Jans Aasman



Title: Building a Recommendation Engine for a Personalized Webpad Browser



Abstract  KPN Research, the Research Department of the largest Dutch telecom operator
is actively designing and testing out new broadband service concepts that make it easier for their customers to use the Internet. Recently, we initiated a pilot focused on the interface and services that a mobile webpad might have in the Living room area of the home.

Our goal was to create a bookmark-based browser interface that could be tailored to the information and entertainment needs of every family member, yet was as easy to use as a television remote.  It also needed provided recommendations to interesting web sites based on the users' interests and surfing patterns.

The base technology for this product is a Recommender System.  The task of the recommendation engine server is to monitor user behaviour and generate recommendations of web pages for each user. Our web page recommender system was built in Common Lisp using using Allegro CL, Allegrostore, Allegroserve and the Clementine neural network engine.

The solution that we build is unique in that we combined existing collaborative filtering techniques with advanced document classification software and detailed user monitoring. Secondly, we used hundreds of people to generate and validate an initial bookmark structure for a large number of
categories and subcategories.  Thirdly, the recommendation engine is integrated in a very user friendly personal browser that has been tested with real users in a number of pilots.