7 citations · 8 across the 4 of their papers we have counts for
4 papers
DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models
Wuchao Li, Rui Huang, Haijun Zhao +10
Sequential Recommendation (SR) plays a pivotal role in recommender systems by tailoring recommendations to user preferences based on their non-stationary historical interactions. A…
A Self-boosted Framework for Calibrated Ranking
Shunyu Zhang, Hu Liu, Wentian Bao +2
Scale-calibrated ranking systems are ubiquitous in real-world applications nowadays, which pursue accurate ranking quality and calibrated probabilistic predictions simultaneously.…
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…
Query-dominant User Interest Network for Large-Scale Search Ranking
Tong Guo, Xuanping Li, Haitao Yang +9
Historical behaviors have shown great effect and potential in various prediction tasks, including recommendation and information retrieval. The overall historical behaviors are var…