Abstract: Computerized data processing and electronic file management methods of organizing and indexing electronic records in an electronic database for categorizing new products that are being added to an existing database of product offerings and computerized digital data processing methods of transferring digital information between a plurality of computers and employing computer instructions to categorize new products that are being added to an existing database of product offerings. Multiple classification models classify a description of a particular product and the classifications are compared, and if found to be equivalent, are added to the existing database of product offerings. If the classifications from the models are not equivalent, then the description is sent to multiple people for classification and the classifications from the people are compared, and if found to be equivalent, are added to the existing database of product offerings.

Abstract: A method of computing a featured set of product groups for a query on an inventory of products. Each of the products can have one or more attribute-value pairs. Each of the one or more attribute-value pairs can have an attribute. The method can include determining a result group of the products matching the query. The method also can include determining relevance scores for the product groups. The method further can include determining a featured attribute and the featured set of the product groups for the featured attribute. The method also can include, after receiving the query from a user, transmitting for display at least one page of a website, where the at least one page has an option to view each of the product groups of the featured set of the product groups that has been selected. Other embodiments of related systems and methods are also disclosed.

Abstract: A system, method, and non-transitory computer-readable storage media includes a computer system having a server configured to generate labeled data on products to produce pairwise preferences, to combine the pairwise preferences, editorial ratings of the products, and inferred consumer relevances of the products to produce an ideal ranking for the products, to learn rerank function parameters based on the ideal ranking for the products, to output product rerank function parameters based on the learned rerank function parameters, to receive a product search query from a user device of a user, to get products that match the product search query, to rank the products based on the product rerank function parameters, and to output the ranked products as search results to the user device of the user.

Abstract: A system and method includes a computer system having a server configured to receive a product search query, the server being configured to receive a product search query, receive data on products, compute product insights on the products, evaluate the product insights, marking those product insights that need validation, output the product insights that need validation, rank the products trading off relevance with testing promising products, and output a mix of relevant and promising products as search results to a user device of a user.

Abstract: Systems and methods are disclosed herein for classifying records, such as product records, using a machine learning algorithm. After training a classification model according to a machine learning algorithm using an initial training set, records are classified and high confidence classifications identified. Remaining classifications are submitted to a crowdsourcing forum that validates or invalidates the classifications or marks them as to unclear to evaluate. Invalidated classifications are automatically analyzed to identify one or both of classification values and categories having a high proportion of invalidated classifications. Requests are transmitted to analysts to generate training data that is added to the training set. The process of classifying records and obtaining crowdsourced validation thereof may then repeat.

Abstract: Systems and methods are disclosed herein for performing classification of documents or performing other tasks based on rules. The rules may include context rules that define a mapping that relates a value and context in a document to an attribute to which the value corresponds. Products are selected for labeling with attributes by identifying patterns, e.g. values and contexts that are not covered by a current rule set. Those products having a highest score are selected for labeling in a crowd sourcing forum, where the score is based on the number of non-covered patterns and a frequency of occurrence of the non-covered patterns in a document corpus. Proposed rules are generated for frequently occurring patterns and submitted to analysts for one or both of completion and validation. Proposed rules may include a proposed attribute for a frequently occurring value and corresponding context.

Abstract: Systems and methods are disclosed herein for generating a normalized record from an import record, the normalized record having attribute-value pairs corresponding to a native schema. In import records, a plurality of attribute-value are identified each having an attribute label not found in a native schema. One or more attribute labels in the native schema having as possible values one or more values corresponding to the values of the plurality of attribute-value pairs are also identified. The computer system generates one or more normalization rules relating one or more attribute labels of the plurality of attribute-value pairs to at least a portion of the one or more attribute labels in the native schema. Normalization rules may be validated by crowdsourcing. Normalization rules may be applied by identifying implicated rules by classifying the import record and identifying rules applicable to the classification.

Abstract: Computerized data processing and electronic file management methods of organizing and indexing electronic records in an electronic database for categorizing new products that are being added to an existing database of product offerings and computerized digital data processing methods of transferring digital information between a plurality of computers and employing computer instructions to categorize new products that are being added to an existing database of product offerings. Multiple classification models classify a description of a particular product and the classifications are compared, and if found to be equivalent, are added to the existing database of product offerings. If the classifications from the models are not equivalent, then the description is sent to multiple people for classification and the classifications from the people are compared, and if found to be equivalent, are added to the existing database of product offerings.

Abstract: Systems and methods are disclosed herein for obtaining a structured listing of attributes and corresponding values based on an unstructured document, such as a product description in a product record. Putative values are identified in the document and corresponding candidate attributes are identified in a taxonomy. Attribute-value pairs are then evaluated with respect to a plurality of rules. Attribute-value pairs and outputs of the one or more rules are evaluated using a machine-learning algorithm, such as a decision tree, in order to determine which attribute-value pairs to retain. Retained attribute-value pairs are stored and used to respond to search queries and facilitate comparison of products. Attributes selected may also be used to update a product template.

