�ݺ�ߣshows by User: marcCsloan

�ݺ�ߣshows by User: marcCsloan / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: marcCsloan / Sat, 05 Jul 2014 08:12:48 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: marcCsloan Dynamic Information Retrieval Tutorial - SIGIR 2015 /slideshow/dynamic-information-retrieval-tutorial/36653675 dynamicinformationretrievalmodelingslides-140705081248-phpapp01
Dynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and information filtering systems. Examples include large datasets containing sequential data capturing document dynamics and modern IR systems observing user dynamics through interactivity. Existing IR techniques are limited in their ability to optimize over changes, learn with minimal computational footprint and be responsive and adaptive. The objective of this tutorial is to provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling. Dynamic IR Modeling is the statistical modeling of IR systems that can adapt to change. It is a natural follow-up to previous statistical IR modeling tutorials with a fresh look on state-of-the-art dynamic retrieval models and their applications including session search and online advertising. The tutorial covers techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and presents to fellow researchers and practitioners a handful of useful algorithms and tools for solving IR problems incorporating dynamics. http://www.dynamic-ir-modeling.org/ A newer version of this tutorial presented at WSDM 2015 can be found here http://www.slideshare.net/marcCsloan/dynamic-information-retrieval-tutorial-wsdm-2015 This version has a greater emphasis on the underlying theory and a guest lecture on evaluation by Dr Emine Yilmaz. The newer version presents a wider range of applications of DIR in state of the art research and includes a guest lecture on evaluation by Prof Charles Clarke. @inproceedings{Yang:2014:DIR:2600428.2602297, author = {Yang, Hui and Sloan, Marc and Wang, Jun}, title = {Dynamic Information Retrieval Modeling}, booktitle = {Proceedings of the 37th International ACM SIGIR Conference on Research \&\#38; Development in Information Retrieval}, series = {SIGIR '14}, year = {2014}, isbn = {978-1-4503-2257-7}, location = {Gold Coast, Queensland, Australia}, pages = {1290--1290}, numpages = {1}, url = {http://doi.acm.org/10.1145/2600428.2602297}, doi = {10.1145/2600428.2602297}, acmid = {2602297}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {dynamic information retrieval modeling, probabilistic relevance model, reinforcement learning}, }]]>
Dynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and information filtering systems. Examples include large datasets containing sequential data capturing document dynamics and modern IR systems observing user dynamics through interactivity. Existing IR techniques are limited in their ability to optimize over changes, learn with minimal computational footprint and be responsive and adaptive. The objective of this tutorial is to provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling. Dynamic IR Modeling is the statistical modeling of IR systems that can adapt to change. It is a natural follow-up to previous statistical IR modeling tutorials with a fresh look on state-of-the-art dynamic retrieval models and their applications including session search and online advertising. The tutorial covers techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and presents to fellow researchers and practitioners a handful of useful algorithms and tools for solving IR problems incorporating dynamics. http://www.dynamic-ir-modeling.org/ A newer version of this tutorial presented at WSDM 2015 can be found here http://www.slideshare.net/marcCsloan/dynamic-information-retrieval-tutorial-wsdm-2015 This version has a greater emphasis on the underlying theory and a guest lecture on evaluation by Dr Emine Yilmaz. The newer version presents a wider range of applications of DIR in state of the art research and includes a guest lecture on evaluation by Prof Charles Clarke. @inproceedings{Yang:2014:DIR:2600428.2602297, author = {Yang, Hui and Sloan, Marc and Wang, Jun}, title = {Dynamic Information Retrieval Modeling}, booktitle = {Proceedings of the 37th International ACM SIGIR Conference on Research \&\#38; Development in Information Retrieval}, series = {SIGIR '14}, year = {2014}, isbn = {978-1-4503-2257-7}, location = {Gold Coast, Queensland, Australia}, pages = {1290--1290}, numpages = {1}, url = {http://doi.acm.org/10.1145/2600428.2602297}, doi = {10.1145/2600428.2602297}, acmid = {2602297}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {dynamic information retrieval modeling, probabilistic relevance model, reinforcement learning}, }]]> Sat, 05 Jul 2014 08:12:48 GMT /slideshow/dynamic-information-retrieval-tutorial/36653675 marcCsloan@slideshare.net(marcCsloan) Dynamic Information Retrieval Tutorial - SIGIR 2015 marcCsloan Dynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and information filtering systems. Examples include large datasets containing sequential data capturing document dynamics and modern IR systems observing user