Abstract: A method including obtaining search results for a search query, determining a type of the search query, calculating a degree of focus score for the search query when the type of the search query is a non-navigation type, determining one or more social annotations to provide for display along with the search results based on the degree of focus score, and providing the one or more social annotations for display with the search results.

Abstract: A system for categorizing users based on activities in a social network analyzes behavior data of online social activities for each user of a set of users. The system generates a user activity log for each user of the set of users, where the user activity log for each user is generated based on the behavior data. The system determines a set of behavioral categories based on the users' activity logs, each behavioral category of the set of behavioral categories being defined by a set of values corresponding to the one or more social activities in the behavior data. The system also associates at least one user of the set of users with one behavioral category of the set of behavioral categories based on the set of values defining the one behavioral category and user activity log for the at least one user.

Abstract: Configurations for measuring sharing behavior and audience coverage for one or more users are provided. Some configurations can include the following components: an input processor (IP), a data processor (DP) and a visualization processor (VP). The IP extracts data from user logs or processes existing raw data, and prepares the data for the data processor. In one example, the IP transforms each user record into a structured file including time-ordered data corresponding to instances of information sharing activity, target users, and associated date-time(s) for the sharing activity. The DP takes the time-ordered data from the IP and performs statistical evaluations to classify and determine the audience coverage and sharing behavior. The VP receives processed data from the DP and generates different graphical visualizations (e.g., one or more graphs) based on the statistical analysis provided by the DP.

Abstract: Systems and methods for providing real-time analysis of feature relationships are provided. In some aspects, a method includes receiving user activity data and user status data for users in the interactive network, the interactive network comprising at least two user features; generating a user dataset by associating, for each user, the user's activity data with the user's status data using a unique identification of the user and a timestamp; analyzing the user dataset using a statistical model; and providing, for display, an output of the analysis by the statistical model, the output including an indicator of a relationship between a use of one of the two user features with a use of the other of the two user features.

Abstract: A method including obtaining search results for a search query, determining a type of the search query, calculating a degree of focus score for the search query when the type of the search query is a non-navigation type, determining one or more social annotations to provide for display along with the search results based on the degree of focus score, and providing the one or more social annotations for display with the search results.

Abstract: Systems and methods for determining a group recommendation of an object, such as a restaurant, movie, or other object, from a plurality of candidate objects based on user comparisons of characteristic traits of the candidate objects are provided. In particular, keywords associated with characteristic traits are identified. The keywords are then presented to members of the group as a series of selection queries. The selection queries require a user to select or rank the keywords based on user preferences. The responses to the selection queries are used to generate a ranking score for each of the plurality of candidate objects and to select one or more of the candidate objects to recommend to the group.

Abstract: A system and method for facilitating automatic objective-based feature improvement, the method including receiving a request to identify an optimal alternative for each of one or more features of a computer-implemented entity, determining an alternative feature range for each of the one or more features, the alternative feature range defining a range of possible alternative features available with respect to the feature, selecting one or more alternative features for each feature from the alternative feature range of the feature, generating a plurality of alternative sets, each including an alternative feature for at least one of the one or more features, selecting a plurality of user groups from a pool of users and assigning each user group of the plurality of users groups to one of the plurality of alternative sets based on user characteristics of the users and historical information regarding the interaction of the user with the computer-implemented entity.