Mr. DLib

RARD: The Related-Article Recommendation Dataset

We are proud to announce the release of ‘RARD’, the related-article recommendation dataset from the digital library Sowiport and the recommendation-as-a-service provider Mr. DLib. The dataset contains information about 57.4 million recommendations that were displayed to the users of Sowiport. Information includes details on which recommendation approaches were used (e.g. content-based filtering, stereotype, most popular), what types of features were used in content based filtering (simple terms vs. keyphrases), where the features were extracted from (title or abstract), and the time when recommendations were delivered and clicked. In addition, the dataset contains an implicit item-item rating matrix that was created based on the recommendation click logs. RARD enables researchers to train machine learning algorithms for research-paper recommendations, perform offline evaluations, and Read more…

By Joeran Beel, ago
Mr. DLib

Mr. DLib 1.2 released: JabRef recommendations completed; CORE recommendation API connected

There are two major news coming along with the new version of Mr. DLib’s Recommendation API. JabRef finally uses Mr. DLib for it’s recommender system We have announced this already a while ago, but now, finally, Mr. DLib’s recommendations are available in one of the most popular open-source reference managers, i.e. JabRef. Currently, Mr. DLib enables JabRef users to retrieve a list of related-article recommendations, given a currently selected entry in the reference list (see screenshot). In the long run, we aim for creating personalized recommendations, too. Mr. DLib is not the only provider of recommendations-as-a-service in Academia. Another provider is the CORE project, with whom we partnered now. CORE is offering an API similar to the one we offer. We Read more…

By Joeran Beel, ago
Publications

Paper accepted at ISI conference in Berlin: “Stereotype and Most-Popular Recommendations in the Digital Library Sowiport”

Our paper titled “Stereotype and Most-Popular Recommendations in the Digital Library Sowiport” is accepted for publication at the 15th International Symposium on Information Science (ISI) in Berlin. Abstract: Stereotype and most-popular recommendations are widely neglected in the research-paper recommender-system and digital-library community. In other domains such as movie recommendations and hotel search, however, these recommendation approaches have proven their effectiveness. We were interested to find out how stereotype and most-popular recommendations would perform in the scenario of a digital library. Therefore, we implemented the two approaches in the recommender system of GESIS’ digital library Sowiport, in cooperation with the recommendations-as-a-service provider Mr. DLib. We measured the effectiveness of most-popular and stereotype recommendations with click-through rate (CTR) based on 28 million delivered Read more…

By Joeran Beel, ago
Help Wanted

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 help us implementing the ideas. There is also a good chance that we get more funding, maybe also for Bachelor students and postdoctoral researchers. The positions will be located in Tokyo, Copenhagen or Konstanz (Germany). In the following, there is a list of potential projects. If you are interested, please apply, and if you have own ideas, do not hesitate to discuss them with us. What exactly you Read more…

By Joeran Beel, ago
Recommender Systems

Research Paper Recommender Systems: A Literature Survey (Preprint)

As some of you might know, I am a PhD student and the focus of my research lies on research-paper recommender systems. Now, I am about to finish an extensive literature review of more than 200 research articles on research paper recommender systems. My colleagues and I summarized the findings in this 43-page preprint. The preprint is in an early stage, and we need to double check some numbers, improve grammar etc. but we would like to share the article anyway. If you are interested in the topic of research paper recommender system, it hopefully will give you a good overview of that field. The review is also quite critical and should give you some good ideas about the current problems Read more…

By Joeran Beel, ago
Recommender Systems

New pre-print: “Research Paper Recommender System Evaluation: A Quantitative Literature Survey”

As you might know, Docear has a recommender system for research papers, and we are putting a lot of effort in the improvement of the recommender system. Actually, the development of the recommender system is part of my PhD research. When I began my work on the recommender system, some years ago, I became quite frustrated because there were so many different approaches for recommending research papers, but I had no clue which one would be most promising for Docear. I read many many papers (far more than 100), and although there were many interesting ideas presented in the papers, the evaluations... well, most of them were poor. Consequently, I did just not know which approaches to use in Docear. Meanwhile, we reviewed all these papers more carefully and analyzed how exactly authors conducted their evaluations. More precisely, we analyzed the papers for the following questions.

  1. To what extent do authors perform user studies, online evaluations, and offline evaluations?
  2. How many participants do user studies have?
  3. Against which baselines are approaches compared?
  4. Do authors provide information about algorithm’s runtime and computational complexity?
  5. Which metrics are used for algorithm evaluation, and do different metrics provide similar rankings of the algorithms?
  6. Which datasets are used for offline evaluations
  7. Are results comparable among different evaluations based on different datasets?
  8. How consistent are online and offline evaluations? Do they provide the same, or at least similar, rankings of the evaluated approaches?
  9. Do authors provide sufficient information to re-implement their algorithms or replicate their experiments?
(more…)

By Joeran Beel, ago
Help Wanted

We need your help (i.e. a server) to build a repository for academic PDF files

It's a while ago that we started crawling the Web for academic PDFs to index them and use them for Docear's research paper recommender system. Meanwhile, we have collected quite a few PDFs.  Unfortunately, in a foreseeable future, our servers' disks will be full and the load of our servers is too high already (that's why you sometimes won't get recommendations in Docear - our servers simply are too busy). Since our budget is tight and we don't want to spend too much time for server administration neither, we are asking for your help: Do you have a server that you could spare? What we need is the following (more…)

By Joeran Beel, ago