Link Prediction in a Semi-bipartite Network for Recommendation

Authors: 
Aastha Nigamand Nitesh V. Chawla
Citation: 
Proceedings of the Asian Conference on Intelligent Information and Database Systems (ACIIDS), pp. 127–135,2016
Publication Date: 
March, 2016

There is an increasing trend amongst users to consume information from websites and social media. With the huge influx of content it becomes challenging for the consumers to navigate to topics or articles that interest them. Particularly in health care, the content consumed by a user is controlled by various factors such as demographics and lifestyle. In this paper, we use a semi-bipartite network model to capture the interactions between users and health topics that interest them. We use a supervised link prediction approach to recommend topics to users based on their past reading behavior and contextual data associated to a user such as demographics.