Abstract: A method includes generating, as executed by a processor on a computer, a plurality of topic-specific user knowledge models for each user of a plurality of users, each topic-specific user knowledge model representing a level of knowledge possessed by a respective user on a single topic from a set of globally defined topics shared among the plurality of users, generating a plurality of topic-specific expert knowledge models, each topic-specific expert knowledge model representing an aggregate level of knowledge possessed by a plurality of expert users on a single topic from a set of globally defined topics shared among the plurality of users, comparing the topic-specific user knowledge model of the first user with the topic-specific expert knowledge model for a respective topic to determine a distance between a user knowledge level and an aggregate expert knowledge level for the topic.

Abstract: Visualizing social media conflict is provided. Active users in a set of human users authoring a number of textual messages regarding a particular topic more than a threshold number of textual messages are selected. Keywords are selected that occur more than a threshold number of times within the textual messages regarding the particular topic. A sentiment score is computed for each of the keywords occurring more than the threshold number of times within the textual messages using a keyword co-occurrence graph. A sentiment of each of the active users is determined based on the computed sentiment score of each of the selected keywords that are authored by a particular active user. Two distinct groups from the active users are selected based on at least one of a relationship between the two distinct groups and a determined degree of conflict between the two distinct groups with regard to the particular topic.

Abstract: Improved visual context management techniques are disclosed. By way of example, a technique for updating an existing visual display to incorporate new information comprises obtaining new information requested by a subsequent user query, and dynamically deriving one or more visual transformations for updating at least a portion of the existing display to incorporate the new information, wherein the transformation derivation is modeled as an optimization operation which attempts to balance visual context management constraints and to achieve a desired presentation of intended information.

Abstract: A system including a global topic model building module that builds a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, a user model building module that builds a plurality of user models, each user model being built based on an activity of a respective user, an expertise model building module that builds an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, a processor to compare a user model of the first user with the expertise model for the topic; and an expertise assessment and learning recommendation module that recommends an activity associated with the set of data to the first user based on the comparison.

Abstract: A method including generating a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, the global topic model including a topic representation for a topic, generating a plurality of user models, each user model being generated based on the activity of a respective user, generating an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, comparing a user model of the first user with the expertise model for the topic, the comparing being performed by a processor of a computer system, and recommending an activity associated with the set of data to the first user based on the comparison.

Abstract: Methods and apparatus are provided for ranking information content based on performance data of prior users of the information content. Information content is ranked by receiving a search request specifying search criteria; identifying a preliminary document list by searching a corpus using the search criteria; identifying content elements in documents in the preliminary document list; assigning a value to at least one document in the preliminary document list based on a score for each content element in the at least one document, wherein the score is based on a performance measure of one or more related users that accessed one or more documents having a given content element; and providing search results based on the assigned values. The score can be assigned, for example, based on a regression model between the performance measure and one or more of the content elements and/or one or more social network metrics.

Abstract: Visualizing social media conflict is provided. Textual messages by a set of human users connected via a network regarding a particular topic are collected. Active users in the set of human users authoring a number of textual messages regarding the particular topic more than a threshold number of textual messages are selected. Keywords are selected that occur more than a threshold number of times within the textual messages regarding the particular topic. A sentiment score is computed for each of the keywords occurring more than the threshold number of times within the textual messages using a keyword co-occurrence graph. A sentiment of each of the active users is determined based on the computed sentiment score of each of the selected keywords that are authored by a particular active user.

Abstract: A method of automating incoming message prioritization. The method including training a global classifier of a computer system using training data. Dynamically training a user-specific classifier of the computer system based on a plurality of feedback instances. Inferring a topic of the incoming message received by the computer system based on a topic-based user model. Computing a plurality of contextual features of the incoming message. Determining a priority classification strategy for assigning a priority level to the incoming message based on the computed contextual features of the incoming message and a weighted combination of the global classifier and the user specific classifier. Classifying the incoming message based on the priority classification strategy.

Abstract: A system and methods are provided for identifying conversations in tweet streams. A method includes grouping tweet messages in the tweet streams into tweet groups, responsive to hashtags therefor and time intervals in which the tweet message were sent. The method further includes splitting the tweet groups into subgroups responsive to secondary hashtags and a time separation between the tweets messages. The method also includes clustering any of the subgroups into a respective same conversation responsive to word occurrences, word frequencies, and account holders. The method additionally includes merging any of the subgroups having different hashtags into the respective same conversation responsive to overlapping glossary and account lists. Each of the tweet groups and each of the subgroups correspond to a respective different one of the conversations when unable to be split, clustered, or merged.

Abstract: A system and methods are provided for identifying conversations in tweet streams. A method includes grouping tweet messages in the tweet streams into tweet groups, responsive to hashtags therefor and time intervals in which the tweet message were sent. The method further includes splitting the tweet groups into subgroups responsive to secondary hashtags and a time separation between the tweets messages. The method also includes clustering any of the subgroups into a respective same conversation responsive to word occurrences, word frequencies, and account holders. The method additionally includes merging any of the subgroups having different hashtags into the respective same conversation responsive to overlapping glossary and account lists. Each of the tweet groups and each of the subgroups correspond to a respective different one of the conversations when unable to be split, clustered, or merged.

