Various positions to work on research-paper recommender systems (Mr. DLib) and Docear (Bachelor/Master/PhD/Post-Doc)

Updated on 2017-08-14: Here at Docear and Mr. DLib we have many exciting projects in the field of recommender systems, user modelling, personalisation, and adaptive systems (primarily with a focus on digital libraries but we are also open for domains such as health care, transportation, and tourism). If you are interested in pursuing any of […]

Students & PostDocs: We have open positions in Tokyo, Copenhagen, and Konstanz (2-24 months)

Update 2016-01-12: The salary in Tokyo would be around 1.600 US$ per month, not 1.400.

2015 has been a rather quiet year for Docear, but 2016 will be different. We have lots of ideas for new projects, and even better – we have funding to pay at least 1 Master or PhD student, to […]

New paper for UMAP’15: Exploring the Potential of User Modeling based on Mind Maps

One reason why we originally started the development of Docear was our interest in how people are creating mind-maps and how the information contained in mind-maps could be used for building recommender systems and other user-modeling applications. As a result of our we developed Docear’s research-paper recommender system, and if you are interested in how […]

Wanted: Participants for a User Study about Docear’s Recommender System

We kindly ask you to participate in a brief study about Docear’s recommender system. Your participation will help us to improve the recommender system, and to secure long-term funding for the development of Docear in general! If you are willing to invest 15 minutes of your time, then please continue reading.

Participate in the Study […]

Three new research papers (for TPDL’13) about user demographics and recommender evaluations, sponsored recommendations, and recommender persistance

After three demo-papers were accepted for JCDL 2013, we just received notice that another three posters were accepted for presentation at TPDL 2013 on Malta in September 2013. I think the papers are quite interesting (well, that’s probably no surprise since I wrote them ;-)). They cover some quite novel aspects of recommender systems relating […]