An effective topic-based ranking technique for categorised research articles Online publication date: Sun, 25-Mar-2018
by Rajendra Kumar Roul; Jajati Keshari Sahoo
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 4, No. 1, 2018
Abstract: The number of research articles is increasing very rapidly on the web due to the large volume of research work happening everyday. Maintaining and searching the required articles according to the user requirements is the need of the hour. Classification and ranking are the two important techniques of information retrieval which can shed light in this direction. This paper proposes an effective ranking approach which is the follow-up of our earlier classification work in which by using the keywords extracted from the keyword section of the articles, a huge volume of articles are classified into their respective categories. To rank these articles in each category, the proposed ranking approach uses latent Dirichlet allocation which transforms the text into the topics and then applies inverted indexing technique on it. Five benchmark datasets are used for experimental work. Results obtained from the experiment indicate that the performance of the proposed ranking technique is promising.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Systems Engineering (IJCSYSE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook