8th Conference of the CLEF Initiative in Dublin

Author: Andreas Lommatzsch

The 8th conference of the CLEF initiative has been held at the Trinity College in Dublin from September 11th till September 14th. The CLEF initiative is a European campaign focusing on evaluation methods in Information Retrieval and related fields.

The News Recommendation Lab (NewsREEL) runs as a CLEF lab the third year. NewsREEL gives researchers the ability to evaluate news recommendation algorithm both online and offline. The stream-based data, the use multi-dimensional benchmarking (taking into account precision and technical metrics) as well as the evaluation based on live user feedback make NewsREEL special CLEF lab.

In the NewsREEL workshop the participating teams presented the evaluated algorithms. The spectrum of implemented solutions covered a wide spectrum of approaches ranging from framework-based approaches (using abstract query languages) to more experimental approaches (using Contextual Bandits). The enabled a vivid, fruitful discussion between the participating teams. Detailed descriptions of the approaches are explained in the working notes.

Co-located with CLEF 2017, the yearly conference of the MediaEval Benchmarking Initiative was held. Several joint conference sessions and poster sessions provided the basis for interesting discussions and the exchange of ideas. Hopefully, the cooperation between the two benchmarking initiatives will continue in the future.

NewsREEL will continue also in 2018. It will not run as a CLEF lab in order to focus stronger on data mining and recommender algorithms.

The following photos give a visual impression from the Trinity College Campus in Dublin as well as from the CLEF conference and MediaEval conference.

ACM Conference on Recommender Systems 2017

The 11th edition of the ACM Conference on Recommender Systems will take place in Como, Italy, in the time 27-31 August, 2017. The conference features three keynotes, a panel discussion, eleven paper and three industry sessions, three tutorials, and thirteen workshops. On Tuesday, 29 August 11 a.m., Benjamin Kille will present the article “Recommending Personalised News in Short User Sessions” co-authored with Elena Epure, Rebecca Deneckere, Camille Salinesi (Panthéon-Sorbonne), Jon Espen Ingvaldsen (formerly NTNU), and Sahin Albayrak (DAI-Labor). The article analyses how category information can support news recommender systems to select a more relevant set of suggested readings. On of the workshops, the Temporal Reasoning in Recommender Systems Workshop, features another CC-IRML publication. The article “On the Decaying Utility of News Recommendation Models” by Benjamin Kille and Sahin Albayrak shows the necessity to update recommendation models in the domain of news. Its presentation is scheduled on Thursday, 31 August, 9:50 a.m.

International Conference on Web Intelligence

The IEEE/WIC/ACM International Conference on Web Intelligence will take place 23-26 August, 2017, in Leipzig, Germany. The conference is co-located with the 15th German International Conference on Multiagent Systems Technologies (MATES). It features six keynotes, seven paper sessions, seven tutorials, and nine workshops one of which is the International Workshop on News Recommendation and Analytics. Among its publications, the article “Incorporating Context and Trends In News Recommender Systems” by Andreas Lommatzsch and Benjamin Kille explores how contextual factors and trends can help readers to find more relevant news articles. The talk is scheduled on Wednesday, 23 August, 2017 at 10 a.m.

I4CS conference 2017, Darmstadt, Germany

Author: Andreas Lommatzsch

The 17th edition of the International Conference on Innovative Internet Community Systems (I4CS) was held at T-Systems in Darmstadt 26-28 June 2017. The conference focuses on Internet Community Systems including distributed architectures, Social Networks and open collaboration services as well as on eHealth and intelligent transport solutions.

I presented datasets and methods for the sentiment analysis of German News and Forum texts. My talk discussed the challenges of automatically determining the sentiments of German texts with respect to the specific application scenario. The developed approach and the implemented systems have been discussed in detail.

Many interesting papers were presented at the I4CS conference. I would like to mention the talk "On a Fog Computing Platform Built on ARM Architectures by Docker Container Technology" given by Udo Krieger. The paper discusses the concepts of a fog computing platform extending the principles and services of cloud computing to the edge of a network. The talk gave a good overview on the platform design principles and the implemented software components optimized for ensuring highly-available, efficiently working fog cells. The talk was awarded with the best presentation award.

Additional highlights of the conference were a visit of the QIVICON House, a tour over the Technologie-Zentrum Rhein Main business park, and the sightseeing tour through the city of Darmstadt.

The 18th I4CS conference will be held in Zilina, Slovakia.

Big Data Universe 2017 in Budapest

The Big Data Universe 17 in Budapest took place in the Aqvarium Club, an exclusive location in the very middle of the city, located below a glass roof right under a public pool. The conference gathered top influencers of the hungarian big data industry, interested in state of the art products and research directions. A wide range of speakers like Lucas Bernardi (Booking.com), Dániel Molnár (Door2Door GmbH) Attila Komor (Nextent Informatics Co.) and Mátyás Dobó (Telekom) provided insights into the challenges and solutions of their daily work.

