35 citations · 88 across the 11 of their papers we have counts for
11 papers
Towards Automated Negative Sampling in Implicit Recommendation
Fuyuan Lyu, Yaochen Hu, Xing Tang +3
Negative sampling methods are vital in implicit recommendation models as they allow us to obtain negative instances from massive unlabeled data. Most existing approaches focus on s…
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
Bowei He, Xu He, Renrui Zhang +3
With the continuous increase of users and items, conventional recommender systems trained on static datasets can hardly adapt to changing environments. The high-throughput data req…
Dynamically Expandable Graph Convolution for Streaming Recommendation
Bowei He, Xu He, Yingxue Zhang +2
Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs. However, conventional recommendation models…
Compressed Interaction Graph based Framework for Multi-behavior Recommendation
Wei Guo, Chang Meng, Enming Yuan +8
Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-fa…
A Survey on User Behavior Modeling in Recommender Systems
Zhicheng He, Weiwen Liu, Wei Guo +4
User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and…
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…
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
Fuyuan Lyu, Xing Tang, Hong Zhu +4
Learning embedding table plays a fundamental role in Click-through rate(CTR) prediction from the view of the model performance and memory usage. The embedding table is a two-dimens…
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…
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation
Yankai Chen, Huifeng Guo, Yingxue Zhang +4
Learning vectorized embeddings is at the core of various recommender systems for user-item matching. To perform efficient online inference, representation quantization, aiming to e…
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
Yankai Chen, Yifei Zhang, Yingxue Zhang +5
Due to the promising advantages in space compression and inference acceleration, quantized representation learning for recommender systems has become an emerging research direction…
Content Filtering Enriched GNN Framework for News Recommendation
Yong Gao, Huifeng Guo, Dandan Lin +3
Learning accurate users and news representations is critical for news recommendation. Despite great progress, existing methods seem to have a strong bias towards content representa…