Abstract: A system and method for follow up management comprising determining if a user has a repository record, extracting information from the repository record associated with the user, and acting on information stored in the repository record. The method may be practiced on a system for managing online interaction comprising a business rules engine a follow up repository, and a follow up engine.

Abstract: A system and method for follow up management comprising determining if a user has a repository record, extracting information from the repository record associated with the user, and acting on information stored in the repository record. The method may be practiced on a system for managing online interaction comprising a business rules engine a follow up repository, and a follow up engine.

Abstract: A ranked set of users may be calculated from an expertise categorization for each user and a person's trust in the users for specific categories. The ranked set of users may be used for presenting search results, recommendations, social marketing, or other uses. A person's reputation may be determined through various online activities. A person's trust in another person may be related to their proximity and activity in one or more social networks.

Abstract: A system and method for follow up management comprising determining if a user has a repository record, extracting information from the repository record associated with the user, and acting on information stored in the repository record. The method may be practiced on a system for managing online interaction comprising a business rules engine a follow up repository, and a follow up engine.

Abstract: There is provided a computer-implemented method of identifying anomalous entities in a dataset, comprising: selecting a subset of training entities from entities of at least one dataset; determining dummy tuplets of entities in the subset by applying a permutation function on real tuplets, wherein the real tuplets represent original and normal data of the at least one dataset, wherein the dummy tuplets represent anomalous data based on artificially created data not found in the original and normal at least one dataset, each one of the real tuplets and dummy tuplets comprises at least two of the training entities; analyzing the dummy tuplets and the real tuplets to identify at least one predefined characteristic relation that statistically differentiates between the real tuplets and the dummy tuplets according to a distinguishing requirement; and outputting the identified at least one predefined characteristic relation to identify a normal entity and/or an anomalous entity.

Abstract: There is provided a computer-implemented method of identifying anomalous entities in a dataset, comprising: selecting a subset of training entities from entities of at least one dataset; determining dummy tuplets of entities in the subset by applying a permutation function on real tuplets, wherein the real tuplets represent original and normal data of the at least one dataset, wherein the dummy tuplets represent anomalous data based on artificially created data not found in the original and normal at least one dataset, each one of the real tuplets and dummy tuplets comprises at least two of the training entities; analyzing the dummy tuplets and the real tuplets to identify at least one predefined characteristic relation that statistically differentiates between the real tuplets and the dummy tuplets according to a distinguishing requirement; and outputting the identified at least one predefined characteristic relation to identify a normal entity and/or an anomalous entity.

Abstract: A system and method for follow up management comprising determining if a user has a repository record, extracting information from the repository record associated with the user, and acting on information stored in the repository record. The method may be practiced on a system for managing online interaction comprising a business rules engine a follow up repository, and a follow up engine.

Abstract: A system and method for dynamically generating a web page is disclosed. The web page includes a frame having content cells. Content for the content cells is provided based in part on business rules. A business rules engine processes, in real-time, an online interaction and determines appropriate content for the content cells. The business rules engine outputs a pointer to the content which is used to populate the content cell.

Abstract: There is provided a method for combining datasets, comprising: receiving a primary training dataset; receiving unclassified secondary dataset(s) comprising secondary data instances including secondary fields; identifying, for a first set of values of primary field(s) of training dataset, a second set of secondary fields of the secondary datasets according to the first set of values matched to corresponding values in respective secondary field(s) of secondary dataset(s) according to a matching requirement; linking each respective matched values to other secondary fields of the respective matched secondary field; generating a set of classification features based at least on the linked secondary fields; selecting a subset of pivotal classification features according to a correlation requirement; identifying a subset of pivotal secondary fields based on the secondary fields associated with each pivotal classification feature; and documenting the selected subset of pivotal features for use in an automated machine

Abstract: There is provided a method for generating features for use in an automated machine learning process, comprising: receiving a first training dataset comprising unclassified raw data instances each including a set of objects of arbitrary types; applying a function to each data instance to calculate a set of first results; generating a set of classification features each including the function for application to a newly received data instance to calculate a second result, and a condition defined by a respective member of the set of first results applied to the second result; applying each classification feature to each instance of an unclassified second training dataset to generate a set of extracted features; selecting a subset of pivotal classification features from the set of classification features according to a correlation requirement between classification variable(s) and each respective member of the set of extracted features; and documenting the subset of pivotal features.

