TY  - JOUR
T1  - Academic Tweet Concept Based Co-author Recommendation
AU - Manju, G. AU - Geetha, T.V. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 19
SP  - 3763
EP  - 3769
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.3763.3769
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.3763.3769
KW  - Co-author recommendation
KW  -lambda rank
KW  -twitter
KW  -semantic relatedness
KW  -concept
AB  - Researchers carrying out research and writing research article requires knowledgeable person in their topic to assist them in successfully publishing the study. Hence, this study presents a solution to this problem by recommending suitable co-authors for a particular topic. We identify co-researchers by incorporating researchers social similarity along with the traditional features like proficiency in a research area, semantic similarity of research interests and publication details. We have determined the social similarity of the researcher based on the Twitter social network. We determine the concept, social and difference topic similarity between the researchers and rank the co-authors using Lambda rank algorithm. We investigated the approach by carrying out experiments with datasets of academic publications in the area of computer science. The experimental results illustrates that the combination of social and semantic features provides better recommended list of co-authors, when compared to baseline approach.
ER  -