5 papers · 1 filter
Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation
Kai Cheng, Hao Wang, Wei Guo +4
Generative models offer a promising paradigm for the final stage reranking in multi-stage recommender systems, with the ability to capture inter-item dependencies within reranked l…
Rethinking Purity and Diversity in Multi-Behavior Sequential Recommendation from the Frequency Perspective
Yongqiang Han, Kai Cheng, Kefan Wang +1
In recommendation systems, users often exhibit multiple behaviors, such as browsing, clicking, and purchasing. Multi-behavior sequential recommendation (MBSR) aims to consider thes…
A Universal Framework for Compressing Embeddings in CTR Prediction
Kefan Wang, Hao Wang, Kenan Song +6
Accurate click-through rate (CTR) prediction is vital for online advertising and recommendation systems. Recent deep learning advancements have improved the ability to capture feat…
Scaling New Frontiers: Insights into Large Recommendation Models
Wei Guo, Hao Wang, Luankang Zhang +16
Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate inc…
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
Hao Wang, Yongqiang Han, Kefan Wang +6
In the realm of recommendation systems, users exhibit a diverse array of behaviors when interacting with items. This phenomenon has spurred research into learning the implicit sema…