Abstract: A content delivery system for generating personalized content for a user. The system maintains an interest graph that shows the user's current attachment to one or more topics. When a user performs an action, a topic is determined for the action and the user's interest graph is modified based on the action. The system also receives content and analyzes the language of the content to determine a topic of the content. A similarity between the user's interests and the content is determined. The content is also analyzed to determine the popularity of the content. The user's interest level and the popularity of the content are then used to provide the user with a personalized content, such as a content recommendation or enhanced content.

Abstract: A content delivery system for generating personalized content for a user. The system maintains an interest graph that shows the user's current attachment to one or more topics. When a user performs an action, a topic is determined for the action and the user's interest graph is modified based on the action. The system also receives content and analyzes the language of the content to determine a topic of the content. A similarity between the user's interests and the content is determined. The content is also analyzed to determine the popularity of the content. The user's interest level and the popularity of the content are then used to provide the user with a personalized content, such as a content recommendation or enhanced content.

Abstract: A system and a method for analyzing conversational data comprising colloquial or informal terms and having an informal structure. A corpus of training language maps, each associated with an entity, is generated from conversational data retrieved from sources previously associated with entities. Subsequently received conversational data is processed to generate a conversational language map which is compared to a plurality of the stored training language maps. A confidence value is generated describing the similarity of the conversational language map to each of the plurality of the stored training language maps. The entity associated with the training language map having the highest confidence value is then associated with the conversational language map.