2 papers
cs.IR2024
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation
Jun Wang, Haoxuan Li, Chi Zhang +4
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 (pCV…
cs.IR2023
Exploring and Exploiting Data Heterogeneity in Recommendation
Zimu Wang, Jiashuo Liu, Hao Zou +4
Massive amounts of data are the foundation of data-driven recommendation models. As an inherent nature of big data, data heterogeneity widely exists in real-world recommendation sy…