Title: A hybrid framework for social tag recommendation using context driven social information

Authors: Gerard Deepak; Sheeba Priyadarshini

Addresses: Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India ' Department of Computer Science, St. Joseph's College (Autonomous), Langford Road, Bangalore, India

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%.

Keywords: adaptive normalised compression distance; ANCD; agent oriented; recommendation system; semantic collaborative filtering; social information; social tagging; social web.

DOI: 10.1504/IJSCCPS.2016.084759

International Journal of Social Computing and Cyber-Physical Systems, 2016 Vol.1 No.4, pp.312 - 325

Received: 03 Jan 2017
Accepted: 19 Feb 2017

Published online: 25 Jun 2017 *

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