Besides the conference talks, we where able to present our newest meta machine learning prototype, which is a part of the projects under the roof of the new Berlin School of AI, a cooperation between the DAI-Labor and T-Labs . It is the first step towards gathering reproducable performance data of machine learning algorithms in order to compare not just the predictive performance, but also the costs it takes to use them. It enables decisions about efficiency, not just effectivity. In the future we want to go beyond and use the gathered data for model selection and tuning approaches based on meta features.

We where happy to show our work and to get valuable feedback from experts from all over the place. Very special thanks go out to Dr. Tomáš Horváth and his team, who where a great help and showed us around.


Something for the weekend

Google presented a great demo to play a piano duet with a computer. So if you have time on the weekend and no company, maybe Tensorflow is a good match.

Check out Magenta

NewsREEL 2017

Author: Benjamin Kille

We are excited to announce the start of another edition of CLEF NewsREEL. This year’s challenge continues with both tasks:

NewsREEL Live: interact with an operating news recommender systems and real users
NewsREEL Replay: analyze a comprehensive data set to determine the best recommender

Our partners at plista are currently busy revising the Open Recommendation Platform (ORP). The new systems will be more stable and equipped with a new UI for a much better user experience. Until the new systems has been released, we encourage participants to engage in NewsREEL Replay.

We released a new data set of more than 168 million events recorded over the stretch of four weeks. The data set provides a representative picture of what your recommender will encounter in NewsREEL Live. The following charts gives you an impression on what the data looks like.




If you want to join us in the pursuit of news recommender systems evaluation, please register at here and make sure to check the boxes for NewsREEL. You will receive information about how to access the data shortly after successful registration.

6th Recommender Meetup Amsterdam

Author: Andreas Lommatzsch

The Recommender Meetup Amsterdam (www.recommenders.nl) is meetup focusing on innovative recommender algorithms and AI methods. The meetup is organized by the TU Delft and hosted by different companies in the Netherlands. The 6th RecSys:NL Meetup was held on September 28th at ORTEC Amsterdam.

There were three talks: First, Alexandros Karatzoglou (Telefonica Research, Barcelona, Spain) gave a talk about Deep Learning Methods and the use of Deep Leaning approaches in Recommender Systems. In his talk, he discussed the state of the art of Deep Learning methods and explained current trends and application scenarios in the Deep Learning methods are used.

The second talk was given by Andreas Lommatzsch (TU Berlin, Berlin, Germany). In his talk "NewsREEL challenge: Online evaluation of recommender systems" he explained the CLEF NewsREEL challenge and the challenges of online evaluation methods. The CLEF NewsREEL challenge allows researchers to evaluate algorithms for recommending online news articles. The talk explained the NewsREEL scenario in detail. The specific challenges have been discussed such as the continuous changes in the set of relevant items, the influence of context parameters, and the variance in the user behavior. In addition, the talk presented different algorithmic approaches as well as the experiences gained organizing the challenge. The slides are available here.

Daniel Kohlsdorff (XING, Hamburg, Germany) explained the challenges of providing high-quality job recommendations. He presented selected components of the XING’s recommender system explaining content based recommendations with latent spaces, and outlier filtering. In addition, Daniel presented the results of the RecSys challenge 2016 (co-organized by XING) and gave an outlook on the RecSys challenge 2017.

After the talks there has been a lively discussion accompanied by pizza and beer.

LWDA conference 2016, Potsdam, Germany

Author: Andreas Lommatzsch

The LWDA ("Lernen Wissen Daten Analysen") conference is a conference organized by the Gesellschaft für Informatik (German Informatics Society). The 2016 edition was held in Potsdam, 12-14 September 2016.
The conference is organized in 4 tracks focusing on Information Retrieval (IR), Knowledge Discovery and Machine Learning (KDML), Databases (DB) and Knowledge Management (WM).
CC IRML contributes to the conference in three different tracks.

The paper "An Interactive e-Government Question Answering System" presents a Question-Answering system developed for the specific requirements of an e-government scenario. The system combines information retrieval and AI methods for providing direct answers to questions concerning governmental services. The systems has been developed in the master course "Semantic Search Project".

The paper "Topical Video-On-Demand Recommendations based on Event Detection" presents an event-based recommender system for art house movies Based on semantic sources and social media events are identified that are used for computing movie recommendations related to specific days and events.

The poster "Applying Topic Model in Context-Aware TV Programs Recommendation" presents our approach for learning context aware recommendations combining topic models and model built based on a user data-based Latent-Dirichlet Allocation.

The very active poster session and the high-quality keynote talks as well as the modern venue (HPI, Campus Griebnitzsee) inspired valuable discussion and new research ideas.

CrowdRec: Topical Semantic Recommendations for Auteur Films @RecSys 2016

Author: Till Plumbaum

At this years RecSys we presented a demo system that collects topics of interest from Twitter and RSS-Feeds, extracts relevant Named Entities, and uses semantic relations for recommending movies closely related to these topics.
During the poster session, which was running for 3 1/2 hours, we were constantly in discussion with the great RecSys crowd.

You can get the poster here. If you are interested in the topic, we are happy to get in touch with you, just send a mail to the authors and we will get back to you.

The research leading to these results was performed in the CrowdRec project, which has received funding from the EU 7th Framework Programme FP7/2007-2013 under grant agreement No. 610594.