17 citations · 17 across the 6 of their papers we have counts for
6 papers
SOLAR: SVD-Optimized Lifelong Attention for Recommendation
Chenghao Zhang, Chao Feng, Yuanhao Pu +9
Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its quadratic cost in sequence length N makes long-cont…
DUET: Dual Model Co-Training for Entire Space CTR Prediction
Yutian Xiao, Meng Yuan, Fuzhen Zhuang +9
The pre-ranking stage plays a pivotal role in large-scale recommender systems but faces an intrinsic trade-off between model expressiveness and computational efficiency. Owing to t…
Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples
Xiaoxiao Xu, Hao Wu, Wenhui Yu +3
We propose a general model-agnostic Contrastive learning framework with Counterfactual Samples Synthesizing (CCSS) for modeling the monotonicity between the neural network output a…
Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning
Jiakai Tang, Sunhao Dai, Zexu Sun +6
In recent years, graph contrastive learning (GCL) has received increasing attention in recommender systems due to its effectiveness in reducing bias caused by data sparsity. Howeve…
IFA: Interaction Fidelity Attention for Entire Lifelong Behaviour Sequence Modeling
Wenhui Yu, Chao Feng, Yanze Zhang +3
The lifelong user behavior sequence provides abundant information of user preference and gains impressive improvement in the recommendation task, however increases computational co…
RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm
Yabin Zhang, Wenhui Yu, Erhan Zhang +4
ChatGPT has achieved remarkable success in natural language understanding. Considering that recommendation is indeed a conversation between users and the system with items as words…