Case Study: KPN Research achieves personalized, portable webpage recommendations with Franz Inc.'s Allegro CL solution

KPN Research - Customer Case Study

KPN Research, the research arm of the largest Dutch telecom operator, needed a portable webpage recommender for a Living Room webpad: a bookmark-based browser that’s as simple as a TV remote, personalized for each family member and able to recommend sites based on interests and surfing patterns. Facing the need to process millions of URLs under a tight six-month schedule, KPN Research chose Franz Inc.’s Allegro CL platform and related products—AllegroStore, AllegroServe—and the Clementine neural network engine to build the system.

Using Franz Inc.’s Allegro CL stack, KPN implemented a Common Lisp recommendation engine combining user profiling, neural-network document classification and collaborative filtering, with a client-side “Personal Browser” that merges favorites and system recommendations. AllegroStore handled persistent CLOS objects and AllegroServe provided prefetching and an editorial interface; Franz Inc. also provided timely support during development. The system was built in about six months by a small team, tested in user pilots, and is now under review by publishers for knowledge-management and targeted content distribution—demonstrating rapid delivery, scalable architecture for large URL volumes, and clear market interest.