Abstract:
A system determines a name probability based on a first name dataset frequency of a first name value stored by a first name field in a personal record and a last name dataset frequency of a last name value stored by a last name field in a personal record. The system determines at least one other probability based on another dataset frequency of another value stored by another field in the personal record and an additional dataset frequency of an additional value stored by an additional field in the personal record. The system determines a combined probability based on the name probability and the at least one other probability. The system increments a count of identifiable personal records for each personal record that has a corresponding combined probability that satisfies an identifiability threshold. The system outputs a message based on the count of identifiable personal records.
Abstract:
User trust scores based on registration features is described. A system identifies registration features associated with a user registered to interact with a database. The system calculates a registration trust score for the user based on a comparison of multiple registration features associated with the user to corresponding registration features associated with previous users who are restricted from interacting with the database and/or corresponding registration features associated with previous users who are enabled to interact with the database. The system restricts the user from interacting with the database if the registration trust score is above a registration threshold.
Abstract:
New account routing to user account sets is described. A system creates multiple accounts profiles corresponding to multiple sets of accounts, based on multiple attributes associated with each account of the multiple sets of accounts. The system calculates multiple account scores for an account based on comparing multiple attributes associated with the account against the corresponding multiple accounts profiles, wherein the account is not in the multiple sets of accounts. The system identifies a highest account score of the multiple account scores. The system routes the account to a user associated with a set of accounts corresponding to the highest account score.
Abstract:
A system tokenizes raw values and corresponding standardized values into raw token sequences and corresponding standardized token sequences. A machine-learning model learns standardization from token insertions and token substitutions that modify the raw token sequences to match the corresponding standardized token sequences. The system tokenizes an input value into an input token sequence. The machine-learning model determines a probability of inserting an insertion token after an insertion markable token in the input token sequence. If the probability of inserting the insertion token satisfies a threshold, the system inserts the insertion token after the insertion markable token in the input token sequence. The machine-learning model determines a probability of substituting a substitution token for a substitutable token in the input token sequence. If the probability of substituting the substitution token satisfies another threshold, the system substitutes the substitution token for the substitutable token in the input token sequence.
Abstract:
A method of managing crowdsourced data includes storing contact information regarding a plurality of contacts within a community-updateable repository accessible by a plurality of users, receiving a plurality of discrepancy reports associated with a selected contact of the plurality of contacts, extracting fact data regarding the selected contact from the plurality of discrepancy reports, determining an action to be taken based on the fact data and a fact model applied to the fact data, and performing the action to modify the community-updateable repository.
Abstract:
A personalized recommendation model scores each object in an interaction set of objects with which a user interacted and in a ransom set of objects with which the user lacks known interaction. A system sorts each scored object based on a decreasing order of each corresponding score, and identifies a high scoring set of the sorted objects with a number (equal to the number of objects in the interaction set of objects) of highest corresponding scores. The system aggregates a corresponding order value for each object in the high scoring set that is also in the interaction set of objects (the corresponding order value for an object is based on a corresponding order for the object in the high scoring set). The system evaluates the model for the user by dividing the aggregated order value by an aggregation of a corresponding order value for each object in the high scoring set.
Abstract:
A system and method for evaluating claims from sources to update database records. A trust score is developed for each source. If a source submits a claim, the trust score for that source and the value of the claim are evaluated against prior conflicting claims. If the current claim is deemed the most likely, then it is adopted as provisional “truth”. If not, the current claim is rejected.
Abstract:
A personalized recommendation model scores each object in an interaction set of objects with which a user interacted and in a ransom set of objects with which the user lacks known interaction. A system sorts each scored object based on a decreasing order of each corresponding score, and identifies a high scoring set of the sorted objects with a number (equal to the number of objects in the interaction set of objects) of highest corresponding scores. The system aggregates a corresponding order value for each object in the high scoring set that is also in the interaction set of objects (the corresponding order value for an object is based on a corresponding order for the object in the high scoring set). The system evaluates the model for the user by dividing the aggregated order value by an aggregation of a corresponding order value for each object in the high scoring set.
Abstract:
New account recommendations for user account sets are described. A system creates an accounts profile for a set of accounts based on multiple attributes associated with each account of the set of accounts. The system calculates an account score for an account based on comparing multiple attributes associated with the account against the accounts profile, wherein the account is not in the set of accounts. The system determines whether the account score satisfies an account score threshold. The system recommends the account to a user associated with the set of accounts if the account score satisfies the account score threshold.
Abstract:
A method, apparatus, and computer-readable medium are disclosed. The method may include receiving a request to merge a first unified profile with a second unified profile, the first and second unified profile each having a set of records grouped according to a set of rules. The method may include merging the first unified profile with the second unified profile to generate a merged unified profile including a merged set of records. The method may include generating an association between the second set of records and the first set of records. The method may include receiving a request to undo the merging. The method may include ingesting, based at least in part on the request to undo the merging, the merged set of records into the automated match process by applying the set of rules and excluding the generated association.