-End Auto-Tuning System for Recommendation with Hourly Feedback
Xufeng Cai, Ziwei Guan, Lei Yuan +11
Modern recommendation systems can be broadly divided into two key stages: the ranking stage, where the system predicts various user engagements (e.g., click-through rate, like rate…
User Welfare Optimization in Recommender Systems with Competing Content Creators
Fan Yao, Yiming Liao, Mingzhe Wu +6
Driven by the new economic opportunities created by the creator economy, an increasing number of content creators rely on and compete for revenue generated from online content reco…