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?

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Docear welcomes Georgia M. Kapitsaki, a visiting researcher from Cyprus, who will be supporting us with our recommender system

Yesterday we welcomed Dr. Georgia M. Kapitsaki here in our main office in Magdeburg, Germany. Georgia is from the University of Cyprus and will stay one month with us. Her main interest lies in our research-paper recommender system and she will support us in improving our recommender system and performing some research. We Read more…

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. They cover some novel aspects of recommender systems relating to re-showing recommendations multiple times, considering user demographics when evaluating recommender systems, and investigating the effect of labelling recommendations. However, you can read the papers yourself, as we publish them as pre-print:

Paper 1: The Impact of Users’ Demographics (Age and Gender) and other Characteristics on Evaluating Recommender Systems (Download PDF | Doc)

In this paper we show the importance of considering demographics and other user characteristics when evaluating (research paper) recommender systems. We analyzed 37,572 recommendations delivered to 1,028 users and found that elderly users clicked more often on recommendations than younger ones. For instance, users with an age between 20 and 24 achieved click-through rates (CTR) of 2.73% on average while CTR for users between 50 and 54 was 9.26%. Gender only had a marginal impact (CTR males 6.88%; females 6.67%) but other user characteristics such as whether a user was registered (CTR: 6.95%) or not (4.97%) had a strong impact. Due to the results we argue that future research articles on recommender systems should report demographic data to make results better comparable.

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Docear at JCDL 2013 in Indianapolis (USA), three demo papers, proof-reading wanted

Three of our submissions to the ACM/IEEE Joint Conference on Digital Libraries (JCDL) were accepted. They relate to recommender systems, reference management, and pdf metadata extraction:

Docear4Word: Reference Management for Microsoft Word based on BibTeX and the Citation Style Language (CSL)

In this demo-paper we introduce Docear4Word. Docear4Word enables researchers to insert and format their references and bibliographies in Microsoft Word, based on BibTeX and the Citation Style Language (CSL). Docear4Word features over 1,700 citation styles (Harvard, IEEE, ACM, etc.), is published as open source tool on http://docear.org, and runs with Microsoft Word 2002 and later on Windows XP and later. Docear4Word is similar to the MS-Word add-ons that reference managers like Endnote, Zotero, or Citavi offer with the difference that it is being developed to work with the de-facto standard BibTeX and hence to work with almost any reference manager.

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