Online learning with social computing based interest sharing

Communities on the Internet are highly self-organizing, dynamic, and ubiquitous. One objective of peers in such a community is sharing common interests, even when compromising privacy. This paper presents a model for peers on the Internet that allows them to discover their common interests in terms of sets of frequently visited URLs. This model assists online learning by automatically presenting users with URLs related to what they are currently browsing, thus saving users’ time searching for additional information and helping to educate them on the current topic. To implement the model and collect test data, FireShare was developed as a plugin for the popular Web browser Firefox. Data was collected and analyzed on the number of discovered frequently visited URL sets, relevancy of mined association rules, and the overhead FireShare imposes on a network. While FireShare favorably validated the proposed model, analysis of the submitted test data shows high potential for success with future versions.