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Katja Hofmann | research on online learning to rank for information retrieval

research on online learning to rank for information retrieval (by Katja Hofmann)

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Katja Hofmann | research on online learning to rank for information retrieval | khofm.wordpress.com Reviews

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research on online learning to rank for information retrieval (by Katja Hofmann)

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Katja Hofmann | Katja Hofmann

https://khofm.wordpress.com/author/khofm

Researcher at Microsoft Research Cambridge, UK. Future interactive systems will learn directly from their users, and continuously adapt to their preferences. My research goal is to develop exploration, reward, and learning approaches that will make this possible. [ . Author Archives for Katja Hofmann. Query Formulation with Query Auto-Completion. In our recent CIKM paper An Eye-tracking Study of User Interactions with Query Auto Completion. For full details, take a look at the paper here. My 5 lectures b...

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RUSSIR 2014: Lectures on Online Experimentation for Information Retrieval | Katja Hofmann

https://khofm.wordpress.com/2014/12/17/russir-2014-lectures-on-online-experimentation-for-information-retrieval

Researcher at Microsoft Research Cambridge, UK. Future interactive systems will learn directly from their users, and continuously adapt to their preferences. My research goal is to develop exploration, reward, and learning approaches that will make this possible. [ . RUSSIR 2014: Lectures on Online Experimentation for Information Retrieval. This summer, I was invited to teach at RuSSIR. To get you started, here are the slides from the first lecture:. Click to share on LinkedIn (Opens in new window).

3

Publications | Katja Hofmann

https://khofm.wordpress.com/publications

Researcher at Microsoft Research Cambridge, UK. Future interactive systems will learn directly from their users, and continuously adapt to their preferences. My research goal is to develop exploration, reward, and learning approaches that will make this possible. [ . For the most up-to-date list of publications, please see my pages on DBLP. B Mitra, F. Radlinski, M. Shokouhi, “An Eye-tracking Study of User Interactions with Query Auto Completion”, CIKM 2014, Shanghai, 2014. A Schuth, S. A. Whites...Aleks...

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Query Formulation with Query Auto-Completion | Katja Hofmann

https://khofm.wordpress.com/2014/12/22/query-formulation-with-query-auto-completion

Researcher at Microsoft Research Cambridge, UK. Future interactive systems will learn directly from their users, and continuously adapt to their preferences. My research goal is to develop exploration, reward, and learning approaches that will make this possible. [ . Query Formulation with Query Auto-Completion. In our recent CIKM paper An Eye-tracking Study of User Interactions with Query Auto Completion. Here’s an example video that shows how searchers examine QAC:. Click to email (Opens in new window).

5

Practical Online Retrieval Evaluation | Katja Hofmann

https://khofm.wordpress.com/2013/03/24/practical-online-retrieval-evaluation

Researcher at Microsoft Research Cambridge, UK. Future interactive systems will learn directly from their users, and continuously adapt to their preferences. My research goal is to develop exploration, reward, and learning approaches that will make this possible. [ . Practical Online Retrieval Evaluation. Thank you to all those who attended the ECIR 2013. Slides (based on the 2011 SIGIR tutorial by Yisong Yue and Filip Radlinski, the slides of this earlier tutorial are available on Yisong’s website.

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CIKM'13 paper accepted - Anne Schuth

http://www.anneschuth.nl/cikm13-paper-accepted

Search Engines that Learn from Their Users. CIKM’13 paper accepted? CIKM’13 paper accepted. July 14, 2013. Our paper “ Evaluating Aggregated Search Using Interleaving. 8221; with Aleksandr Chuklin. And Maarten de Rijke. Was accepted for publication as full paper in CIKM’13. I will present the paper on Tuesday afternoon. Click to print (Opens in new window). Click to share on LinkedIn (Opens in new window). Click to share on Twitter (Opens in new window). Click to share on Facebook (Opens in new window).

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Software - Anne Schuth

http://www.anneschuth.nl/software

Search Engines that Learn from Their Users. The Living Labs for IR Evaluation (LL4IR) is a new evaluation paradigm. I implemented an API for participants (researchers) and sites (search engines) that take part in this Living Lab. Which is also run as a CLEF lab). The API allows participants (researchers) to evaluate their ranking systems on real users of real sites (search engines). On the flip site, it allows sites (search engines) to benefit from the knowledge of the research community. Year = {2016},.

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WSDM'13 paper accepted - Anne Schuth

http://www.anneschuth.nl/wsdm13-paper-accepted

Search Engines that Learn from Their Users. WSDM’13 paper accepted? WSDM’13 paper accepted. November 8, 2012. Our paper “ Reusing Historical Interaction Data for Faster Online Learning to Rank for IR. 8221; with Katja Hofmann. And Maarten de Rijke. Was accepted for publication in WSDM’13. Click to print (Opens in new window). Click to share on LinkedIn (Opens in new window). Click to share on Twitter (Opens in new window). Click to share on Facebook (Opens in new window). Leave a Reply Cancel reply.

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Lerot: an Online Learning to Rank Framework - Anne Schuth

http://www.anneschuth.nl/lerot

Search Engines that Learn from Their Users. Lerot: an Online Learning to Rank Framework? Lerot: an Online Learning to Rank Framework. September 5, 2013. Our demo “ Lerot: an Online Learning to Rank Framework. 8221; with Katja Hofmann. And Maarten de Rijke. Was accepted for publication at the Living Labs for Information Retrieval Evaluation. Workshop at CIKM’13. You can download Lerot from Bitbucket. Click to print (Opens in new window). Click to share on LinkedIn (Opens in new window).

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Katja Hofmann | research on online learning to rank for information retrieval

Research on online learning to rank for information retrieval. Future interactive systems will learn directly from their users, and continuously adapt to their preferences. My research goal is to develop exploration, reward, and learning approaches that will make this possible. [ . Query Formulation with Query Auto-Completion. In our recent CIKM paper An Eye-tracking Study of User Interactions with Query Auto Completion. Here’s an example video that shows how searchers examine QAC:. December 22, 2014.

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