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20182023
most citedEnhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation

64 citations · 143 across the 17 of their papers we have counts for

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12 papers · 1 filter

cs.IR2023

Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction

Canjia Li, Xiaoyang Wang, Dongdong Li +6

Search engine plays a crucial role in satisfying users' diverse information needs. Recently, Pretrained Language Models (PLMs) based text ranking models have achieved huge success…

cs.IR2023★ 1 cited

I^3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval

Qian Dong, Yiding Liu, Qingyao Ai +5

Passage retrieval is a fundamental task in many information systems, such as web search and question answering, where both efficiency and effectiveness are critical concerns. In re…

cs.IR2023★ 2 cited

Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies

Yubao Tang, Ruqing Zhang, Jiafeng Guo +5

Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for document retrieval, wherein a sequence-to-sequence model is learned to directly map queries…

cs.IR2023★ 16 cited

Learning to Tokenize for Generative Retrieval

Weiwei Sun, Lingyong Yan, Zheng Chen +7

Conventional document retrieval techniques are mainly based on the index-retrieve paradigm. It is challenging to optimize pipelines based on this paradigm in an end-to-end manner.…

cs.IR2023

Layout-aware Webpage Quality Assessment

Anfeng Cheng, Yiding Liu, Weibin Li +6

Identifying high-quality webpages is fundamental for real-world search engines, which can fulfil users' information need with the less cognitive burden. Early studies of \emph{webp…

cs.IR2022★ 4 cited

Approximated Doubly Robust Search Relevance Estimation

Lixin Zou, Changying Hao, Hengyi Cai +6

Extracting query-document relevance from the sparse, biased clickthrough log is among the most fundamental tasks in the web search system. Prior art mainly learns a relevance judgm…

cs.IR2022★ 5 cited

A Simple yet Effective Framework for Active Learning to Rank

Qingzhong Wang, Haifang Li, Haoyi Xiong +7

While China has become the biggest online market in the world with around 1 billion internet users, Baidu runs the world largest Chinese search engine serving more than hundreds of…

cs.IR2022

Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking

Qian Dong, Yiding Liu, Suqi Cheng +4

Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their ov…

cs.IR2022

Graph Enhanced BERT for Query Understanding

Juanhui Li, Yao Ma, Wei Zeng +4

Query understanding plays a key role in exploring users' search intents and facilitating users to locate their most desired information. However, it is inherently challenging since…

cs.IR2021★ 1 cited

Pre-trained Language Model based Ranking in Baidu Search

Lixin Zou, Shengqiang Zhang, Hengyi Cai +8

As the heart of a search engine, the ranking system plays a crucial role in satisfying users' information demands. More recently, neural rankers fine-tuned from pre-trained languag…

cs.IR2021★ 4 cited

Pre-trained Language Model for Web-scale Retrieval in Baidu Search

Yiding Liu, Guan Huang, Jiaxiang Liu +7

Retrieval is a crucial stage in web search that identifies a small set of query-relevant candidates from a billion-scale corpus. Discovering more semantically-related candidates in…

cs.IR2018★ 3 cited

Etymo: A New Discovery Engine for AI Research

Weijian Zhang, Jonathan Deakin, Nicholas J. Higham +1

We present Etymo (https://etymo.io), a discovery engine to facilitate artificial intelligence (AI) research and development. It aims to help readers navigate a large number of AI-r…