信息检索与数据挖掘 2019/3/316 SIGIR 2017 Best Paper BitFunnel:Revisiting Signatures for Search Since the mid-90s there has been a widely-held belief that signature files are inferior to inverted files for text indexing.In recent years the Bing search engine has developed and deployed an index based on bit-sliced signatures.This index,known as BitFunnel,replaced an existing production system based on an inverted index....... The BitFunnel algorithm directly addresses four fundamental limitations in bit-sliced block signatures. At the same time,our mapping of the algorithm onto a cluster offers opportunities to avoid other costs associated with signatures.We show these innovations yield a significant efficiency gain versus classic bit- sliced signatures and then compare BitFunnel with Partitioned Elias-Fano Indexes,MG4J,and Lucene. https://dl.acm.org/citation.cfm?doid=3077136.3080789
信息检索与数据挖掘 2019/3/31 6 SIGIR 2017 Best Paper BitFunnel: Revisiting Signatures for Search • Since the mid-90s there has been a widely-held belief that signature files are inferior to inverted files for text indexing. In recent years the Bing search engine has developed and deployed an index based on bit-sliced signatures. This index, known as BitFunnel, replaced an existing production system based on an inverted index.…… • The BitFunnel algorithm directly addresses four fundamental limitations in bit-sliced block signatures. At the same time, our mapping of the algorithm onto a cluster offers opportunities to avoid other costs associated with signatures. We show these innovations yield a significant efficiency gain versus classic bitsliced signatures and then compare BitFunnel with Partitioned Elias-Fano Indexes, MG4J, and Lucene. https://dl.acm.org/citation.cfm?doid=3077136.3080789
信息检索与数据挖掘 2019/3/317 SIGR2017 Honourable mentions(最佳提名) IRGAN:A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models Jun Wang (University College London),Lantao Yu(Shanghai Jiao Tong University),Weinan Zhang(Shanghai Jiao Tong University),Yu Gong (Alibaba Inc.)Yinghui Xu (Alibaba Inc.)Benyou Wang (Tianjin University),Peng Zhang(Tianjin University),Dell Zhang(Birkbeck, University of London) ·评价指标设计一直是信息检索技术研究中的核心问题之 一 , 而估计用户的期望收益与期望付出则是搜索用户行为模 型的关键组成部分。受模型框架限制，当前几乎所有信息 检索评价指标均无法做到同时将用户的期望收益和付出纳 入会话终上条件的估计。针对这一问题，迁算机系师生受 流行电子游戏"“Bejewed（中文名：宝右迷阵)”机制肩发 设计了一个创新性的用户交互模型框架，将期望收益与 付出因素重新建模，并把现有的绝大多数评价指标纳入这 二框架的范畴。在真实用户行为数据上的实验表明，该框 架比现宥指标能够更好的预测用芦满意程度
信息检索与数据挖掘 2019/3/31 7 SIGIR 2017 Honourable Mentions(最佳提名) • IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models • Jun Wang (University College London), Lantao Yu (Shanghai Jiao Tong University), Weinan Zhang (Shanghai Jiao Tong University), Yu Gong (Alibaba Inc.), Yinghui Xu (Alibaba Inc.), Benyou Wang (Tianjin University), Peng Zhang (Tianjin University), Dell Zhang (Birkbeck, University of London) • 评价指标设计一直是信息检索技术研究中的核心问题之一 ，而估计用户的期望收益与期望付出则是搜索用户行为模 型的关键组成部分。受模型框架限制，当前几乎所有信息 检索评价指标均无法做到同时将用户的期望收益和付出纳 入会话终止条件的估计。针对这一问题，计算机系师生受 流行电子游戏“Bejewed（中文名：宝石迷阵）”机制启发 ，设计了一个创新性的用户交互模型框架，将期望收益与 付出因素重新建模，并把现有的绝大多数评价指标纳入这 一框架的范畴。在真实用户行为数据上的实验表明，该框 架比现有指标能够更好的预测用户满意程度
信息检索与数据挖掘 2019/3/318 SIGIR 2016 Best Paper Understanding Information Need:an fMRI Study In this paper,we investigate the connection between an information need and brain activity.Using functional Magnetic Resonance Imaging (fMRD),we measured the brain activity of twenty four participants while they performed a Question Answering (Q/A)Task,where the questions were carefully selected and developed from TREC-8 and TREC 2001 Q/A Track.The results of this experiment revealed a distributed network of brain regions commonly associated with activities related l to in-formation need and retrieval and differing brain activity in processing scenarios when participants knew the answer to a given question and when they did not and needed to search
信息检索与数据挖掘 2019/3/31 8 SIGIR 2016 Best Paper Understanding Information Need: an fMRI Study • In this paper, we investigate the connection between an information need and brain activity. Using functional Magnetic Resonance Imaging (fMRI), we measured the brain activity of twenty four participants while they performed a Question Answering (Q/A) Task, where the questions were carefully selected and developed from TREC-8 and TREC 2001 Q/A Track. The results of this experiment revealed a distributed network of brain regions commonly associated with activities related to in-formation need and retrieval and differing brain activity in processing scenarios when participants knew the answer to a given question and when they did not and needed to search