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.

Abstract: Websites operated by relationship building service providers collect information from users for use in identifying candidate matches for a user. At least a portion of the information may include freeform text entered by the user to identify the user's interests. The freeform text entered by users may be preprocessed and a user interest group model formed using a generative Natural Language Process that is applied to determine user interest groups from the freeform text. Such user interest groups identify related words based on the frequency of their occurrence and relationship within the freeform text. Individual user interest group membership vectors for each user included in a number of users may be determined using the user interest group model. At least one compatibility aspect between users may then be assessed based on the similarity or distance between the user interest group membership vectors associated with each user.

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 matching or pairing system and method for matching first and second entities having a greater likelihood of forming a successful pairing includes a trained machine learning system to provide heuristic values useful in determining a compatibility score for the pairing. During training of the machine learning system, a training example selection device can provide attribute values logically associated with entities engaged in historically successful pairings and a number of hypothetically successful pairings. The hypothetically successful pairings may be based at least in part on historically successful pairings where at least one attribute value logically associated with at least one entity in the pairing is varied, adjusted, or subjected to a loosened constraint. During run-time operation a screening device can screen unsuccessful pairings and forward potentially successful pairings that meet a threshold value to the neural network. The system can then determine a compatibility score for the pairing.

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.