Use Cases

In projects together with the companies like Studo, Nekom, TripRebel, Blanc Noir and many more, the Know-Center has developed several recommender solutions and gained the expertise to create the ScaR recommender framework. This research framework is the scalable state-of-the art recommendation engine that provides recommendation services nearly of the shelf packed with features that many commercial systems do not have. We used this framework successfully in past and ongoing projects.


If you are interested in setting up a research project using the ScaR framework, please contact the head of the social computing team
Ass. Prof. Elisabeth Lex
Elisabeth Lex

If you are interested in a Business Project please contact our business developer Matthias Traub
Matthias Traub

Project Partner Recommender

OpenAIRE Matchmaker is an innovative new service built upon the OpenAIRE scholarly graph that enables organisations to keyword search for organisations with the strongest records of funding success from the ever-expanding range of funders included in OpenAIRE. Drawing information from OpenAIRE on institutions, projects and funders, OpenAIRE Matchmaker will enable scientific institutions interested in forming consortia to identify potential partners with the exact disciplinary strengths and competences.
Furthermore, we provide personalized recommendations using Collaborative-Filtering and Content-Based Filtering. This recommendation service can be easily embedded into the OpenAIRE portal (or on research funder websites as an OpenAIRE-branded service), resulting in a new OpenAIRE service which demonstrates the added-value potential of the OpenAIRE scholarly graph for researchers and funders.

Social Book Recommender

In collaboration with our partner Leibnitz-Informationszentrum Wirtschaft (ZBW) we've created a social book recommender system. The recommendations are based on the rental information of two different platforms, which are then combined within the ScaR framework. This enables to create recommendations on a bigger set of interactions and create meaningful recommendations for each platform individually. The main focus has been on the interaction, hence, utilizing collaborative filtering methods for recommending books to user or books for certain bookmark lists. Further in this collaboration we've used the rental interaction data to create a easy to browse and understand bubble visualization.

Job Recommender

Studo App offers "Studo Jobs" functionality which is a platform used by students and helping them find a part-time or a full-time job. In order to improve user experience and increase user satisfaction, "Studo Jobs" platform has been extended with recommendation functionality using ScaR as the recommendation engine. The role of ScaR in this use-case is to analyze job postings and compare them with the student profiles with the goal of matching the most suitable candidates with the companies. In addition to the traditional recommendation approaches, ScaR application in this project also utilizes powerful Deep Learning approaches using Paragraph Vectors.
Our project partner Moshbit GmbH is a Start-Up company founded in 2015. The app "Studo" developed by Moshbit offers an overall concept in the form of a software solution to assist the students with their study program. "Studo" aggregates services used by students in their daily life and therefore provides them an easy solution for many organisational and administrative issues they have to deal with.

Social Recommender

ScaR provides recommendations to help clients to find the right social counsellors and organizations for their specific problem and help counsellors to answer the clients’ questions by recommending possible answers and related resources. Feedback from a central ticket system and user interactions improve the recommendation mechanism over time and make the system learn what institutions can handle what questions.
Our project partner Nekom offers Know-How and software development in Marketing, Retail and Multichannel E-Commerce. They are also involved in creating an online platform for social organizations to improve the process of serving their clients needs.

Conference Assistent

Conference Assistant is an Android and iOS App developed by the Know-Center with the goal of providing support for the conference organizers as well as conference attendees. Main features of the Conference Assistant App are to provide fast access to the conference schedule, location of the session rooms, information on speakers as well as details on talks and other related events. So far it has been used to support several public conferences such as the World Usability Congress, i-KNOW and Sandbox. ScaR was successfully integrated in the Conference Assistant with the goal of providing personalized recommendations to users. Two types of entities are recommended - talks and people. ScaR utilizes the data from the user and uses traditional recommendation algorithms to match similar users based on their interests or to recommend them talks they might be interested in.
Demo instance:

Location Based Recommender

Together with the company Blanc Noir the Know-Center developed a recommender system for the use in both, online and offline marketplaces. While in online marketplaces (e.g., product web shops) a user solely interacts through a virtual environment, in offline marketplaces (e.g., shopping mall) the user physically interacts with the store. The core of the project was to recognize the different activity patterns a user does in a virtual environment (searches through products, liking a Facebook page, etc.) and physical environments (e.g., walks through the shopping mall, dwells in front of a specific store, etc.) and to combine those. In order to achieve the goals, the project has focused on incorporating physical environments (e.g., shopping malls into the loop) by using IPS beacon technology. As such, physical interactions can be tracked in the real world and be leveraged for creating recommendations in the virtual. The other way around also applies, as a user’s online behaviour can mapped and leveraged in the physical world, too.
For this use case check out the online demo


The Data Market Austria (DMA) offers a platform to bring datasets, data services, consulting, and infrastructure offers to a common marketplace. The recommender systems included in DMA analyses all offerings, to derive suggestions for collaboration between them, like which dataset could be best processed by which data service. The suggestions should help the costumers on DMA to identify new collaborations reaching beyond traditional industry boundaries to get in touch with new clients or suppliers in the digital domain. Human brokers will work together with the recommender system to set up data value chains matching different offers to create a data value chain solving the problems in various domains. In its final expansion stage, DMA is intended to be a central hub for all actors participating in the Austrian data economy, regardless of their industrial and research domain to overcome traditional domain boundaries.

Ideas on arising new businessmodels coming with such a ecosystem have been published at the New Business Models Conference 2017 in Graz. (Procs )
M. Traub, H. Gursch, E. Lex, R. Kern. Data Market Austria - Austria's First Digital Ecosystem for Data, Businesses, and Innovation.

Music Recommender

Older people are often faced with dementia. It has been shown, that people with this illness still respond to music they like. Therefore we are developed a music recommender system, which grounds its decisions in biomedical data. All data transfer and functionality is implemented on an android device. In a first step we conducted a study. During this study users had to listen to music while we measured their physiological feedback with a wearable device. After this we developed algorithms that are able to predict the user feedback while listening to a song. In a parallel process we developed a recommender system that link songs with each other (collaborative filtering). In the end we combined all three research and development processes in a mobile application that automatically plays songs to the listener that he or she likes.

Learning Resources

In course of the H2020 project AFEL (Analytics for Everyday Learning), ScaR is used as recommender engine to suggest learning resources to users. Thus, we use concepts of Learning Analytics in order to design a recommender system, which not only accounts for the interests of a user but also for the user's competence level. Our learning resource recommender system will be evaluated in an online study within the Spanish learning platform Didactalia
Next to the Know Center there are 5 other partners involved in the AFEL project beeing GNOSS, Knowledge Media Research Center Tübingen, Leibniz Universitaet Hannover, National University of Ireland Galway and The Open University

Hotel Booking


Together with the fashion retailer MyManou, we developed an E-commerce recommender system. Therefore, we utilized various Collaborative-Filtering and Content-Based Filtering algorithms for the personlized recommendation of fashion articles to users. Apart from that, we introduced filter criteria (e.g., price range, colour, etc.) to enable real-time filtering possibilities for the recommendation results.

Ticket Recommender