49 citations · 164 across the 15 of their papers we have counts for
15 papers
Model-enhanced Vector Index
Hailin Zhang, Yujing Wang, Qi Chen +16
Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in docume…
Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference
Junyan Li, Li Lyna Zhang, Jiahang Xu +9
Deploying pre-trained transformer models like BERT on downstream tasks in resource-constrained scenarios is challenging due to their high inference cost, which grows rapidly with i…
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Rui Li, Xu Chen, Chaozhuo Li +8
Embedding models have shown great power in knowledge graph completion (KGC) task. By learning structural constraints for each training triple, these methods implicitly memorize int…
IRGen: Generative Modeling for Image Retrieval
Yidan Zhang, Ting Zhang, Dong Chen +11
While generative modeling has become prevalent across numerous research fields, its integration into the realm of image retrieval remains largely unexplored and underjustified. In…
UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation
Daixuan Cheng, Shaohan Huang, Junyu Bi +7
Large Language Models (LLMs) are popular for their impressive abilities, but the need for model-specific fine-tuning or task-specific prompt engineering can hinder their generaliza…
A Neural Corpus Indexer for Document Retrieval
Yujing Wang, Yingyan Hou, Haonan Wang +13
Current state-of-the-art document retrieval solutions mainly follow an index-retrieve paradigm, where the index is hard to be directly optimized for the final retrieval target. In…
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…
HousE: Knowledge Graph Embedding with Householder Parameterization
Rui Li, Jianan Zhao, Chaozhuo Li +9
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties. However, existing approaches can on…
Gophormer: Ego-Graph Transformer for Node Classification
Jianan Zhao, Chaozhuo Li, Qianlong Wen +5
Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area,…
Localized Graph Collaborative Filtering
Yiqi Wang, Chaozhuo Li, Mingzheng Li +5
User-item interactions in recommendations can be naturally de-noted as a user-item bipartite graph. Given the success of graph neural networks (GNNs) in graph representation learni…
AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search
Chaozhuo Li, Bochen Pang, Yuming Liu +7
Sponsored search ads appear next to search results when people look for products and services on search engines. In recent years, they have become one of the most lucrative channel…
TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search
Jason Yue Zhu, Yanling Cui, Yuming Liu +7
Text encoders based on C-DSSM or transformers have demonstrated strong performance in many Natural Language Processing (NLP) tasks. Low latency variants of these models have also b…
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…