19 citations · 32 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
BARS: Towards Open Benchmarking for Recommender Systems
Jieming Zhu, Quanyu Dai, Liangcai Su +5
The past two decades have witnessed the rapid development of personalized recommendation techniques. Despite significant progress made in both research and practice of recommender…
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Yi Li, Jieming Zhu, Weiwen Liu +6
The goal of recommender systems is to provide ordered item lists to users that best match their interests. As a critical task in the recommendation pipeline, re-ranking has receive…