4 citations · 5 across the 8 of their papers we have counts for
6 papers · 1 filter
DUET: Joint Exploration of User Item Profiles in Recommendation System
Yue Chen, Yifei Sun, Lu Wang +17
Traditional recommendation systems represent users and items as dense vectors and learn to align them in a shared latent space for relevance estimation. Recent LLM-based recommende…
Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator
Jun Yin, Zhengxin Zeng, Mingzheng Li +11
Owing to the unprecedented capability in semantic understanding and logical reasoning, the pre-trained large language models (LLMs) have shown fantastic potential in developing the…
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings
Shitao Xiao, Zheng Liu, Weihao Han +10
Vector quantization (VQ) based ANN indexes, such as Inverted File System (IVF) and Product Quantization (PQ), have been widely applied to embedding based document retrieval thanks…
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval
Shitao Xiao, Zheng Liu, Weihao Han +9
Ad-hoc search calls for the selection of appropriate answers from a massive-scale corpus. Nowadays, the embedding-based retrieval (EBR) becomes a promising solution, where deep lea…
Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search
Jianjin Zhang, Zheng Liu, Weihao Han +9
Embedding based retrieval (EBR) is a fundamental building block in many web applications. However, EBR in sponsored search is distinguished from other generic scenarios and technic…
Learning Fast Matching Models from Weak Annotations
Xue Li, Zhipeng Luo, Hao Sun +5
This paper proposes a novel training scheme for fast matching models in Search Ads, which is motivated by the real challenges in model training. The first challenge stems from the…