22 citations · 44 across the 13 of their papers we have counts for
7 papers · 1 filter
Hyperspherical Quantization: Toward Smaller and More Accurate Models
Dan Liu, Xi Chen, Chen Ma +1
Model quantization enables the deployment of deep neural networks under resource-constrained devices. Vector quantization aims at reducing the model size by indexing model weights…
Result Diversification in Search and Recommendation: A Survey
Haolun Wu, Yansen Zhang, Chen Ma +4
Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of provide…
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
Haolun Wu, Yingxue Zhang, Chen Ma +4
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations. Many SOTA methods f…
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
Haolun Wu, Chen Ma, Yingxue Zhang +3
Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utiliz…
Gradient-based Bi-level Optimization for Deep Learning: A Survey
Can Chen, Xi Chen, Chen Ma +2
Bi-level optimization, especially the gradient-based category, has been widely used in the deep learning community including hyperparameter optimization and meta-knowledge extracti…
Unbiased Implicit Feedback via Bi-level Optimization
Can Chen, Chen Ma, Xi Chen +3
Implicit feedback is widely leveraged in recommender systems since it is easy to collect and provides weak supervision signals. Recent works reveal a huge gap between the implicit…
Joint Multisided Exposure Fairness for Recommendation
Haolun Wu, Bhaskar Mitra, Chen Ma +2
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of…