Abstract: A fraud detection system may obtain a number of known fraudulent end-user profiles and/or otherwise undesirable end-user profiles. Using statistical analysis techniques that include clustering the end-user profiles by attributes and attribute values and/or combinations of attributes and attribute values, the fraud detection system identifies on a continuous, periodic, or aperiodic basis those attribute values and/or attribute value combinations that appear in fraudulent or otherwise undesirable end-user profiles. Using this data, the fraud detection system generates one or more queries to identify those end-user profiles having attribute values or combinations of attribute values that likely indicate a fraudulent or otherwise undesirable end-user profile.

Abstract: Information related to apparently successful matches between two entities is collected, and culled based on a later indication that the match failed. Matches between two entities may be generated based on comparative information with other entities who appear to share some characteristics or preferences. Matches may be based on actual actions, in contrast to expressed preferences. Actual actions may be taken into account in addition to expressed preferences. Generation of matches may take into account geographical and/or temporal proximity and/or likelihood of receiving a response, in addition to other attributes of an entity. Matching algorithms may be updated based on entity input. Potential matches may be presented to third party entities for evaluation.

Abstract: Information related to apparently successful end users is collected, stored, and used to generate at least one evaluation criteria to compare to at least one component of a respective end user desiring to become a successful end user. The information may be generated based on comparative information with other entities who appear to share some components with the respective end user. The components may be based on actual actions, preferences, constraints, attributes, etc. A number of components of the respective end user are compared relative to a set of defined evaluation criteria that specifies defined evaluation criteria for at least some of those components. In response to detecting at least one discrepancy, a prompt is provided to the respective end user indicative of the discrepancy.

Abstract: Information related to apparently successful matches between two entities is collected, and culled based on a later indication that the match failed. Matches between two entities may be generated based on comparative information with other entities who appear to share some characteristics or preferences. Matches may be based on actual actions, in contrast to expressed preferences. Actual actions may be taken into account in addition to expressed preferences. Generation of matches may take into account geographical and/or temporal proximity and/or likelihood of receiving a response, in addition to other attributes of an entity. Matching algorithms may be updated based on entity input. Potential matches may be presented to third party entities for evaluation.

Abstract: A fraud detection system may obtain a number of known fraudulent end-user profiles and/or otherwise undesirable end-user profiles. Using statistical analysis techniques that include clustering the end-user profiles by attributes and attribute values and/or combinations of attributes and attribute values, the fraud detection system identifies on a continuous, periodic, or aperiodic basis those attribute values and/or attribute value combinations that appear in fraudulent or otherwise undesirable end-user profiles. Using this data, the fraud detection system generates one or more queries to identify those end-user profiles having attribute values or combinations of attribute values that likely indicate a fraudulent or otherwise undesirable end-user profile.

Abstract: Information related to apparently successful end users is collected, stored, and used to generate at least one evaluation criteria to compare to at least one component of a respective end user desiring to become a successful end user. The information may be generated based on comparative information with other entities who appear to share some components with the respective end user. The components may be based on actual actions, preferences, constraints, attributes, etc. A number of components of the respective end user are compared relative to a set of defined evaluation criteria that specifies defined evaluation criteria for at least some of those components. In response to detecting at least one discrepancy, a prompt is provided to the respective end user indicative of the discrepancy.

Abstract: A fraud detection system may obtain a number of known fraudulent end-user profiles and/or otherwise undesirable end-user profiles. Using statistical analysis techniques that include clustering the end-user profiles by attributes and attribute values and/or combinations of attributes and attribute values, the fraud detection system identifies on a continuous, periodic, or aperiodic basis those attribute values and/or attribute value combinations that appear in fraudulent or otherwise undesirable end-user profiles. Using this data, the fraud detection system generates one or more queries to identify those end-user profiles having attribute values or combinations of attribute values that likely indicate a fraudulent or otherwise undesirable end-user profile.

Abstract: Information related to apparently successful matches between two entities is collected, and culled based on a later indication that the match failed. Matches between two entities may be generated based on comparative information with other entities who appear to share some characteristics or preferences. Matches may be based on actual actions, in contrast to expressed preferences. Actual actions may be taken into account in addition to expressed preferences. Generation of matches may take into account geographical and/or temporal proximity and/or likelihood of receiving a response, in addition to other attributes of an entity. Matching algorithms may be updated based on entity input. Potential matches may be presented to third party entities for evaluation.

Abstract: Information related to apparently successful matches between two entities is collected, and culled based on a later indication that the match failed. Matches between two entities may be generated based on comparative information with other entities who appear to share some characteristics or preferences. Matches may be based on actual actions, in contrast to expressed preferences. Actual actions may be taken into account in addition to expressed preferences. Generation of matches may take into account geographical and/or temporal proximity and/or likelihood of receiving a response, in addition to other attributes of an entity. Matching algorithms may be updated based on entity input. Potential matches may be presented to third party entities for evaluation.

Abstract: Information related to apparently successful matches between two entities is collected, and culled based on a later indication that the match failed. Matches between two entities may be generated based on comparative information with other entities who appear to share some characteristics or preferences. Matches may be based on actual actions, in contrast to expressed preferences. Actual actions may be taken into account in addition to expressed preferences. Generation of matches may take into account geographical and/or temporal proximity and/or likelihood of receiving a response, in addition to other attributes of an entity. Matching algorithms may be updated based on entity input. Potential matches may be presented to third party entities for evaluation.

