Ancestry.com Operations Inc.

Surveymonkey Inc.

Peng Jiang

Azadeh Moghtaderi

Lei Wu

Gann Alexander Bierner

Ruhan Wang

Jack Reese

Yen-Yun Yu

Tyler Folkman

Ian James Stiles

Rey Furner

Eugene Greenwood

Jianlong Qi

Aaron Ling

Atanu Roy

Tsung-Nan Liu

Yingrui Yang

Christopher Miller

Surbhi Gupta

Yuchen Zhang

Riccardo Bucco

Krishna Sumanth Muppalla

Systems and methods for determining whether two tree persons in a genealogical database correspond to the same real-life individual. Embodiments include identifying two tree persons in a genealogical database and extracting a plurality of features from both tree persons to generate two vectors. Embodiments also include calculating a plurality of metrics between the two vectors to generate a metric function. Embodiments further include generating feature weights using a recursive process based on training data input by external users, and generating a score by calculating a weighted sum of the metric function being weighted by the feature weights. The generated score may then be compared to a threshold value.

Machine-learning system and method for identifying same person in genealogical databases

A method of filtering an original set of user-provided text responses across a network, including receiving, from one or more processors on one or more user computers, multiple user-provided text responses to a question, the multiple user-provided text responses forming the set, identifying text responses that are no-value text responses and removing the no-value text responses from the original set, removing text responses from the original set where the length of the response does not meet a threshold length, identifying text responses that are gibberish responses and removing gibberish responses from the original set, sending the remaining responses as a filtered set of responses to a machine learning system, the machine learning system to: perform clustering on the filtered set to identify one or more clusters of text responses that are similar to each other, identifying text responses outside the one or more clusters as noise responses, scoring the noise responses, and removing text responses having a score equal to or below a threshold score from the filtered set of responses to produce a final set, and outputting the final set of responses to an information gatherer.

System and method for determining open-ended response quality

Described herein are systems, methods, and other techniques for evaluating a classifier model. The classifier model may be provided with a set of elements to be classified into N classes. Classification results may be obtained from the classifier model. N class-specific precisions and N class-specific recalls for the N classes may be computed based on the classification results. N class-specific precision weights and N class-specific recall weights corresponding to the N classes may be obtained. A weighted f-measure may be computed by weighting the N class-specific precisions with the N class-specific precision weights and weighting the N class-specific recalls with the N class-specific recall weights.

Multiclass classification with diversified precision and recall weightings

Systems and methods for training a machine learning (ML) ranking model to rank genealogy hints are described herein. One method includes retrieving a plurality of genealogy hints for a target person, where each of the plurality of genealogy hints corresponds to a genealogy item and has a hint type of a plurality of hint types. The method includes generating, for each of the plurality of genealogy hints, a feature vector having a plurality of feature values, the feature vector being included in a plurality of feature vectors. The method includes extending each of the plurality of feature vectors by at least one additional feature value based on the number of features of one or more other hint types of the plurality of hint types. The method includes training the ML ranking model using the extended plurality of feature vectors and user-provided labels.

Genealogy item ranking and recommendation

A method for ranking genealogical records includes using a machine learning model to rank multiple searched records based on relevancy. The relevancy may be determined by identifying features included in a record and scaling each feature by a corresponding weight factor. In addition, a method for training a machine learning model and increasing the convergence speed of the training is described. To train the model, a machine learning process is used to optimize a ranking performance metric. A set of weights corresponding to multiple features are used to rank multiple past search records in a training set. An initial set of the weights are set by the expectation values of the weights. The weights are incrementally changed to optimize the ranking performance metric. The step size of the increment is determined based on the sensitivity of the ranking performance metric relative to the step size.

Customized coordinate ascent for ranking data records

An information entropy-based metric is used to represent a degree of diversity of a search result of genealogical records. In response to a query, a data query server locates a set of multiple records that match the query. The records are classified into different record types based on the records' attributes. One or more distributions of numbers of records classified into each record type are determined. Each distribution corresponds to one of the subsets the records. For each distribution, an entropy value is determined. A cumulative entropy that corresponds to a sum of the entropy values of those distributions is then determined. The cumulative entropy may serve as the entropy-based metric of the search result. The cumulative entropy may also be normalized by an ideal cumulative entropy. The normalized metric allows the diversity of different search results to be compared across different queries that may generate different numbers of records.

Diversity evaluation in genealogy search

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Sunnyvale, CA, United States of America

Peng Jiang