5 papers
RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning
Deyi Ji, Yuekui Yang, Liqun Liu +7
Advertising (Ad) is a cornerstone of the digital economy, yet the moderation of video advertisements remains a significant challenge due to their complexity and the need for precis…
RAVEN: Robust Advertisement Video Violation Temporal Grounding via Reinforcement Reasoning
Deyi Ji, Yuekui Yang, Haiyang Wu +3
Advertisement (Ad) video violation detection is critical for ensuring platform compliance, but existing methods struggle with precise temporal grounding, noisy annotations, and lim…
Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising
Tongtong Liu, Zhaohui Wang, Meiyue Qin +4
The integration of Large Language Models (LLMs) with retrieval systems has shown promising potential in retrieving documents (docs) or advertisements (ads) for a given query. Exist…
Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation
Chendi Ge, Xin Wang, Ziwei Zhang +6
Cross-domain recommendation (CDR) mitigates data sparsity and cold-start issues in recommendation systems. While recent CDR approaches using graph neural networks (GNNs) capture co…
AdaS&S: a One-Shot Supernet Approach for Automatic Embedding Size Search in Deep Recommender System
He Wei, Yuekui Yang, Yang Zhang +3
Deep Learning Recommendation Model(DLRM)s utilize the embedding layer to represent various categorical features. Traditional DLRMs adopt unified embedding size for all features, le…