Abstract: An apparatus for automating a prioritization of an incoming message, including a batch learning module that generates a global classifier based on training data that is input to the batch learning module. A feedback learning module that generates a user-specific classifier based on a plurality of feedback instances. A feature extraction module that receives the incoming message and a topic-based user model, infers a topic of the incoming message based on the topic-based user model, and computes a plurality of contextual features of the incoming message. A classification module that dynamically determines a priority classification strategy for assigning a priority level to the incoming message based on the plurality of contextual features of the incoming message and a weighted combination of the global classifier and the user-specific classifier, and classifies the incoming message based on the priority classification strategy.

Abstract: Visualizing social media conflict is provided. Textual messages by a set of human users connected via a network regarding a particular topic are collected. Active users in the set of human users authoring a number of textual messages regarding the particular topic more than a threshold number of textual messages are selected. Keywords are selected that occur more than a threshold number of times within the textual messages regarding the particular topic. A sentiment score is computed for each of the keywords occurring more than the threshold number of times within the textual messages using a keyword co-occurrence graph. A sentiment of each of the active users is determined based on the computed sentiment score of each of the selected keywords that are authored by a particular active user.

Abstract: A method, system and computer program product for finding a diversified ranking list for a given query. In one embodiment, a multitude of date items responsive to the query are identified, a marginal score is established for each data item; and a set, or ranking list, of the data items is formed based on these scores. This ranking list is formed by forming an initial set, and one or more data items are added to the ranking list based on the marginal scores of the data items. In one embodiment, each of the data items has a measured relevance and a measured diversity value, and the marginal scores for the data items are based on the measured relevance and the measured diversity values of the data items.

Abstract: A method of modeling a user includes performing a role-based classification of tangible interactions involving the user performed via a computer system of an organization, creating a collection of role-specific interactions, creating, from the collection of role-specific interactions, a plurality of role-specific models of the user, wherein the plurality of role-specific models constitute a user model of the user, outputting one or more of the role-specific models to a different user model associated with a different user, and consolidating the output one or more of the role-specific models with a second plurality of role-specific models of the different user model within the different user model.

Abstract: Methods and systems are provided for automatic visual preview of non-visual data. A visual preview of non-visual data is generated by obtaining the non-visual data; obtaining metadata describing one or more semantic data types in the obtained non-visual data; selecting one or more visual metaphors for the obtained non-visual data based on the metadata; and generating the visual preview of the non-visual data using the one or more selected visual metaphors. As used herein, non-visual data does not have an established automatic method for generating a preview of the non-visual data. A user can optionally interact with the visual preview.

Abstract: A dataset including at least one temporal event sequence is collected. A one-class sequence classifier f(x) that obtains a decision boundary is statistically learned. At least one new temporal event sequence is evaluated, wherein the at least one new temporal event sequence is outside of the dataset. It is determined whether the at least one new temporal event sequence is one of a normal sequence or an abnormal sequence based on the evaluation. Numerous additional aspects are disclosed.

Abstract: Methods and apparatus are provided for inferring user interests from both direct and indirect social neighbors. User interests are inferred from social neighbors by exploiting the correlation among multiple attributes of a user, in addition to the social correlation of an attribute among a group of users. Attributes of a user are inferred by obtaining an inferred set of attributes comprised of one or more attributes of social neighbors of the user. Thereafter, the inferred set is modified using a user attribute correlation model describing a probability that the attributes in the inferred set co-occur on the user and one or more of the social neighbors. An inference quality of the obtained attributes can optionally be obtained based on social network properties of the social neighbors. Interactions with the user and/or the social neighbors can be employed to solicit feedback to improve the one or more inferred attributes.

Abstract: A wheel assembly for an infant carrier apparatus can include a shock absorber, and at least a wheel pivotally coupled with the shock absorber. The shock absorber is formed integrally in a single body comprising a cushion structure capable of elastic deformation, and a mount structure having a tubular shape adapted to assemble with a support frame of an infant carrier apparatus. Examples of the infant carrier apparatus can include an infant stroller apparatus.

Abstract: A system including a global topic model building module that builds a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, a user model building module that builds a plurality of user models, each user model being built based on an activity of a respective user, an expertise model building module that builds an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, a processor to compare a user model of the first user with the expertise model for the topic; and an expertise assessment and learning recommendation module that recommends an activity associated with the set of data to the first user based on the comparison.

Abstract: A method including generating a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, the global topic model including a topic representation for a topic, generating a plurality of user models, each user model being generated based on the activity of a respective user, generating an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, comparing a user model of the first user with the expertise model for the topic, the comparing being performed by a processor of a computer system, and recommending an activity associated with the set of data to the first user based on the comparison.