A Recommendation System for Online Social Feeds by Exploiting User Response Behaviour
Date Issued
2012
Date
2012
Author(s)
Soh, Ping-Han
Abstract
In recent years, online social networks have been dramatically expanded. Active users spend hours communicating with each other via these networks such that an enormous amount of data is created every second.
The tremendous amount of newly created information costs users much time to discover interesting messages from their online social feeds. The problem is even exacerbated if the users access these networks via mobile devices. To help users discover interesting messages efficiently,
in this paper, we propose a new approach to recommend interesting messages for each user by exploiting the user''s response behaviour. The proposed approach is then demonstrated to be easily extended to deal with the temporal recommendation. We investigate the response behaviour on the most popular social network, and the experimental results show that the proposed approach provides obvious improvement over the current online social feeds.
Subjects
Recommendation System
Online Social Networks
User Response Behaviour
Type
thesis
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