Web augmentation techniques allow the adaptation of websites on client side using browser extensions or plug-ins designed to run dedicated user scripts. However, while number and variety of such scripts from publicly available repositories have grown remarkably in recent years, they usually neglect the user’s personal prole or individual preferences, and therefore fail to provide enhanced personalized services. At the same time recommender systems have become powerful tools to improve personalization on the Web. Yet, many popular websites lack this functionality, e. g., for missing nancial incentive. Therefore, we present a novel approach to empower user script developers to build more personalized augmenters by utilizing collaborative ltering functionality as an external service. Thus, script writers can build recommender systems into arbitrary websites, in fact operating across multiple website domains, while guarding privacy and supplying provenance information. This paper discusses the architecture of the proposed approach, including real-world application scenarios, and presents our tool kit and publicly available prototype. The results show the feasibility of combining Web augmentation with recommender systems, to empower the crowd to build new kinds of applications for a more personalized browsing experience.
['Martin Wischenbart', 'Sergio Firmenich', 'Gustavo Rossi', 'Manuel Wimmer']
Recommender Systems for the People — Enhancing Personalization in Web Augmentation