Learning personal preferences on online newspaper articles from user behaviors

Abstract

This paper discusses methods by which user preferences for WWW-based newspaper articles can be learned from user behaviors. Two modes of inference were compared in an experiment: one using explicit feedback and the other using implicit feedback. In the explicit feedback mode, the users score all articles according to their relevance. In the implicit feedback mode, the user reads articles by performing scrolling and enlarging operations, and the system infers from the operations how much the user was interested in each article. Our newspaper on the WWW, called ANATAGONOMY, has a learning engine and a scoring engine on the server. The system users read daily news articles by using a WWW browser in which there is an interaction agent that monitors the user behaviors. The learning engine on the server infers user preferences from the interaction agent, and the scoring engine scores new articles and creates personalized newspaper pages based on the extracted user profiles. In an experiment, the system was able to personalize the newspaper to some extent when using only implicit feedback when some parameters were properly set, but the personalization was not as precise as it was when explicit feedback was used. By mixing explicit feedback with implicit feedback, the system could personalize newspapers quickly and precisely without requiring too much effort on the part of the users. User preferences can also be used to construct information retrieval agents or even to create cyberspace communities of the users that have similar interests. We think that the proposed technique for learning user preferences greatly enhances the value of the WWW.