Modeling and Learning User Profiles for Personalized Content Service

Heung-Nam Kim, Inay Ha, Seung-Hoon Lee, and Geun-Sik Jo

Abstract. With the spread of the digital library and the web, users can obtain a wide variety of information, and also can access novel content. In this environment, finding useful information from a huge amount of available content becomes a time consuming process. In this paper, we focus on user modeling for personalization to recommend content relevant to user interests. We exploit the data mining techniques for identifying useful and meaningful patterns of users. Each user model, collectively called PTP (Personalized Term Pattern), is represented as both interest patterns and disinterest patterns. We present empirical experiments using NSF research award datasets to demonstrate our approach and evaluate performance compared with existing methods.

LNCS 4822, p. 85 ff.

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