2 papers
cs.IR2025
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…
cs.IR2025
An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
Tiantian He, Minzhi Xie, Runtong Li +6
We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short v…