Abstract: Improved browsing experience in social bookmarking by leveraging aspects of self tagging and prediction. Quality recommendations are provided for sites of interest to the user and information about what types of people like the current website. Self-tagging is used as an effective means to perform personalized searches. Machine learning and reasoning is employed to predict self-tags based on a website visited and/or website behavior, and self-tags associated with a website and/or webpage based on content of that website and/or webpage. The architecture can be embodied as a browser utility to leverage and extend social-bookmarking information. The utility facilitates the display of information related to a summary view of the users who liked/disliked the current page or website, a tag cloud associated with webpages, and a recommendation button that causes self-tag recommendations to be displayed and that recommends links based on the combination of user tags and content.

Abstract: Improved browsing experience in social bookmarking by leveraging aspects of self tagging and prediction. Quality recommendations are provided for sites of interest to the user and information about what types of people like the current website. Self-tagging is used as an effective means to perform personalized searches. Machine learning and reasoning is employed to predict self-tags based on a website visited and/or website behavior, and self-tags associated with a website and/or webpage based on content of that website and/or webpage. The architecture can be embodied as a browser utility to leverage and extend social-bookmarking information. The utility facilitates the display of information related to a summary view of the users who liked/disliked the current page or website, a tag cloud associated with webpages, and a recommendation button that causes self-tag recommendations to be displayed and that recommends links based on the combination of user tags and content.