Search Results for author:
Found 2 papers, 0 papers with code
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation
Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction.
Exploring and Exploiting Data Heterogeneity in Recommendation
In this work, we focus on exploring two representative categories of heterogeneity in recommendation data that is the heterogeneity of prediction mechanism and covariate distribution and propose an algorithm that explores the heterogeneity through a bilevel clustering method.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
