Automating data aggregation for collaborative filtering in Ruby on Rails

Collaborative filtering and information filtering are tried and proven methods of utilizing aggregated data about a website's users to provide catered content. Passive filters are one subset of such algorithms that utilize data about a user's interactions with a website in viewing content, purchasing items, etc. My work develops a set of extensions for Ruby on Rails that, when inserted into an existing application, will comprehensively log information associated with different types of user interactions to provide a sound base for many passive filter implementations. The extensions will log how users interact with the application server (content accessed, forms submitted, etc) as well as how users interact with that content on their own browser (scrolling, AJAX requests, JavaScript calls, etc). Given existing open-source collaborative filtering algorithms, the ability to automatically aggregate user-interaction data in any arbitrary Rails application significantly decreases the barrier to implementing passive filtering in an already efficient agile web development framework. Further, my work utilizes the logged data to implement a web interface to view analytic information about the components of an application.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Malconian, Daniel R

Advisor dc:contributor.advisor

• Edward Barrett.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/46016

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/46016