A hybrid framework for social tag recommendation using context driven social information Online publication date: Sun, 25-Jun-2017
by Gerard Deepak; Sheeba Priyadarshini
International Journal of Social Computing and Cyber-Physical Systems (IJSCCPS), Vol. 1, No. 4, 2016
Abstract: Tagging images uploaded on the web is important as tags serve as the primary entities for future retrieval of these images. The numbers of images uploaded to the web via social networking sites is increasing in the era of smart phone technology and the internet. These images need to be tagged correctly which would ease its future retrieval. With the emergence of the Web 3.0 which is a standard for semantic web, a semantic tag recommender is desirable. In this paper, a context aware social tagger is proposed which recommends high quality tags by using varied contexts of social information. A semantic collaborative filtering strategy is proposed to make the social tagger semantics compliant. The social tagger also encompasses an intelligent agent, driven by second order co-occurrence pointwise mutual information strategy to increase the relevance and quality of the recommended tags. The proposed social tagger yields an average accuracy of 84.04%.
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 Social Computing and Cyber-Physical Systems (IJSCCPS):
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