Abstract: A system, apparatus, and method are provided for retrieving new content from a multitude of web feeds (e.g., hundreds of thousands). Each of multiple sources hosts one or more web feeds (e.g., RSS feeds, Atom feeds), and is categorized based on how frequently new content is found in the source's feeds. Each source has a corresponding status indicator whose value increases (to a maximum value), each time the source's feeds are serviced (e.g., polled, downloaded) and new content is received, or decreases (to a minimum value) if no new content is received and the indicator has not been recently decreased. Each category of web feed source corresponds to a range of possible status indicator values, and a given source may dynamically move between categories as its status indicator value changes.

Abstract: A system, method, and apparatus are provided for disambiguating organization names. Selected names that are shared among multiple organizations may or may not be categorized or characterized (e.g., by industry, by size, by reach). As content items are received (e.g., news stories, magazine articles, social media content), occurrences of the selected names are identified. Each item that includes at least one name is processed to determine which of the multiple entities that have the name (if any) is the organization referenced or mentioned in the item. The same model may be applied to disambiguate all names or, depending on the name's categorization, different models or procedures may be applied to disambiguate the name.

Abstract: A system, method, and apparatus are provided for disambiguating personal names. Selected names that are shared among multiple people are categorized or characterized as either famous or non-famous and, if non-famous, are also designated as common or uncommon. As content items are received (e.g., news stories, magazine articles, social media content), occurrences of the selected names are identified. Each item that includes at least one name is then processed to determine which of the multiple people that have the name (if any) is the person referenced in the item. Depending on the name's categorization (e.g., famous, non-famous and common, non-famous and uncommon), different models or procedures may be applied to disambiguate the occurrence of the name.

Abstract: A system, apparatus, and method are provided for configuring web feeds for users/subscribers. A user identifies a topic of interest, which may be a person, organization, event, place, concept, or other thing. A content processor analyzes content items received from a multitude of publishers' feed to identify their topics. A relevance score regarding each associated topic is calculated for each of the received items and, for each user that subscribed to the topic, the item's relevance score is compared to a user-specific threshold relevance score to determine whether to add it to the user's feed. The threshold relevance score for a topic for a user is computed based on factors such as the average relevance scores of the topic's content items, the standard deviation, the user's relationship with the topic, a target frequency with which items corresponding to the topic should be offered to the user, etc.

Abstract: A system, apparatus, and method are provided for retrieving new content from a multitude of web feeds (e.g., hundreds of thousands). Each of multiple sources hosts one or more web feeds (e.g., RSS feeds, Atom feeds), and is categorized based on how frequently new content is found in the source's feeds. Each source has a corresponding status indicator whose value increases (to a maximum value), each time the source's feeds are serviced (e.g., polled, downloaded) and new content is received, or decreases (to a minimum value) if no new content is received and the indicator has not been recently decreased. Each category of web feed source corresponds to a range of possible status indicator values, and a given source may dynamically move between categories as its status indicator value changes.