Cox Communications, Inc.

Ryan M S Baker

Douglas David Gravino

Michael Lee Poffenberger

Social network friend-based recommendations are provided. Based on user consumption data provided by one or more service providers, a determination may be made as to which social network contacts a user has who may have preferences for video, music, and other media that are close to the user's. The determination of social network contacts who share a common interest and the user consumption data of those social network contacts may be utilized to infer recommendations for the user. Other functionalities may be provided, such as manual recommendations, placing emphasis on recommendations from particular friends, email notifications of content that a friend has consumed that the user has not, an option to view what a social network contact is viewing, providing a list of programs that the user and a social network contact have both seen, and providing advertisements based on consumption data, recommendations, and/or discovered areas of interest.

Recommendation system based on common interests in social networks

Non-intrusive user identification to provide personalized media content is provided. Using one or more sensors, a user or a combination of users may be identified. If the user or combination of users is known, user information may be retrieved and rules may be applied for providing content personalized to the identified person or group of people. User behavior may be monitored and changes in the audience composition may be monitored so that personalized content may be adjusted accordingly.

Automated audience recognition for targeted mixed-group content

Embodiments are configured to populate one or more virtual channels with accessible video content from disparate sources. Users are oftentimes overwhelmed with an abundance of available video content. Embodiments of the present invention may be utilized to generate an index of all video content to which a user has access from various video content providing systems and cross-match the index of video content with tagged video metadata. The cross-matched video content may be processed through various filters and mapped to one or more classifications. One or more virtual channels may be generated according to the one or more classifications and populated with the cross-matched video content. The virtual channels are customized to the user based on the availability of the content to the user and on user preference data.

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Lilburn, GA, United States of America

Ryan M S Baker