Abstract: Content items, such as e-books, audio files, video files, etc., may be recommended to a user based on the user's presence at a geolocation or venue. Geolocation is the geospatial location of the user, while a venue is a designated area for an activity, such as a concert hall, aircraft, waiting room, etc. The recommendations may be of content items relating to the geolocation or venue, or they may be content items being accessed by others who are, or have been, in approximately the same geolocation or venue.

Abstract: Disclosed are various embodiments for using relevance indications provided by a user in performing a search. Search results corresponding to a search query are provided to a user of a client computing device. The user provides a relevance indication corresponding to relevance of at least one of the search results. A subsequent search is performed which takes the relevance indication into account.

Abstract: Disclosed are various embodiments for using relevance indications provided by a user in performing a search. Search results corresponding to a search query are provided to a user of a client computing device. The user provides a relevance indication corresponding to relevance of at least one of the search results. A subsequent search is performed which takes the relevance indication into account.

Abstract: Content items, such as e-books, audio files, video files, etc., may be recommended to a user based on the user's presence at a geolocation or venue. Geolocation is the geospatial location of the user, while a venue is a designated area for an activity, such as a concert hall, aircraft, waiting room, etc. The recommendations may be of content items relating to the geolocation or venue, or they may be content items being accessed by others who are, or have been, in approximately the same geolocation or venue.

Abstract: Disclosed are various embodiments for using relevance indications provided by a user in performing a search. Search results corresponding to a search query are provided to a user of a client computing device. The user provides a relevance indication corresponding to relevance of at least one of the search results. A subsequent search is performed which takes the relevance indication into account.

Abstract: Content items, such as e-books, audio files, video files, etc., may be recommended to a user based on the user's presence at a geolocation or venue. Geolocation is the geospatial location of the user, while a venue is a designated area for an activity, such as a concert hall, aircraft, waiting room, etc. The recommendations may be of content items relating to the geolocation or venue, or they may be content items being accessed by others who are, or have been, in approximately the same geolocation or venue.

Abstract: Campaign messages may be prioritized for a given potential recipient by generating one or more learning structures associated with the campaign message. The learning structure(s) is then evaluated relative to the potential recipient to determine potential effectiveness of the campaign messages for the potential recipient. One such learning structure is a decision tree comprising a hierarchy of attributes associated with past recipients of the campaign message. Attributes associated with the potential recipient are then applied to the decision tree to obtain a metric of potential value of the campaign message to the potential recipient.

Abstract: Content items, such as e-books, audio files, video files, etc., may be recommended to a user based on the user's presence at a geolocation or venue. Geolocation is the geospatial location of the user, while a venue is a designated area for an activity, such as a concert hall, aircraft, waiting room, etc. The recommendations may be of content items relating to the geolocation or venue, or they may be content items being accessed by others who are, or have been, in approximately the same geolocation or venue.

Abstract: The controversiality of an offering in a computer implemented system is computed based on user satisfaction feedback. A controversiality index can be provided to indicate the extent to which the offering is controversial.

Abstract: A decision tree is provided as a machine learning classifier to predict a user attribute, such as a geographical location of a user, based on a network address. More specifically, the decision tree is constructed via machine learning on a set of sample data that reflects a relationship between a network address and a user attribute of a “known user” whose profile information is recognizable. For a given network address, the decision tree can be used as a machine learning classifier to predict the most likely user attribute of a potential user. With the predicted attribute, a network service can target a group of potential users for various campaigns without recognizing the identities of the potential users.