LWDA Conference in Mannheim

Authors: Andreas Lommatzsch and Asmaa Haja

The conference "Lernen Wissen, Daten, Analysen" has been held in Darmstadt August 22-24, 2018. The conference is organized by the German Computer Science Society (GI) and consists of 5 tracks: Information Retrieval (FGIR 2018), Knowledge Management (FGWM 2018), Knowledge Discovery, Data Mining, and Machine Learning (KDML 2018, Business Intelligence (WSBI 2018), and Large-Scale Data Management and Processing – Applications in Research and Industry (FGDB 2018).

The conferences program consists of research paper presentation, a poster session and four interesting keynotes from different domains. The first keynote was given by Daniela Nicklas. The talk discussed the question of who watches the sensors, which are used for monitoring real-world phenomena. The second keynote, given by Kerstin Bach, explained the "selfBACK" decision support system (DSS) project. The system uses Case-Based Reasoning for supporting patients with low back pains. Frank Giesler presented the third talk. He introduced a tried-and-tested approach that ensures successful data-driven digital product development through the close involvement of future users. The last talk, given by Stephan Mandt, gave an overview of some exciting recent developments in deep probabilistic modeling, which combines deep neural networks with probabilistic models for unsupervised learning.

The DAI-Labor presented the research conducted in the student project. Our paper "Normalization of Time series for Improving Recommendations" studies how observed log data in recommender systems can be decomposed into several different characteristic patterns. We explain that the decomposition is the basis for understanding the lifecycle of the items and the characteristic user preferences. Furthermore, the decomposition can be used for trend-based prediction, extrapolating the extracted time series taking into account the characteristic patterns. The methods have been studied in a News Recommendation scenario that is characterized by a continuously changing set of items and strong variances in the number of users during the day.
In addition to a talk, we also presented a poster allowing us to discuss details of the scenario and our approach individually.

Overall, the conference provided many interesting talks covering a wide spectrum of topics. The positive atmosphere has been the basis for exciting discussion.

EYZ Media Research: Cooperation With DAI-Labor TU Berlin

Authors: Jing Juan, Andreas Lommatzsch, Antje Marx

Recommender systems have proven effective to help the audience of streaming platforms finding relevant movies and serials hidden in huge amount of available content. The question is how can the audience be assisted in finding the most interesting movies matching the individual user preferences. Popular platforms, like Netflix, Pandora, and Amazon successfully provide recommender systems. Collaborative filtering based approach (item-based and user-based) are popular to provide recommendations.

Standard recommendation approaches have several weaknesses in niche market due to the user data sparsity, missing ratings, and highly specific items. Therefore, customized recommendation strategies are needed, adapting to the specific system features while catering users’ demand. And such customization can take place when traditional recommender algorithms tailored to the special items format and user experience in the corresponding niche markets, thereby add value to the specific system or platform.
This has been the motivation for the cooperation between EYZ Media and the DAI-Labor of the TU Berlin.

The collaboration between EYZ Media and DAI-Lab in year 2018 considers both traditional recommendation strategies (content-based approach and collaborative filtering approach) and modern event-based approach (introducing domain relevant trend in heterogeneous system resource like Twitter). Obeying the API and dataflow definition from EYZ Media, DAI-Labor provides the support on recommendation solution by building up elasticsearch service, collaborative filtering component and event-based recommender pipeline. Corresponding evaluation turned out from both experts’ subjective opinion and offline evaluation on specific metrics help us better understand how recommenders booster user activities in the niche market.

Project details are discussed in the realeyz tech blog.

DAI-Lab

The DAI-Labor (Distributed Artificial Intelligence Laboratory) at the TU Berlin conducts research and development projects in order to provide solutions for a new generation of artificial intelligence systems and services. The competence center Information Retrieval and Machine Learning (IRML) focuses on developing innovative recommender systems and information retrieval systems combining machine learning techniques, graph-based approaches and social media analysis.

realeyz

EYZ Media operates the VOD platform realeyz, on the web at realeyz.de, as Prime Video Channels and as an app in the stores. Thanks to its focus on independent film, realeyz has built up a growing user base and is one of the top 3 VOD specialty providers in Germany. RealEYZ stands for intelligent, exciting, classic and innovative content in all formats and lengths, curated carefully and with passion. Every day a new film, all in its original version with a selection of 1,000+ titles – and the focus on indie movies appeals above all to young, metropolitan users. The use of many unique components created by EYZ’s own dev. Team such as the recommender system with its algorithms that enable individual and personal UX as well as realeyz’’s presence on different channels and technical environments sharpen the profile, strengthen customer loyalty and establish the realeyz brand as the essential hub for independent productions. RealEYZ is supported by the EU-CREATIVE program and is part of the EuroVoD network.

