1. 1 JCDL 2013 Report Kazunari Sugiyama WING meeting 23 rd August, 2013

2. 2 Outline of JCDL13 Venue – Indianapolis, Indiana, USA x Indianapolis JW MarriottX

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4. 4 Outline of JCDL13 Review Process – For each submission, (1) 3 reviewers read and rate for each paper, (2) Then each paper was read by 2 additional meta-reviewers Acceptance rate – 29.9% [50 / 167] Full paper : 28 / 95 (29.4%) Short paper: 22 / 72 (30.6%) Future JCDL – 2014: London, UK 8-12 Sep., Joint with TPDL (Theory and Practice of Digital Libraries) – 2015, 2016: Tennessee or New York – 2017: European country

5. Nominees for Best Papers W. Ke: “Information-theoretic Term Weighting Schemes for Document Clustering” A. Hinze and D. Bainbridge: “Tipple: Location-Triggered Mobile Access to a Digital Library for Audio Books” P. Bogen, A. McKenzie, and R. Gillen: “Redeye: A Digital Library for Forensic Document Triage” 5 K. Sugiyama and M.-Y. Kan: “Exploiting Potential Citation Papers in Scholarly Paper Recommendation” Vannevar Bush Best Paper Award

6. Nominees for Best Student Papers E. Momeni, K. Tao, B. Haslhofer, and G.-J. Houben: “Identification of Useful User Comments in Social Media: A Case Study on Flickr Commons” S. Ainsworth and M. Nelson: “Evaluating Sliding and Sticky Target Policies by Measuring Temporal Drift in Acyclic Walks Through a Web Archive” S. D. Torres, D.Hiemstra, and T. Huibers: “Vertical Selection in the Information Domain of Children” S. Tuarob and L. C. Pouchard, and C. Lee Giles: “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” 6

7. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Outline] Automatic annotation of metadata 7 Tag recommendation

8. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Approach] TF-IDF Topic model Baseline: – I. H. Witten et al.: “KEA: Practical Automatic Keyphrase Extraction (DL’99) 8

9. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Experimental Data] The Oak Ridge National Laboratory Distributed Active Archive Center (DAAC) Dryad Digital Repository (DRYAD) The Knowledge Network for Biocomplexity (KNB) TreeBASE: A Repository of Phylogenetic Information (TreeBASE) 9

10. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Evaluation Measures] Precision, Recall, F1 Mean Reciprocal Rank (MRR) Binary Preference (Bpref) – A measure that can take the order of recommended tags into account 10

11. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Experimental Results] 11

12. “Automatic Tag Recommendation for Metadata Annotation Using Probabilistic Topic Modeling” [Example of recommended tags] 12 15/207/202/20