A personalized web agent with implicit feedback and hybrid filtering strategy

摘要

This paper presents a web agent called BookmarkFeeder that recommends web pages for a user. It learns user interests by reading user's bookmark items and monitoring the user behavior. Based on the automatically constructed user profile, it collects and filters web pages to recommend related web pages as bookmark items. BookmarkFeeder has two distinguished features compared with other web agents. First, it uses implicit feedback for learning. It learns the user's interests by monitoring the user behavior on the bookmark items without relying on explicit feedback. Second, it uses hybrid filtering strategy that uses URL-based recommendation in conjunction with content-based recommendation. By using the hyperlink information, a web page that contains no text can also be recommended. The performance of the proposed system is demonstrated through the experiments with untrained users.