关键词: 网络爬虫, 个性化阅读, 建构主义学习理论, 领域资讯

Abstract: Network news reading has become the main way for personal knowledge acquisition in the Internet era. Wherein, how to improve the efficiency of acquiring news should be the core goal for personalized news service systems. In order to automatically gather suitable news for personalized requirements, depth-first and breadth-first Web page extraction strategies are firstly studied, and then a constructivist cognition based extraction model with balanced combined random walking strategy is newly proposed. The new model is based on the idea of constructivist learning theory of human. It models and simulates the process of progressively browsing and cognizing news information of users. The experimental analysis on healthy domain Web pages shows that, the proposed method can better simulate the human browsing selection procedures, which will supply a fundamental manner for developing personalized news extraction service system.

Key words: Web crawler, personalized reading, constructivist learning theory, domain news