Search Results for author:
Found 3 papers, 1 papers with code
Treatment Effect Estimation for User Interest Exploration on Recommender Systems
The empirical results validate the effectiveness of UpliftRec in discovering users' hidden interests while achieving superior recommendation accuracy.
Multiple Interest and Fine Granularity Network for User Modeling
User modeling plays a fundamental role in industrial recommender systems, either in the matching stage and the ranking stage, in terms of both the customer experience and business revenue.
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce
In this paper, we present a novel Generator and Critic slate re-ranking approach, where the Critic evaluates the slate and the Generator ranks the items by the reinforcement learning approach.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