dynamics through interactivity. Existing IR techniques are limited in their ability to optimize over changes, learn with minimal computational footprint and be responsive and adaptive. The objective of this tutorial is to provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling. Dynamic IR Modeling is the statistical modeling of IR systems that can adapt to change. It is a natural follow-up to previous statistical IR modeling tutorials with a fresh look on state-of-the-art dynamic retrieval models and their applications including session search and online advertising. The tutorial covers techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and presents to fellow researchers and practitioners a handful of useful algorithms and tools for solving IR problems incorporating dynamics. http://www.dynamic-ir-modeling.org/ A newer version of this tutorial presented at WSDM 2015 can be found here http://www.slideshare.net/marcCsloan/dynamic-information-retrieval-tutorial-wsdm-2015 This version has a greater emphasis on the underlying theory and a guest lecture on evaluation by Dr Emine Yilmaz. The newer version presents a wider range of applications of DIR in state of the art research and includes a guest lecture on evaluation by Prof Charles Clarke. @inproceedings{Yang:2014:DIR:2600428.2602297, author = {Yang, Hui and Sloan, Marc and Wang, Jun}, title = {Dynamic Information Retrieval Modeling}, booktitle = {Proceedings of the 37th International ACM SIGIR Conference on Research \&\#38; Development in Information Retrieval}, series = {SIGIR '14}, year = {2014}, isbn = {978-1-4503-2257-7}, location = {Gold Coast, Queensland, Australia}, pages = {1290--1290}, numpages = {1}, url = {http://doi.acm.org/10.1145/2600428.2602297}, doi = {10.1145/2600428.2602297}, acmid = {2602297}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {dynamic information retrieval modeling, probabilistic relevance model, reinforcement learning}, } <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dynamicinformationretrievalmodelingslides-140705081248-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Dynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and information filtering systems. Examples include large datasets containing sequential data capturing document dynamics and modern IR systems observing user dynamics through interactivity. Existing IR techniques are limited in their ability to optimize over changes, learn with minimal computational footprint and be responsive and adaptive. The objective of this tutorial is to provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling. Dynamic IR Modeling is the statistical modeling of IR systems that can adapt to change. It is a natural follow-up to previous statistical IR modeling tutorials with a fresh look on state-of-the-art dynamic retrieval models and their applications including session search and online advertising. The tutorial covers techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and presents to fellow researchers and practitioners a handful of useful algorithms and tools for solving IR problems incorporating dynamics. http://www.dynamic-ir-modeling.org/ A newer version of this tutorial presented at WSDM 2015 can be found here http://www.slideshare.net/marcCsloan/dynamic-information-retrieval-tutorial-wsdm-2015 This version has a greater emphasis on the underlying theory and a guest lecture on evaluation by Dr Emine Yilmaz. The newer version presents a wider range of applications of DIR in state of the art research and includes a guest lecture on evaluation by Prof Charles Clarke. @inproceedings{Yang:2014:DIR:2600428.2602297, author = {Yang, Hui and Sloan, Marc and Wang, Jun}, title = {Dynamic Information Retrieval Modeling}, booktitle = {Proceedings of the 37th International ACM SIGIR Conference on Research \&\#38; Development in Information Retrieval}, series = {SIGIR '14}, year = {2014}, isbn = {978-1-4503-2257-7}, location = {Gold Coast, Queensland, Australia}, pages = {1290--1290}, numpages = {1}, url = {http://doi.acm.org/10.1145/2600428.2602297}, doi = {10.1145/2600428.2602297}, acmid = {2602297}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {dynamic information retrieval modeling, probabilistic relevance model, reinforcement learning}, }

Dynamic Information Retrieval Tutorial - SIGIR 2015 from Marc Sloan

]]> 7390 12 https://cdn.slidesharecdn.com/ss_thumbnails/dynamicinformationretrievalmodelingslides-140705081248-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation White http://activitystrea.ms/schema/1.0/post

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0 https://cdn.slidesharecdn.com/profile-photo-marcCsloan-48x48.jpg?cb=1526232589 I have a PhD in Computer Science, I am an internationally experienced software engineer and now a budding entrepreneur looking for exciting new opportunities. Get in touch :) Specialities: - Web search and information retrieval research - Machine learning, AI and data science - Lean and agile software development - Full stack web development (Javascript, CSS, HTML, Node, Django etc,) http://mediafutures.cs.ucl.ac.uk/people/MarcSloan/