Abstract: A social network content recommendation system allows users to identify friends, recommend content to friends, and receive awards for influencing friends. Internet pages can be used to display rankings of recommended programs, user profiles and their awards, and dynamic chats relating to specific programs, such as television shows.

Abstract: Generating synthesized speech audio data on behalf of a given user in a conversation. The synthesized speech audio data includes synthesized speech that incorporates textual segment(s). The textual segment(s) can include recognized text that results from processing spoken input, of the given user, using a speech recognition model and/or can include a selection of a rendered suggestion that conveys the textual segment(s). Some implementations dynamically determine one or more prosodic properties for use in speech synthesis of the textual segment, and generate the synthesized speech with the one or more determined prosodic properties. The prosodic properties can be determined based on the textual segment(s) used in speech synthesis, textual segment(s) corresponding to recent spoken input of additional participant(s), attribute(s) of relationship(s) between the given user and additional participant(s) in the conversation, and/or feature(s) of a current location for the conversation.

Abstract: A social network content recommendation system allows users to identify friends, recommend content to friends, and receive awards for influencing friends. Internet pages can be used to display rankings of recommended programs, user profiles and their awards, and dynamic chats relating to specific programs, such as television shows.

Abstract: A social network content recommendation system allows users to identify friends, recommend content to friends, and receive awards for influencing friends. Internet pages can be used to display rankings of recommended programs, user profiles and their awards, and dynamic chats relating to specific programs, such as television shows.

Abstract: A content recommendation system is disclosed that allows users to identify friends, recommend content to friends, and receive awards for influencing friends. Interfaces can be used to display rankings of recommended programs, user profiles and their awards, and dynamic chats relating to specific content.

Abstract: A system allows a user to select multimedia content items from sources that include, but are not limited to, any of: Internet, network, or local. Selected multimedia content items may be stored in user specific caches residing in at least one cloud based storage device. Multimedia content items may be transcoded while or after being retrieved from a source and then stored in a user specific cache. Multimedia content items may be selected by a user from the user's specific cache and streamed to a user device.

Abstract: A content recommendation system is disclosed that allows users to identify friends, recommend content to friends, and receive awards for influencing friends. Interfaces can be used to display rankings of recommended programs, user profiles and their awards, and dynamic chats relating to specific content.

Abstract: Various methods are disclosed for monitoring user browsing activities, and for using such information to provide item recommendations to users. In one embodiment, a monitoring component of a merchant's Web site maintains a record of products viewed by each user during a current browsing session—preferably based on visits to product detail pages. A recommendations component uses the resulting history of viewed products to identify additional products to recommend, preferably using a pre-existing table that maps products to related products. In one embodiment, this table is generated by periodically analyzing user browsing histories to identify correlations between purchases, viewing events, and/or other actions performed with respect to particular products. The recommended items may be displayed together with an option to individually deselect the recently viewed items on which the recommendations are based.

Abstract: Various methods are disclosed for monitoring user browsing activities that indicate user interests in particular products, or other items, represented in an electronic catalog, and for using such information to identify items that are related to one another. In one embodiment, relationships between items within an electronic catalog are determined by identifying items that are frequently viewed by users within the same browsing session (e.g., items A and B are related because a significant portion of those who viewed A also viewed B). The resulting item relatedness data may be stored in a table that maps items to sets of related items. The table may be used to provide personalized item recommendations to users, and/or to supplement item detail pages of the electronic catalog with lists of related items. In one embodiment, the table is used to provide session-specific item recommendations to users.