Keyword Ranking Personalization via Document Concentration Weighting

The patent segments users into different user groups based on weblog data characteristics, allowing keyword rankings to be customized for each segment. This enables personalized keyword provision without requiring complete system redesign, as each user group receives tailored results based on their specific interests and behaviors.

The patent applies local quality by providing different keyword rankings to different user groups based on their specific characteristics. Each user group receives keyword results that are locally optimized for their interests, rather than a uniform approach for all users, thereby improving adaptability while maintaining manageable system complexity.

The patent performs preliminary actions by pre-analyzing weblog data to identify user group characteristics and interests before keyword ranking. This advance preparation allows the system to efficiently provide relevant keyword information to each user group without requiring complex real-time processing, thereby reducing information loss while managing processing complexity.

The patent implements self-service by utilizing existing weblog data that users have already generated, rather than requiring additional explicit user input. The system automatically analyzes this data to determine user group characteristics and provide tailored keyword rankings, reducing the burden on users while improving information relevance.