Supported by Investitonsbank Berlin

1st Annual Meeting of the German Chinese Association for Artificial Intelligence

Author: Andreas Lommatzsch

The first annual meeting of the German Chinese Association for Artificial Intelligence (GCAAI) took place at Porsche Digital Lab, on July 27th, 2018.

GCAAI promotes the exchange of educational resources between Germany and Chine in the field of artificial intelligence. They organize meetups and seminars focusing on AI topics. The events feature speakers from academia and industry. Participants gain access to valuable information, have the opportunity to discuss recent trends with scholars, and receive support to find internships or job opportunities.

The meetup at the Porsche Digital Lab has been an exciting event for discussing current projects and research activities in Artificial Intelligence. The meeting started with six short presentations: Dr. Han Xiao, a Senior Research Scientist at Tencent-AI, presented the challenges when teaching machines to comprehend texts and to answer questions. He gave an excellent overview of methods for extracting and summarizing information. In addition, the talk discussed the problem of detecting questions unrelated to a given text/domain.

In the meetup, I gave a short introduction about recommender system and presented the NewsREEL challenge as well as the MediaEval NewsREEL Benchmark. I highlighted the research questions and the unique chance to evaluate news recommender algorithms with consideration of multi-media data coming from an operating news recommender system.

The talks have been a good starting point for fruitful discussions. The meeting continued with a dinner in the Indochinese restaurant in Kreuzberg. The next meeting of the GCAAI is planned for November.

Announcing the 6th International Workshop on News Recommendation and Analytics (INRA 2018)

Author: Andreas Lommatzsch

The 27th edition of the Conference on Knowledge Management and Machine Learning will take place in Torino (Italy) October 22-26. In 2018, the conference focuses on big data, big information, and big knowledge. Thus, the topics of interest cover a wide range which includes data acquisition, pre-processing, analysis, visualization, evaluation, and innovative applications.

The news domain generates a plethora of data leading to various problems in the realm of "big data". We want to address these issues and more in the sixth edition of the "Workshop on News Recommendation and Analytics (INRA)" in conjunction with CIKM.

Continuously changing streams of unstructured, fragmentary stories necessitate research on how to categorize, verify, and present news. A multitude of challenging research questions emerges as a result. The workshop brings together researchers, media companies, and practitioners to exchange ideas and explore ways to support creating, distributing, and recommending digital news.

The primary topics of interests are:

  • News Recommendation
  • News Analytics
  • Fake News and Disinformation
  • User Experience Issues
  • Evaluation Platforms, Methods and Datasets

We also provide Data Sets and Evaluation Platforms if you would like to test their ideas on real world news settings.

The Keynote Talks will be given by

  • Frank Hopfgartner, Information School of University of Sheffield, UK
  • Anja Benner-Tischler, University of Kassel, Germany
  • Leif Ramming, Plista GmbH, Germany

Use the EasyChair Conference system to submit contributions. Accepted papers will be published in CEUR workshop series by CIKM.

Please do not forget the important dates:

INRA 2018

6th International Workshop on News Recommendation and Analytics (INRA 2018)

Presenting our Chatbot Framework at the Conference of Innovative Internet Community Services

Author: Andreas Lommatzsch

The 18th edition of the International Conference on Innovative Internet Community Systems (I4CS) took place at the University of Zilina, Slovakia, 18-20 June 2018.
This year the conference focuses was on innovation methods for enterprises, methods for building the Internet of Things (IoT) as well as innovative applications.

I presented our chatbot framework optimized for answering questions related to public administrations’ services. In contrast to most existing frameworks, our approach provides detailed answers to several hundred services and helps users in a dialog to find all information relevant for a specific information need. The system combines several data sources (databases, geo information, ontologies, and synonyms) and uses several different natural language processing methods for detecting the user’s intension. In my talk, I presented our approach in detail and discussed our experiences operating the chatbot system live on the service web portal of the Berlin’s administration. Find more details in the conference paper.

The conference featured a multitude of exciting presentations. I would like to highlight the talk “Innovation Management Methods in the Aviation Industry” given by Karl-Heinz Lüke. The presentation discussed the results of a survey of external and in-house innovation methods giving profound insights into the aviation industry’s way of thinking.

The conference’s main social event was a hiking tour in the Mala Fatra with its magnificent gorges and beautiful hills. The tour has fostered discussing trends and experiences concerning current research projects in an inspiring landscape.

In 2019, the 19th edition of the I4CS conference will be held in Wolfsburg, Germany.

NewsREEL Multimedia @ MediaEval Benchmark 2018

Author: Andreas Lommatzsch

The MediaEval Benchmarking is an Initiative for Multimedia Evaluation offering data and challenges in the form of shared tasks. MediaEval focusses on innovative algorithms and methods for exploring and retrieval multi-media data. The provided data typically cover several modalities such as audio-visual (video, audio), textual, and contextual data.

NewsREEL Multimedia (News Recommendation with Image/Text Contents) runs as a new pilot task in 2018. The task is based on the observation that images have a big impact on perception of news. The NewsREEL MultiMedia provides a data collection of news articles, item popularity data, and (compressed) image data. The task consists in predicting the popularity of new items based on the multi-media data. The quality of the predictions is measured with the Precision and the NDCG.

The MediaEval workshop 2018 will be held at EURECOM in Sophia Antipolis, France, in November 2018.

Web page: MediaEval-Benchmark

Web page: MediaEval-Benchmark

Tutorial Real-time Recommendations with Streamed Data @ KI2018

Author: Andreas Lommatzsch

The 41st German Conference on Artificial Intelligence will be held September 24-28th, 2018, Berlin, Germany. The conference brings together academic and industrial researchers from all areas of AI. It is an well-established venue to discuss new trends in AI and to exchange new research results. The deadline for submitting abstracts is on April 30th, 2018.

Tutorials and workshops are scheduled for the first two days of the conference. We plan to give a tutorial on Real-time Recommendations with Streamed Data. The tutorial introduces a combined view on multi-dimensional benchmarking and real-time requirements in recommender systems. Participants will not only learn about recommendation algorithms but also delve into practical issues related to system maintenance, model updating, and continuous evaluation. For more details please visit the tutorial webpage.

KI2018-Tutorial-Webpage

KI2018-Tutorial-Webpage

This week in AI #42

With this post, we want to start sharing some of the interesting news regarding the topic of AI. Regarding the fact that we are a research group based in Germany, we will also post articles and references in German – feel free to use deepl.com for translation…

And starting with some news in German and from Germany:

  • Wenn der Roboter Wünsche von den Augen abliest
    heise.de

    Die International Conference on Human-Agent Interaction (HAI 2017) findet in Bielefeld statt und beschäftigt sich mit der Kommunikation zwischen Mensch und Maschine und der Erkenntnis, dass Kommunikation nicht nur auf Worte beschränkt ist.
  • Emirate richten Ministerium für Künstliche Intelligenz ein
    heise.de

    “Die nächste globale Welle ist die künstliche Intelligenz. Wir wollen das Land sein, das darauf am besten vorbereitet ist”, schrieb Ministerpräsident Mohammed bin Raschid Al Maktum am Donnerstag auf Twitter. Dazu nur der Hinweis auf den Zeit Artikel vom 28.09.2017 – Diese Überschrift muss leider noch laden ….
  • A brief guide to mobile AI chips
    theverge.com

    A reminder that the current AI (re)evolution is not only about the algorithms but also about the hardware. And while the author is making some good points for specialized hardware, the conclusion “SO DO I NEED AN AI CHIP IN MY PHONE? – No, not really. So much work is being done on making AI services run better on the hardware currently available, that unless you’re a real power user, you don’t need to worry about it.” is the wrong one. We instead need more efforts to bring specialized hardware (and software) to products to improve the usefulness of AI services. Everybody who has tried using Siri or any other smart assistant without a stable internet connection will probably agree. The current detour over the cloud is because we lack proper hardware on the mobile devices.
  • The BBC is turning to AI to improve its programming
    engadget.com

    Kudos to the BBC for constantly exploring new paths. I remember that BBC was also the first TV station I heard of trying out Semantic Web standards. One idea BBC is researching as “a concept called object-based broadcasting…that could be assembled in different ways depending on the user or end-hardware” – the dream of truly personalized TV broadcasting.

Till Plumbaum

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.