49 citations · 53 across the 7 of their papers we have counts for
7 papers · 1 filter
Heterogeneous Multi-treatment Uplift Modeling for Trade-off Optimization in Short-Video Recommendation
Chenhao Zhai, Chang Meng, Xueliang Wang +5
The rapid proliferation of short videos on social media platforms presents unique challenges and opportunities for recommendation systems. Users exhibit diverse preferences, and th…
Combinatorial Optimization Perspective based Framework for Multi-behavior Recommendation
Chenhao Zhai, Chang Meng, Yu Yang +3
In real-world recommendation scenarios, users engage with items through various types of behaviors. Leveraging diversified user behavior information for learning can enhance the re…
Coarse-to-fine Dynamic Uplift Modeling for Real-time Video Recommendation
Chang Meng, Chenhao Zhai, Xueliang Wang +6
With the rise of short video platforms, video recommendation technology faces more complex challenges. Currently, there are multiple non-personalized modules in the video recommend…
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…