Abstract: A system and method for follow up management comprising determining if a user has a repository record, extracting information from the repository record associated with the user, and acting on information stored in the repository record. The method may be practiced on a system for managing online interaction comprising a business rules engine a follow up repository, and a follow up engine.

Abstract: A system and method for dynamically generating a web page is disclosed. The web page includes a frame having content cells. Content for the content cells is provided based in part on business rules. A business rules engine processes, in real-time, an online interaction and determines appropriate content for the content cells. The business rules engine outputs a pointer to the content which is used to populate the content cell.

Abstract: A system and method for follow up management comprising determining if a user has a repository record, extracting information from the repository record associated with the user, and acting on information stored in the repository record. The method may be practiced on a system for managing online interaction comprising a business rules engine a follow up repository, and a follow up engine.

Abstract: Techniques for determining a set of keywords associated with a document are provided. A document is received that may be classified into a taxonomy that includes a plurality of categories. A categorization ranking is determined for each category for the received document. A set of categories of the taxonomy having highest categorization rankings is determined for the received document. Documents representing the set of categories having highest categorization rankings are combined together into a cumulative representative text that includes a plurality of terms. A cumulative term corpus importance score is determined for each term in the cumulative representative text. The cumulative term corpus importance score for a particular term indicates an importance of the particular term in a context of the cumulative representative text. A set of terms of the cumulative representative text having highest cumulative term corpus importance scores is selected to be keywords for the received document.

Abstract: A method of identifying an element. The method comprises setting a training set comprising a plurality of data units, selecting a function group of building block functions adapted for processing said plurality of data units, combining members of said function group to create a stream of a plurality of combination functions each complied from at least two members of said function group, applying each member of said stream on each of said plurality of data units to create a set of results, analyzing said set of results to identify a correlation between at least one member of said stream and a target variable for an analysis of said plurality of data units, and outputting said at least one member or an indication thereof.

Abstract: A method of identifying an element. The method comprises setting a training set comprising a plurality of data units, selecting a function group of building block functions adapted for processing said plurality of data units, combining members of said function group to create a stream of a plurality of combination functions each complied from at least two members of said function group, applying each member of said stream on each of said plurality of data units to create a set of results, analyzing said set of results to identify a correlation between at least one member of said stream and a target variable for an analysis of said plurality of data units, and outputting said at least one member or an indication thereof.

Abstract: Topical affinity badges in information retrieval is described, for example, where graphical elements are presented together with ranked search results from a search engine, to indicate a degree of association between an author of a document and a topic. In various embodiments, a measure of an author's affinity to a topic is predicted using a machine learning system that takes as input author features derived from documents of the author. In various examples topical affinity scores or labels are used to generate badges and/or to index documents at an index of a search engine. For example, badges may be presented with ranked search results, in pop up displays or in other ways. In examples author features relating to any of recency, frequency and presentation of an author's documents are generated, where presentation features may relate to biographical information such as experience, qualifications, and previous documents.

Abstract: A system and method for follow up management comprising determining if a user has a repository record, extracting information from the repository record associated with the user, and acting on information stored in the repository record. The method may be practiced on a system for managing online interaction comprising a business rules engine a follow up repository, and a follow up engine.

Abstract: A system and method for follow up management comprising determining if a user has an unexpired repository record, extracting information from the repository record associated with the user; and acting on instructions stored in the repository record. The method is practiced on a system for managing online interaction comprising a business rules engine a follow up repository, and a follow up engine.

Abstract: A recommendation system may use a network of relationships between many different entities to find search results and establish a relevance value for the search results. The relevance value may be calculated by analyzing trust and similarity components of each relationship between the search user and the entity providing the search results. The entities may be, for example, persons associated within express or implied social networks, or corporations or other organizations with a historical or other reputation. The relationships may be created through many different contact mechanisms and may be unidirectional, asymmetric bidirectional, or symmetric bidirectional relationships. The relationships may be different based on topic or other factors.