6 papers
Generative End-to-end Ad Retrieval at Douyin
Shaowen Zeng, Yanhua Huang, Jiacheng Sun +8
Generative retrieval reformulates recommendation as the generation of discrete item tokens. However, scaling this paradigm to real-world recommender systems reveals two critical bo…
IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshu
Yubin Zhang, Haiming Xu, Guillaume Salha-Galvan +6
Content-driven platforms such as Xiaohongshu often leverage click-through rate (CTR) prediction models for recommendation. However, these models depend heavily on item ID embedding…
Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units
Chao Hao, Zezheng Wang, Yanhua Huang +4
This paper investigates the enhancement of reasoning capabilities in language models through token-level multi-model collaboration. Our approach selects the optimal tokens from the…
Beyond Quality: Unlocking Diversity in Ad Headline Generation with Large Language Models
Chang Wang, Siyu Yan, Depeng Yuan +8
The generation of ad headlines plays a vital role in modern advertising, where both quality and diversity are essential to engage a broad range of audience segments. Current approa…
A Metric for MLLM Alignment in Large-scale Recommendation
Yubin Zhang, Yanhua Huang, Haiming Xu +6
Multimodal recommendation has emerged as a critical technique in modern recommender systems, leveraging content representations from advanced multimodal large language models (MLLM…
Look into the Future: Deep Contextualized Sequential Recommendation
Lei Zheng, Ning Li, Yanhuan Huang +3
Sequential recommendation aims to estimate how a user's interests evolve over time via uncovering valuable patterns from user behavior history. Many previous sequential models have…