19 citations · 32 across the 7 of their papers we have counts for
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cs.IR2023
Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation
Liangcai Su, Fan Yan, Jieming Zhu +5
Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications. The success of two-tower matching attributes to…
cs.IR2023★ 2 cited
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
Liangcai Su, Junwei Pan, Ximei Wang +4
Multi-task learning (MTL) has gained significant popularity in recommender systems as it enables simultaneous optimization of multiple objectives. A key challenge in MTL is negativ…
cs.IR2023★ 1 cited
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
Kelong Mao, Jieming Zhu, Liangcai Su +3
Click-through rate (CTR) prediction is one of the fundamental tasks for online advertising and recommendation. While multi-layer perceptron (MLP) serves as a core component in many…