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The Dutch-Belgian Information Retrieval Workshop was held this year in Delft. A nice lot of people from all around the Netherlands, Belgium, and a handful from abroad came together to share their research highlights from this year. It was a nice programme, filled with interesting short talks (about 15mins) that were great to give us a summary of what everybody is working on.
My personal highlight was Chato’s keynote speech on algorithmic bias. Although a lot is discussed on the topic, there is yet no to little work that presents some sort of foundation that describes the issue, frames it and offers some theoretical framework from which we can start thinking about solutions. Chato’s work laid the ground works on this area. He started with explaining what algorithmic bias is and stressed how coupled it is with data collection. I’m looking forward to seeing more on his work on the subject.
Figure 1. “Detecting Algorithmic Discrimination” by Carlos Castillo, presented at DIR 2016. Delft, the Netherlands.
Related Posts
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Starting my talk at ECIR 2018 Industry track. Courtesy of Gabriela Kazai. European Conference on Information Retrieval (ECIR) is a annual European scientific conference around bhe topics of search engines, recommender systems, text analytics, user modeling, and evaluation. This year ECIR was held in Grenoble, France. With more than 250 attendants and 4 days packed with tutorials, workshops, research, and industry talks, it was a great place to be to get updated with the latest and greatest about search engines.
The theme of this year’s Industry Day was to bring lessons learned from industry to academia. What are differences when developing a research algorithm and when we bring to practice? These lessons could inspire and inform our fellow academics for the challenges practitioners face when bring these algorithms to production.
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This year I talked at the industry track where I shared my experiences from running a search startup in the Netherlands. I received positive feedback, and it was good to catch up with old friends and meet new people. If you want a short recap from the latest and greatest trends in search technology, read on my summary on 904Labs blog.
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Recording of the talk is on YouTube; courtesy of Frankfurt Data Science. I was happy to be invited at Frankfurt Data Science meetup to talk about data science, meetups, and how to build a data science-oriented startup. The event was held at Frankfurt School on March 1st, 2018. In my talk I gave an overview of the meetup scene in Amsterdam, briefly presented Amsterdam Data Science and its activites, and then I shared my experiences on founding and developing 904Labs, before I delved into one of my favorite topics: machine learning and search.
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Figure 1. At the stage of AI2Future.
AI2Future is a local, Croatian, initiative for disseminating the importance and for grounding the use of Artificial Intelligence. The conference brings together researchers, A.I. start ups, and large corporates from all around Croatia to share experiences and learn from each other on what A.I. can and cannot do.
I was invited to give one of the two keynote talks. The first keynote focused on conversational agents and natural language processing and mine followed up with insights how search powers a large spectrum of applications from search, question and answering, and recommendations. I focused on the work we do at 904Labs and illustrated the principles of online learning to rank through real-world examples from our experimences with our customers.
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In this paper we describe our participation [1] in the Second Web People Search workshop (WePS2) and detail our approaches. For the clustering task, our focus was on replicating the lessons learned at WEPS1 on the data set made available as part of WEPS2 and on experimenting with a voting-based combination of clustering methods. We found that clustering methods display the same overall behavior on the WEPS1 and WESP2 data sets and that a hierarchical clustering approach delivers the best performance, even outperforming voting-based combinations. For attribute extraction, we explore approaches using pattern matching with manually and automatically constructed patterns. Manual patterns were constructed using expert knowledge and following analysis of sample data. Automatic pattern construction extracts textual and syntactic context around training samples and selects patterns which are expected to perform well based on leave-one-out evaluation. Experimental results show that
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I moved to Barcelona in the first week of September for a three-month internship at Yahoo! Research Labs. I’m very excited about it, and I’m looking forward to getting to know the people here and the problems they are working on.
Update: My work at Yahoo! Research Labs resulted in a publication at SIGIR 2012 [1]; see [post] for the abstract(20120623-sigir2012-language-intent-models.md).
References
[1] Manos Tsagkias and Roi Blanco. 2012. Language intent models for inferring user browsing behavior. In Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval (SIGIR ‘12). Association for Computing Machinery, New York, NY, USA, 335–344. ACM Link. PDF
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I took part in SIREN 2008, a research event in the Netherlands, presenting our work with Martha Larson and Maarten de Rijke, on Term Clouds as Surrogates for User Generated Speech [1]; see post
References
[1] Manos Tsagkias, Martha Larson, and Maarten de Rijke. 2008. Term clouds as surrogates for user generated speech. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ‘08). Association for Computing Machinery, New York, NY, USA, 773–774. ACM Link PDF
