{"id":489929,"date":"2018-06-11T09:08:49","date_gmt":"2018-06-11T16:08:49","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/?post_type=msr-event&p=489929"},"modified":"2025-08-06T11:57:08","modified_gmt":"2025-08-06T18:57:08","slug":"microsoft-sigir-2018","status":"publish","type":"msr-event","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/event\/microsoft-sigir-2018\/","title":{"rendered":"Microsoft @ SIGIR 2018"},"content":{"rendered":"\n\n

Venue:<\/strong> Michigan League (opens in new tab)<\/span><\/a> (Location on Campus Map (opens in new tab)<\/span><\/a>)<\/p>\n

Website:<\/strong> SIGIR 2018 (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n

SIGIR is a major international forum for presentation of the latest state-of-the-art research and demonstration of new systems and methods for connecting people with information: from Web search engines, recommender systems, and social network technology to compelling applications in health, legal, educational, and other domains, research at SIGIR spans both academia and industry.<\/p>\n

Program Committee members<\/h2>\n

Paul Bennett (opens in new tab)<\/span><\/a>, Short Paper Chair
\nJianfeng Gao (opens in new tab)<\/span><\/a>, AI Track Co-chair<\/p>\n

Invited Speakers<\/h2>\n

Distributional Representation of Complex Semantics<\/strong> (Keynote at KG4IR workshop (opens in new tab)<\/span><\/a>)
\nKuansan Wang (opens in new tab)<\/span><\/a>, Microsoft Research<\/p>\n

Lessons from Building a Large-scale Commercial IR-based Chatbot for an Emerging Market<\/strong>
\nPuneet Agrawal and Manoj Kumar Chinnakotla, Microsoft<\/p>\n

Causal Inference over Longitudinal Data to Support Expectation Exploration<\/strong>
\nEmre Kiciman (opens in new tab)<\/span><\/a>, Microsoft Research<\/p>\n

Search and Recommendation in the Enterprise<\/strong> (opens in new tab)<\/span><\/a>
\nPaul Bennett (opens in new tab)<\/span><\/a>, Microsoft Research<\/p>\n

Workshops<\/h2>\n

Learning from Limit\/Noisy data for IR (opens in new tab)<\/span><\/a>
\nHamed Zamani (UMass Amherst), Mostafa Dehghani (Univ. of Amsterdam), Fernando Diaz (Microsoft Research \u2013 Montreal), Hang Li (Toutiao AI Lab), Nick Craswell (Microsoft)<\/p>\n

Microsoft attendees<\/h2>\n

Amjad Abu-Jbara, Microsoft
\nOmar Alonso, Microsoft
\nAhmed Awadallah (opens in new tab)<\/span><\/a>, Microsoft Research
\nPaul Bennett (opens in new tab)<\/span><\/a>, Microsoft Research
\nEdward Cui, Microsoft
\nWeiwei Deng, Microsoft
\nFernando Diaz, Microsoft Research \u2013 Montreal
\nSusan Dumais (opens in new tab)<\/span><\/a>, Microsoft Research
\nAdam Fourney (opens in new tab)<\/span><\/a>, Microsoft Research AI
\nEmre Kiciman (opens in new tab)<\/span><\/a>, Microsoft Research
\nXiaoliang Ling, Microsoft
\nPawel Pietrusinski, Microsoft
\nMona Soliman Habib, Microsoft
\nHui Su, Microsoft<\/p>\n

Career Opportunities<\/h2>\n

ML Engineer (opens in new tab)<\/span><\/a><\/h4>\n

AI & Research (AI&R) at Hyderabad, India comprises of highly motivated researchers, engineers, product managers and data-scientists building end-to-end web-scale and enterprise-scale AI systems. We seek talented, energetic, creative and passionate ML engineers with ability to enhance and apply research to ship and build high-quality products and services.<\/p>\n

Opens in a new tab<\/span><\/p>\n

Full Papers<\/h2>\n

Calendar-Aware Proactive Email Recommendation<\/strong>
\nQian Zhao (University of Minnesota); Paul Bennett (Microsoft); Adam Fourney (Microsoft); Anne Thompson (Microsoft); Shane Williams (Microsoft); Adam D. Troy (Microsoft); Susan Dumais (Microsoft)<\/p>\n

Characterizing and Supporting Question Answering in Human-to-Human Communication<\/strong>
\nXiao Yang (The Pennsylvania State University); Ahmed Hassan Awadallah (Microsoft); Madian Khabsa (Apple); Wei Wang (Microsoft); Miaosen Wang (Microsoft)<\/p>\n

Deep Domain Adaptation Hashing with Adversarial Learning<\/strong>
\nFuchen Long (University of Science and Technology of China); Ting Yao (Microsoft); Qi Dai (Microsoft); Xinmei Tian (University of Science and Technology of China); Jiebo Luo (University of Rochester); Tao Mei (Microsoft)<\/p>\n

Measuring the Utility of Search Engine Result Pages<\/strong>
\nLeif Azzopardi (University of Strathclyde); Paul Thomas (Microsoft); Nick Craswell (Microsoft)<\/p>\n

Natural Language Interfaces with Fine-Grained User Interaction: A Case Study on Web APIs<\/strong>
\nYu Su (University of California Santa Barbara); Ahmed Hassan Awadallah (Microsoft); Miaosen Wang (Microsoft); Ryen White (Microsoft)<\/p>\n

Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling<\/strong>
\nChenyan Xiong (Carnegie Mellon University); Zhengzhong Liu (Carnegie Mellon University); Jamie Callan (Carnegie Mellon University); Tie-Yan Liu (Microsoft)<\/p>\n

Short Papers<\/h2>\n

Ad Click Prediction in sequence with Long Short-Term Memory Networks: An externality-aware model<\/strong>
\nWeiwei Deng (Microsoft); Xiaoliang Ling (Microsoft); Yang Qi (Microsoft); Tunzi Tan (School of Mathematical Sciences @ University of Chinese Academy of Sciences); Eren Manavoglu (Microsoft); Qi Zhang (Microsoft)<\/p>\n

Assessing the Readability of Web Search Results for Searchers with Dyslexia<\/strong>
\nAdam Fourney (Microsoft); Meredith Ringel Morris (Microsoft); Abdullah Ali (University of Washington); Laura Vonessen (University of Washington)<\/p>\n

Attention-driven Factor Model for Explainable Personalized Recommendation<\/strong>
\nJingwu Chen (Institute of Computing Technology, Chinese Academy of Sciences); Fuzhen Zhuang (Institute of Computing Technology, Chinese Academy of Sciences); Xin Hong (Institute of Computing Technology, Chinese Academy of Sciences); Xiang Ao (Institute of Computing Technology, Chinese Academy of Sciences); Xing Xie (Microsoft); Qing He (Institute of Computing Technology, Chinese Academy of Sciences)<\/p>\n

Cross Domain Regularization for Neural Ranking Models using Adversarial Learning<\/strong>
\nDaniel Cohen (University of Massachusetts Amherst); Bhaskar Mitra (Microsoft); Katja Hofmann (Microsoft); Bruce Croft (University of Massachusetts Amherst)<\/p>\n

Multi-level Abstraction Convolutional Model with Weak Supervision for Information Retrieval<\/strong>
\nYifan Nie (University of Montreal); Alessandro Sordoni (Maluuba \u2013 Microsoft); Jian-Yun Nie (University of Montreal)<\/p>\n

Optimizing Query Evaluations using Reinforcement Learning for Web Search<\/strong>
\nCorby Rosset (Microsoft); Damien Jose (Microsoft); Gargi Ghosh (Microsoft); Bhaskar Mitra (Microsoft); Saurabh Tiwary (Microsoft)<\/p>\n

Quantitative Information Extraction From Social Data<\/strong>
\nOmar Alonso (Microsoft); Thibault Sellam (Columbia University)<\/p>\n

Testing the Cluster Hypothesis with Focused and Graded Relevance Judgments<\/strong>
\nEilon Sheetrit (Technion \u2013 Israel Institute of Technology); Anna Shtok (Technion \u2013 Israel Institute of Technology); Oren Kurland (Technion, Israel Institute of Technology); Igal Shprincis (Microsoft, Herzliya, Israel)<\/p>\n

Transparent Tree Ensembles<\/strong>
\nAlexander Moore (Microsoft); Vanessa Murdock (Microsoft); Yaxiong Cai (Microsoft); Kristine Jones (Microsoft)<\/p>\n

SIRIP Industry Papers<\/h2>\n

Puneet Agrawal and Manoj Kumar Chinnakotla. Lessons from Building a Large-scale Commercial IR-based Chatbot for an Emerging Market<\/strong>
\nPuneet Agrawal (Microsoft); Manoj Kumar Chinnakotla (Microsoft)Opens in a new tab<\/span><\/p>\n

The Microsoft Academic Graph<\/a> makes it possible to gain analytic insights about any of the entities within it: publications, authors (opens in new tab)<\/span><\/a>, institutions (opens in new tab)<\/span><\/a>, topics (opens in new tab)<\/span><\/a>, journals (opens in new tab)<\/span><\/a>, and conferences (opens in new tab)<\/span><\/a>. Below, we present historical trend analysis about the SIGIR\u2013 Special Interest Group on Information Retrieval\u2013Conference.<\/p>\n

You can generate your own insights by accessing the Microsoft Academic Graph through the Academic Knowledge API (opens in new tab)<\/span><\/a> or through Azure Data Lake Store (opens in new tab)<\/span><\/a> (please contact us<\/a> for the latter option). If you would like to learn how we generated the insights below, please see the repository with source code (opens in new tab)<\/span><\/a>.<\/p>\n

Click on each image for current trends and data hosted by Microsoft Academic Graph<\/a>.<\/em><\/p>\n

SIGIR paper output<\/h2>\n

The chart below shows the evolution of the number of conference papers for each conference year.<\/p>\n

\"SIGIR (opens in new tab)<\/span><\/a><\/p>\n

In the following chart, the black bars represent average numbers of references per conference paper for each year. The data show that recent publications tend to cite more references. The green bars show the average number of citations of conference papers written in a given year. Note that the citations are raw counts and not normalized by the age of publications. This is because the \u201ccorrect\u201d way to normalize the citation counts turns out to be a nontrivial problem and may well be application dependent. Please treat the raw data presented as an invitation to conduct research on this topic!<\/p>\n

\"\" (opens in new tab)<\/span><\/a><\/p>\n

That being said, a visible trend is that older publications tend to receive more citations because they have more time for researchers to recognize the contributions of the paper. There are, however, notable exceptions, the first in 1994, due to several highly cited papers:<\/p>\n