Rec4LRW - Scientific Paper Recommender System for Literature Review and Writing

In this paper, we introduce Rec4LRW, a recommender system (RS) for assisting researchers in finding research papers for their literature review and writing purposes. This system focuses on three researcher tasks – (1) Building a reading list of research papers, (2) Finding similar papers based on a set of papers, and (3) Shortlisting papers from the final reading list for inclusion in manuscript based on article type. A set of intermediate criteria are proposed to capture the relations between a research paper and its bibliography. The recommendation techniques for the three tasks in Rec4LRW are specifically devised on top of the intermediate criteria. The Rec4LRW workflow along with the screen designs for the three tasks is provided in this paper. The recommendation techniques in the system will be evaluated with state-of-the-art approaches along with user-based evaluation in subsequent studies.

[1]  Sean M. McNee,et al.  Don't look stupid: avoiding pitfalls when recommending research papers , 2006, CSCW '06.

[2]  M. C. Debachere,et al.  Problems in Obtaining Grey Literature , 1995 .

[3]  Nikolaos F. Matsatsinis,et al.  A System based on Multiple Criteria Analysis for Scientific Paper Recommendation , 2007 .

[4]  Jonathan Weese,et al.  UMBC_EBIQUITY-CORE: Semantic Textual Similarity Systems , 2013, *SEMEVAL.

[5]  San-Yih Hwang,et al.  A prototype WWW literature recommendation system for digital libraries , 2003, Online Inf. Rev..

[6]  Teresa P. Gordon,et al.  Meeting user information needs: The impact of major changes in FASB and GASB standards on financial reporting by colleges and universities , 2010 .

[7]  Simone Teufel,et al.  Topical PageRank: A Model of Scientific Expertise for Bibliographic Search , 2014, EACL.

[8]  Esma Aïmeur,et al.  A Multi-criteria Collaborative Filtering Approach for Research Paper Recommendation in Papyres , 2009, MCETECH.

[9]  Hung-Hsuan Chen,et al.  CSSeer: an expert recommendation system based on CiteseerX , 2013, JCDL '13.

[10]  Laurent Romary,et al.  HUMB: Automatic Key Term Extraction from Scientific Articles in GROBID , 2010, *SEMEVAL.

[11]  Vaughn F. Graham.  The literature review: a step-by-step guide for students , 2011 .

[12]  Jian Pei,et al.  Citation recommendation without author supervision , 2011, WSDM '11.

[13]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[14]  Ümit V. Çatalyürek,et al.  TheAdvisor: a webservice for academic recommendation , 2013, JCDL '13.

[15]  Jöran Beel,et al.  Introducing Docear's research paper recommender system , 2013, JCDL '13.

[16]  William W. Cohen,et al.  Recommendation : A Study in Combining Multiple Information Sources , 2007 .

[17]  Gail Hodge.  Systems of Knowledge Organization for Digital Libraries , 2000 .

[18]  M. M. Kessler.  Bibliographic coupling between scientific papers , 1963 .

[19]  Daniel Jurafsky,et al.  Who should I cite: learning literature search models from citation behavior , 2010, CIKM.

[20]  Philip Resnik,et al.  Using Information Content to Evaluate Semantic Similarity in a Taxonomy , 1995, IJCAI.

[21]  Eugene Garfield,et al.  The meaning of the Impact Factor , 2003 .

[22]  Sean M. McNee,et al.  Meeting user information needs in recommender systems , 2006 .

[23]  Christos Faloutsos,et al.  On Constructing Seminal Paper Genealogy , 2014, IEEE Transactions on Cybernetics.

[24]  Qing Li,et al.  Finding Relevant Papers Based on Citation Relations , 2011, WAIM.

[25]  Kiem Hoang,et al.  Scientific publication recommendations based on collaborative citation networks , 2012, 2012 International Conference on Collaboration Technologies and Systems (CTS).

[26]  Chris Cornelis,et al.  Using semi-structured data for assessing research paper similarity , 2013, Inf. Sci..

[27]  Stephen E. Robertson,et al.  Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval , 1994, SIGIR '94.

[28]  Kiem Hoang,et al.  Modeling Collaborative Knowledge of Publishing Activities for Research Recommendation , 2012, ICCCI.

[29]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[30]  C. Hart.  Doing a literature review: releasing the social science research imagination. , 1998 .

[31]  John Riedl,et al.  Automatically building research reading lists , 2010, RecSys '10.