Abstract: Information related to apparently successful matches between two entities is collected, and culled based on a later indication that the match failed. Matches between two entities may be generated based on comparative information with other entities who appear to share some characteristics or preferences. Matches may be based on actual actions, in contrast to expressed preferences. Actual actions may be taken into account in addition to expressed preferences. Generation of matches may take into account geographical and/or temporal proximity and/or likelihood of receiving a response, in addition to other attributes of an entity. Matching algorithms may be updated based on entity input. Potential matches may be presented to third party entities for evaluation.

Abstract: A fraud detection system may obtain a number of known fraudulent end-user profiles and/or otherwise undesirable end-user profiles. Using statistical analysis techniques that include clustering the end-user profiles by attributes and attribute values and/or combinations of attributes and attribute values, the fraud detection system identifies on a continuous, periodic, or aperiodic basis those attribute values and/or attribute value combinations that appear in fraudulent or otherwise undesirable end-user profiles. Using this data, the fraud detection system generates one or more queries to identify those end-user profiles having attribute values or combinations of attribute values that likely indicate a fraudulent or otherwise undesirable end-user profile.

Abstract: Information related to apparently successful end users is collected, stored, and used to generate at least one evaluation criteria to compare to at least one component of a respective end user desiring to become a successful end user. The information may be generated based on comparative information with other entities who appear to share some components with the respective end user. The components may be based on actual actions, preferences, constraints, attributes, etc. A number of components of the respective end user are compared relative to a set of defined evaluation criteria that specifies defined evaluation criteria for at least some of those components. In response to detecting at least one discrepancy, a prompt is provided to the respective end user indicative of the discrepancy.

Abstract: A fraud detection system may obtain a number of known fraudulent end-user profiles. Using statistical analysis techniques that include clustering the end-user profiles by attributes, attribute values, or both, the fraud detection system identifies on a continuous, periodic, or aperiodic basis those attribute values and/or attribute value combinations that appear in fraudulent end-user profiles. Using this data, the fraud detection system generates one or more queries to identify those end-user profiles having attribute values or combinations of attribute values that likely indicate a fraudulent or otherwise undesirable end-user profile. The fraud detection system can run these queries against incoming registrations to identify and screen fraudulent end-user profiles from entering the system and can also run these queries against stored end-user profile databases to identify and remove fraudulent or otherwise undesirable end-user profiles from the end-user database.

Abstract: Relationship building Websites collect considerable self-reported and autonomously collected attribute data on users. Attribute data may be useful for identifying users having compatible or potentially compatible interests, likes, goals, and/or aspirations that the formation of a relationship between the users is possible. At least a portion of the data collected by relationship building Websites may include inbound and outbound messaging statistics and behaviors. When used in conjunction with profile attributes, these messaging statistics and behaviors may be used as training data to generate one or more response predictive models that provide an indication of the profile attributes and messaging behaviors to which a particular user is most likely to respond. Since messaging traffic is a key indicator of relationship building Website health and vitality, it is advantageous to provide users with matches or potential matches with whom they are more likely to exchange messages.

Abstract: A front-end system collects user attribute value data and organizes the data into one or more training data sets and one or more test data sets. The front-end system provides at least some of the test data sets to an input layer of a machine learning system. Within the machine learning system, one or more predictive models are constructed. At an output layer, the predictive models provide output data that includes at least a value indicative of whether a user will upgrade service levels based at least in part on the attribute values logically associated with the respective user. A back-end system generates upgrade offers for subsequent communication to those users identified as being likely to upgrade.

Abstract: A fraud detection system may obtain a number of known fraudulent end-user profiles and/or otherwise undesirable end-user profiles. Using statistical analysis techniques that include clustering the end-user profiles by attributes and attribute values and/or combinations of attributes and attribute values, the fraud detection system identifies on a continuous, periodic, or aperiodic basis those attribute values and/or attribute value combinations that appear in fraudulent or otherwise undesirable end-user profiles. Using this data, the fraud detection system generates one or more queries to identify those end-user profiles having attribute values or combinations of attribute values that likely indicate a fraudulent or otherwise undesirable end-user profile.

Abstract: A fraudulent advertiser account detection system may be used by a Website operator to identify active advertiser accounts as at least one of either a known valid advertiser account or a known fraudulent advertiser account. Each advertiser account has a logically associated account profile. Each account profile includes a number of advertiser attributes, each attribute having one or more logically associated attribute values characterizing the respective advertiser. Using a plurality of known valid advertiser accounts and plurality of known fraudulent advertiser accounts, the system determines a number of initial probabilities and likelihoods that one or more attribute values and/or attribute value combinations present in an advertiser account profile indicate at least one of a known valid or a known fraudulent advertiser account.

Abstract: A fraud detection system may obtain a number of known fraudulent end-user profiles and/or otherwise undesirable end-user profiles. Using statistical analysis techniques that include clustering the end-user profiles by attributes and attribute values and/or combinations of attributes and attribute values, the fraud detection system identifies on a continuous, periodic, or aperiodic basis those attribute values and/or attribute value combinations that appear in fraudulent or otherwise undesirable end-user profiles. Using this data, the fraud detection system generates one or more queries to identify those end-user profiles having attribute values or combinations of attribute values that likely indicate a fraudulent or otherwise undesirable end-user profile.

Abstract: A fraud detection system may obtain a number of known fraudulent end-user profiles and/or otherwise undesirable end-user profiles. Using statistical analysis techniques that include clustering the end-user profiles by attributes and attribute values and/or combinations of attributes and attribute values, the fraud detection system identifies on a continuous, periodic, or aperiodic basis those attribute values and/or attribute value combinations that appear in fraudulent or otherwise undesirable end-user profiles. Using this data, the fraud detection system generates one or more queries to identify those end-user profiles having attribute values or combinations of attribute values that likely indicate a fraudulent or otherwise undesirable end-user profile.