1. Research Paper Recommender System Monica D ă g ă diţ ă

2. Outline  Article recommender systems  Why Scienstein?  Citation analysis methods  Text mining  Document rating  User interface  Conclusions

3. Article recommender systems  Purpose : find relevant articles  Methods used  Content based filtering  Collaborative filtering  Key elements of an article  Citations  Author  Content

4. Why Scienstein?  2008 - PhD students Béla Gipp and Jöran Beel  Appeared as an alternative to academic search engines  Improves simple keyword-based search  Citation analysis  Distance Similarity Index (DSI)  In-text Impact Factor (ItIF)  Author analysis  Source analysis  Implicit/explicit ratings

5. Citation analysis methods  Problems  Homographs  The Mathew Effect  Self citations  Citation circles  Ceremonial citations  Scienstein’s approach – 4 citation analysis methods

6. Citation analysis methods(2)  Cited by  Papers that cite the input document – A&B  Reference list  Papers referenced in the input document – C&D  Bibliographic coupling  Papers that cite the same article(s) – BibCo  Co-citation  Papers cited in the same document – CoCit

7. Citation analysis methods(3)  In-text citation frequency analysis (ICFA)  the frequency with which a research paper is cited within a document  In-text Impact Factor (ItIF)  The higher the ItIF, the closer related is the input document to the cited document

8. Citation analysis methods(4)  In-text citation distance analysis (ICDA)  the distance between references within a text -> the degree of their similarity  Distance Similarity Index (DSI)  calculates the similarity of two documents based on the citation distance OccurenceValue Sentence1 Paragraph1/2 Section1/4 Chapter1/8 Other1/16

9. Text mining  Existing techniques  Additional features  Classification based on details given in the acknowledgements section  Collaborative annotations and classifications  Creating new categories  classifying publications about archaeological sites according to their geographic location -> Google Maps Extension

10. Document rating  Explicit ratings  Improve a user’s own recommendation accuracy  Problem: a large amount is needed  Implicit ratings  Time spent with mouse over a paragraph  Time spent reading an article  Printed articles

11. User interface

12. Conclusions  Scienstein - the first hybrid recommender system for research papers  Known methods  Keyword analysis  Ratings  New methods  In-text Impact Factor (ItIF)  Distance Similarity Index (DSI)  Hybrid system (content based and collaborative filtering) => more powerful tool

13. Questions ?