Abstract: A method of computing a featured set of product groups for a query on an inventory of products. Each of the products can have one or more attribute-value pairs. Each of the one or more attribute-value pairs can have an attribute. The method can include determining a result group of the products matching the query. The method also can include determining relevance scores for the product groups. The method further can include determining a featured attribute and the featured set of the product groups for the featured attribute. The method also can include, after receiving the query from a user, transmitting for display at least one page of a website, where the at least one page has an option to view each of the product groups of the featured set of the product groups that has been selected. Other embodiments of related systems and methods are also disclosed.

Abstract: Systems and methods are disclosed herein for classifying records, such as product records, using a machine learning algorithm. After training a classification model according to a machine learning algorithm using an initial training set, records are classified and high confidence classifications identified. Remaining classifications are submitted to a crowdsourcing forum that validates or invalidates the classifications or marks them as to unclear to evaluate. Invalidated classifications are automatically analyzed to identify one or both of classification values and categories having a high proportion of invalidated classifications. Requests are transmitted to analysts to generate training data that is added to the training set. The process of classifying records and obtaining crowdsourced validation thereof may then repeat. High confidence classifications may be identified using an accuracy model trained to relate an accuracy percentage to a confidence score output by the classification model.

Abstract: The present disclosure extends to methods, systems, and computer program products for automatically determining key words within item information with product types, and classifying new items within product types within a merchant's database.

Abstract: Systems and methods are disclosed herein for generating a normalized record from an import record, the normalized record having attribute-value pairs corresponding to a native schema. In import records, a plurality of attribute-value are identified each having an attribute label not found in a native schema. One or more attribute labels in the native schema having as possible values one or more values corresponding to the values of the plurality of attribute-value pairs are also identified. The computer system generates one or more normalization rules relating one or more attribute labels of the plurality of attribute-value pairs to at least a portion of the one or more attribute labels in the native schema. Normalization rules may be validated by crowdsourcing. Normalization rules may be applied by identifying implicated rules by classifying the import record and identifying rules applicable to the classification.

Abstract: Systems and methods are disclosed herein for obtaining a structured listing of attributes and corresponding values based on an unstructured document, such as a product description in a product record. Putative values are identified in the document and corresponding candidate attributes are identified in a taxonomy. Attribute-value pairs are then evaluated with respect to a plurality of rules. Attribute-value pairs and outputs of the one or more rules are evaluated using a machine-learning algorithm, such as a decision tree, in order to determine which attribute-value pairs to retain. Retained attribute-value pairs are stored and used to respond to search queries and facilitate comparison of products. Attributes selected may also be used to update a product template.

Abstract: Systems and methods are disclosed herein for classifying records, such as product records, using a machine learning algorithm. After training a classification model according to a machine learning algorithm using an initial training set, records are classified and high confidence classifications identified. Remaining classifications are submitted to a crowdsourcing forum that validates or invalidates the classifications or marks them as to unclear to evaluate. Invalidated classifications are automatically analyzed to identify one or both of classification values and categories having a high proportion of invalidated classifications. Requests are transmitted to analysts to generate training data that is added to the training set. The process of classifying records and obtaining crowdsourced validation thereof may then repeat. High confidence classifications may be identified using an accuracy model trained to relate an accuracy percentage to a confidence score output by the classification model.

Abstract: Systems and methods are disclosed herein for classifying records, such as product records, using a machine learning algorithm. After training a classification model according to a machine learning algorithm using an initial training set, records are classified and high confidence classifications identified. Remaining classifications are submitted to a crowdsourcing forum that validates or invalidates the classifications or marks them as to unclear to evaluate. Invalidated classifications are automatically analyzed to identify one or both of classification values and categories having a high proportion of invalidated classifications. Requests are transmitted to analysts to generate training data that is added to the training set. The process of classifying records and obtaining crowdsourced validation thereof may then repeat.

Abstract: The present disclosure extends to methods, systems, and computer program products for updating a merchant database with new items automatically or with minimal human involvement. In operation, methods and systems disclosed use a pipeline of classification models to quantify new product information and create an accurate classification for the new product item.

Abstract: The present disclosure extends to methods, systems, and computer program products for updating a database with new products by classifying the new products within a hierarchy, and then using the hierarchy to improve the classification by including other product types within the classification for the new products.

Abstract: The present disclosure extends to methods, systems, and computer program products for automatically determining key words within item information with product types, and classifying new items within product types within a merchant's database.

Abstract: The present disclosure extends to methods, systems, and computer program products for updating a merchant database with new product items and placing the new product items within a hierarchy of existing merchant product offerings. In operation, the new product is represented by a title and description that can be semantically classified using a plurality of classification models and reviewed by users for accuracy.