出版社：International Association for Computer Information Systems
摘要：People spend far more time searching information over the Internet than using it, because the desired information is often buried within a long list of searched results. Personalized internet access is a feasible solution to solve this search vs. use dilemma, which helps identify the web documents users truly need. A user’s interests are usually represented by a profile. In this research, an improved vector space model representation is proposed to improve the user interests management efficiency. Based on this, the research further proposes a method for user multi- interest modeling integrated with semantic similar network (SSN). It studies the feature selection in user modeling, and proposes a feature selection method combining TF and TF-IDF that is proved a better performance in the test. Finally a complete module design is presented, which provides a personalized recommendation system for practical applications.
关键词：personalization; information retrieval;vector space model; semantic similar network
