49 citations · 51 across the 4 of their papers we have counts for
4 papers · 1 filter
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction
Hengyu Zhang, Chang Meng, Wei Guo +5
Click-Through Rate (CTR) prediction, crucial in applications like recommender systems and online advertising, involves ranking items based on the likelihood of user clicks. User be…
Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation
Chang Meng, Chenhao Zhai, Yu Yang +2
Multi-behavior recommendation algorithms aim to leverage the multiplex interactions between users and items to learn users' latent preferences. Recent multi-behavior recommendation…
Compressed Interaction Graph based Framework for Multi-behavior Recommendation
Wei Guo, Chang Meng, Enming Yuan +8
Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-fa…
Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation
Chang Meng, Ziqi Zhao, Wei Guo +6
Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users' multi-fac…