%0 Book Section %1 younus2014utilizing %A Younus, Arjumand %A Qureshi, MuhammadAtif %A Manchanda, Pikakshi %A O’Riordan, Colm %A Pasi, Gabriella %B Social Informatics %D 2014 %E Aiello, LucaMaria %E McFarland, Daniel %I Springer International Publishing %K deeplearning dnn learning machine ml negative recommender sample sampling %P 384--395 %R 10.1007/978-3-319-13734-6_28 %T Utilizing Microblog Data in a Topic Modelling Framework for Scientific Articles’ Recommendation %U http://dx.doi.org/10.1007/978-3-319-13734-6_28 %V 8851 %X Researchers are actively turning to Twitter in an attempt to network with other researchers, and stay updated with respect to various scientific breakthroughs. Young and novice researchers have also found Twitter as a valuable source of information in terms of staying up-to-date with various developments in their field of research. In this paper, we present an approach to utilize this valuable information source within a topic modeling framework to suggest scientific articles of interest to novice researchers. The approach in addition to producing effective recommendations for scientific articles alleviates the cold-start problem and is a step towards elimination of the gap between Twitter and science. %@ 978-3-319-13733-9

@incollection{younus2014utilizing, abstract = {Researchers are actively turning to Twitter in an attempt to network with other researchers, and stay updated with respect to various scientific breakthroughs. Young and novice researchers have also found Twitter as a valuable source of information in terms of staying up-to-date with various developments in their field of research. In this paper, we present an approach to utilize this valuable information source within a topic modeling framework to suggest scientific articles of interest to novice researchers. The approach in addition to producing effective recommendations for scientific articles alleviates the cold-start problem and is a step towards elimination of the gap between Twitter and science.}, added-at = {2015-01-27T10:39:31.000+0100}, author = {Younus, Arjumand and Qureshi, MuhammadAtif and Manchanda, Pikakshi and O’Riordan, Colm and Pasi, Gabriella}, biburl = {https://www.bibsonomy.org/bibtex/2ecd7b8477f7cacb2d5078b8abb3ee9a8/jaeschke}, booktitle = {Social Informatics}, doi = {10.1007/978-3-319-13734-6_28}, editor = {Aiello, LucaMaria and McFarland, Daniel}, interhash = {6b2b03914baaa0eb1b357d964fc00377}, intrahash = {ecd7b8477f7cacb2d5078b8abb3ee9a8}, isbn = {978-3-319-13733-9}, keywords = {deeplearning dnn learning machine ml negative recommender sample sampling}, language = {English}, pages = {384--395}, publisher = {Springer International Publishing}, series = {Lecture Notes in Computer Science}, timestamp = {2021-06-18T10:33:46.000+0200}, title = {Utilizing Microblog Data in a Topic Modelling Framework for Scientific Articles’ Recommendation}, url = {http://dx.doi.org/10.1007/978-3-319-13734-6_28}, volume = 8851, year = 2014 }