The Many Criteria Optimization and Decision Analysis (MACODA) workshop, organized by a team of successful researcher and scientists in the field of multi and many obejctive optimization was held from Monday 16 September through Friday 20 September 2019 in Leiden.

The motivated participants of this workshop including early stage researchers, post docs, professors and several researchers working in industry all had a shared goal and that was to address this question: "How should we use computational support to deal with problems with many objectives?" This question is crucially important, as the real-world optimization problems can often be formulated as multi or many objective optimization tasks.

The first day of the workshop belonged to several interesting talks given by pioneers in the field of many and multi-objective optimization, Prof. Hisao Ishibuchi and Prof. Miettinnen. Also, the early stage researchers found the opportunity to share their recent works. From the very first day participants found time and opportunity to discuss different topics and challenges in the field. The atmosphere of sharing and learning from each other got even better as we started with the open space workshop model on the second day. In this form of workshops everybody is invited to come up with topics that have a high importance to discuss with the community. Everyone who comes up with a topic can write it on a card and attach it to the wall in a specific cell showing a specific time slot and room. This is an official invitation to the rest of the participants to join the discussion if it is of their interest. Every discussion was documented.

In the course of the workshop a lot of existing challenges in the field along with possible cures were discussed. Some of the groups ended up finalizing their work with a plan for future collaborations in form of a paper or a project.

We are pleased to inform you that the recent work "General Board Game Playing for Education and Research in Generic AI Game Learning" of Prof. Wolfgang Konen has been accepted for publication and it will be presented as a Poster at IEEE CONFERENCE ON GAMES (CoG) 2019.  CoG 2019, held  in London, UK, will be hosting many peer-reviewed paper and poster presentations as well as academical and industrial invited talks in various game technology related fields.  Wolfgang Konen introduces a general board game playing and learning framework, in his recent paper. You can find more information about the developed educational tool by Wolfgang Konen in one of our former blog-posts.

Recommender System is the name given to any software designed to recommend you objects to purchase, to click on or to watch, which might be attractive to you with a high probability. Development of such systems became a hot topic for software developers and machine learning engineers in the last years. A well-performing recommender system can make significant contributions to various online platforms.

Nowadays, many of us interact with recommender systems on a daily basis. If you have an account in Amazon or Netflix or many similar online services, you may have received suggestions that often fit your interest. This can vary from a movie suggested by Netflix or very various products suggested by Amazon. The targeted advertisements on Facebook or the targeted pop-up advertisements in your browser are also other examples of applications of these systems. Almost no two person receives the exact same advertisements or suggestions on an online platform.  All these personalized suggestions and advertistments use recommender systems to find the best match for your interests according to your former ratings, purchases or clicks.

Content-based recommender systems often make use of the explicit features of the users and the items to provide recommendations. In contrary, the Collaborative Filtering recommender systems do not have access to any sort of explicit features and tend to learn the important features of users and items implicitly. The real-world recommender systems are often a hybrid of both approaches.

You can try our movie recommender system demo, by simply giving your own ratings and see what the system will suggest to you. This movie recommender system is based on the implementation of Matrix Factorization algorithm in Python. Matrix Factorization is a collaborative filtering approach which tends to learn implicit features for users and items by factorizing the exiting rating matrix. We expalin this algorithm in more details, in this Google Colaboratory Notebook . We describe how the recommender system problem turns into an optimization problem and how to handle such optimization tasks. Also a step-by-step implementation of this algorithm in Python is provided.

One of the slides of the presentation given by Henning Buhl during the Digital Xchange 2019 Conference

On Saturday, May, 25th, 2019, TH Köln hosted the annual Digital Xchange conference, which is organized as a cooperative work of Opitz Consulting and TH Köln, for the third time. This 1-day conference is becoming more and more popular: this year it attracted more than 800 participants.  More than 100 talks were given in the field of digitalization and its impact on industry. The topics had a large variety and covered many areas of digitalization including Big Data, Cloud Computing, Industry 4.0, Machine Learning, Artifical Intelligence, etc. You can find a list of all talks here.

One of the very interesting talks at Digital Xchange which attracted a large group of audience was "Deep Learning mit Keras und TensorFlow" given by Henning Buhl. He is a Computer Science Bachelor student at TH Köln who also works part-time under supervision of Prof. Wolfgang Konen. His talk coverd a large range of themes from neural networks to machine learning and Python code examples. As part of his talk he gave a live demo on Google Colaboratory with Jupyter notebooks showing TensorFlow and Keras examples. These examples are available on GitHub for your own study if you like. You can find them here or here on GitHub.