Recommending Scientific Collaboration from ResearchGate

Scientific collaboration improves researchers productivity by providing a way to share new ideas, learn new techniques, and find new research applications, increasing the chance to access funding. Beyond ethics and reciprocity, there are other important aspects on achieving scientific collaborations, such as research interests and expected productivity gain, that are paramount to a successful partnership. However, achieve effective collaborations is a hard work and can drain researchers time. In this work, we propose a recommendation approach that uses different strategies to suggest scientific collaboration for researchers based on their research interest. In particular, our approach exploits ResearchGate, a well known research social network from where research interests and researchers production are used to model similarity between them. Experimental results show that the content-based strategy outperforms neighborhood-based collaborative filtering strategies to recommend scientific collaboration with gains of up 16.60% in precision, 37.19% in recall, and 21.16% in F1 for the top-20 recommendation lists.

[1]  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.

[2]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[3]  Thomas Hofmann,et al.  Latent Class Models for Collaborative Filtering , 1999, IJCAI.

[4]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.

[5]  Schubert Foo,et al.  Rec4LRW - Scientific Paper Recommender System for Literature Review and Writing , 2015, ICADIWT.

[6]  Amanda Spink,et al.  Real life, real users, and real needs: a study and analysis of user queries on the web , 2000, Inf. Process. Manag..

[7]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[8]  Charu C. Aggarwal,et al.  Recommender Systems: The Textbook , 2016 .

[9]  Yoav Shoham,et al.  Content-Based, Collaborative Recommendation. , 1997 .

[10]  Silvana Greca,et al.  Towards a Modular Recommender System for Research Papers written in Albanian , 2014, ArXiv.

[11]  Louise Guthrie,et al.  Another Look at the Data Sparsity Problem , 2006, TSD.

[12]  Carlo Tasso,et al.  A Personalized Concept-driven Recommender System for Scientific Libraries , 2014, IRCDL.

[13]  Bradley N. Miller,et al.  Social Information Filtering : Algorithms for Automating “ Word of Mouth , ” , 2017 .

[14]  D. McLeod,et al.  Collaborative Filtering for Information Recommendation Systems , 2006 .

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

[16]  Amr M. Tolba,et al.  Exploiting Publication Contents and Collaboration Networks for Collaborator Recommendation , 2016, PloS one.

[17]  Atsuhiro Takasu,et al.  Collaborator Recommendation for Isolated Researchers , 2014, 2014 28th International Conference on Advanced Information Networking and Applications Workshops.

[18]  Leonid Zhukov,et al.  Co-author Recommender System , 2016 .

[19]  Richard Van Noorden.  Online collaboration: Scientists and the social network , 2014, Nature.

[20]  Jöran Beel,et al.  Scienstein : A Research Paper Recommender System , 2009 .

[21]  Raj Jain,et al.  The art of computer systems performance analysis - techniques for experimental design, measurement, simulation, and modeling , 1991, Wiley professional computing.

[22]  Schubert Foo,et al.  A Framework for Scientific Paper Retrieval and Recommender Systems , 2016, ArXiv.

[23]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[24]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[25]  Kamal Kant Bharadwaj,et al.  Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities , 2011, Expert Syst. Appl..

[26]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .