Abstract: A method of providing results from a search engine comprises generating an initial set based on a query received from a user; generating an attention value based on the query and on the initial set; applying the attention value to the initial set; and presenting a set of results in response to the query, wherein the results are generated according to the application of the attention value to the initial set.

Abstract: A method for ranking documents in search results includes defining a first training data set, the first training data set including, for each of a plurality of user queries, information respective of a document selected by a user from results responsive to the query and information respective of one or more documents within an observation window after the selected document in the results, and defining a second training data set, the second training data set including, for each of the plurality of user queries, information respective of the selected document. The method further includes training a first machine learning model with the first training data set, training a second machine learning model with the second training data set, and ranking documents of a further search result set according to the output of the first machine learning model and the output of the second machine learning model.

Abstract: A method includes determining a first taxonomy of an anchor product. The first taxonomy includes a plurality of levels for classifying products organized from a highest taxonomy level to a lowest level. The method further includes determining a second taxonomy closest to the first taxonomy. The second taxonomy is associated with a group of products, the first taxonomy and the second taxonomy have at least a common highest taxonomy level, and the determination is made at least in part based on co-purchase data indicating that the anchor product and at least one product in the group of products are purchased together more often than products associated with other taxonomies are purchased with the anchor product. The method further includes determining a most similar product to the anchor product from the group of products of the second taxonomy and associating the anchor product and the most similar product in a collection.

Abstract: Methods and apparatus are disclosed for interactive job recommendation and application submission tools of employment website entities. A system includes processor(s). The processor(s) are configured to retrieve, during a session on an employment app operating on a computing device, a first employment opportunity for the candidate based upon candidate information and employment preferences of the candidate. The processor(s) are configured to detect, during the session, an employment command received from the candidate via the computing device in response to presenting the first employment opportunity to the candidate via the computing device.

Abstract: A method includes determining a first taxonomy of an anchor product. The first taxonomy includes a plurality of levels for classifying products organized from a highest taxonomy level to a lowest taxonomy level. The method further includes determining a second taxonomy closest to the first taxonomy. The second taxonomy is associated with a group of products, the first taxonomy and the second taxonomy have at least a common highest taxonomy level, and the determination is made at least in part based on co-purchase data indicating that the anchor product and at least one product in the group of products are purchased together more often than products associated with other taxonomies are purchased with the anchor product. The method further includes determining a most similar product to the anchor product from the group of products of the second taxonomy and associating the anchor product and the most similar product with one another in a product collection.

Abstract: A method for ranking documents in search results includes defining a first training data set, the first training data set including, for each of a plurality of user queries, information respective of a document selected by a user from results responsive to the query and information respective of one or more documents within an observation window after the selected document in the results, and defining a second training data set, the second training data set including, for each of the plurality of user queries, information respective of the selected document. The method further includes training a first machine learning model with the first training data set, training a second machine learning model with the second training data set, and ranking documents of a further search result set according to the output of the first machine learning model and the output of the second machine learning model.

Abstract: Methods and apparatus are disclosed for interactive job recommendation and application submission tools of employment website entities. An example system includes an app manager to review, via a processor during a session of a candidate, a resume of the candidate that includes candidate information. The app manager is to receive a first employment preference of the candidate upon prompting via a display and is to detect an employment command upon presenting a first employment opportunity.

Abstract: A method of determining item features and organizing an interface according to the features includes determining a plurality of features of a plurality of items, the plurality of items accessible through an electronic interface, applying a plurality of machine learning models to the determined features, wherein each of the machine learning models calculates a correlation of each feature to characteristic determinative of user selection on the electronic interface, calculating respective Shapley values of each correlation determined by each of the plurality of machine learning models, determining one or more of the item features that are most strongly correlated with the determinative characteristic according to the respective Shapley values, and causing the electronic interface to be organized according to the determined most strongly correlated item features.

Abstract: A method for predicting a next user selection in an electronic user interface, such as a website, includes training a machine learning model according to a training data set, the training data set including a plurality of token sets, each token set representative of a respective document accessible through the interface, each token set including a plurality of words, each word describing a characteristic of the document, to create a trained model. The method further includes receiving, from a user, a sequence of selections of documents, inputting the sequence of selections to the trained model, and outputting to the user, in response to the sequence of selections, a predicted next document selection according to an output of the trained model.

Abstract: A method of providing results from a search engine comprises generating an initial set based on a query received from a user; generating an attention value based on the query and on the initial set; applying the attention value to the initial set; and presenting a set of results in response to the query, wherein the results are generated according to the application of the attention value to the initial set.

Abstract: A method for ranking documents in search results includes defining a first training data set, the first training data set including, for each of a plurality of user queries, information respective of a document selected by a user from results responsive to the query and information respective of one or more documents within an observation window after the selected document in the results, and defining a second training data set, the second training data set including, for each of the plurality of user queries, information respective of the selected document. The method further includes training a first machine learning model with the first training data set, training a second machine learning model with the second training data set, and ranking documents of a further search result set according to the output of the first machine learning model and the output of the second machine learning model.

Abstract: A computer-implemented method includes extracting, by one or more processors of one or more computing devices, a product family name from each of a plurality of unstructured product titles associated with a plurality of products. The method further includes determining, by the one or more processors, a degree of similarity between model numbers of the plurality of products. The method further includes determining, by the one or more processors, that at least two of the plurality of products are variants of one another by determining that the at least two of the plurality of products have a same extracted product family name and determining that the degree of similarity between the model numbers of the plurality of products is above a predetermined threshold.

Abstract: Method and systems are disclosed for distribution of candidate information to a recruiter list. An example apparatus includes a candidate manager to retrieve, via a processor, a first keyword provided by a candidate during a session of the candidate on an employment website. The apparatus includes a recruiter-candidate comparator to query, in real time during the session, a database including bids for candidates. The recruiter-candidate comparator is to retrieve, in response to identifying that at least one of the bids for candidates includes the first keyword, one or more of the bids for candidates from the database that include the first keyword and determine a recruiter list to include at least one recruiter of the one or more bids for candidates that include the first keyword. The apparatus includes a recruiter manager to distribute, in real time during the session, candidate information of the candidate to the recruiter list.

Abstract: Systems and methods for providing suggestions of complementary products responsive to an anchor product are disclosed. The method includes receiving a selection of an anchor product. A similarity score between text embeddings of the anchor product and text embeddings of a plurality of products in a product database is calculated. A similarity score between an image feature of the anchor product and an image feature of the plurality of products in the product database is calculated. A weighted score between the two similarity scores as calculated for the anchor product and the plurality of products in the product database is calculated. At least one of the products from the product database having a highest weighted score is selected and returned responsive to the selection of the anchor product.

Abstract: A method includes determining a first taxonomy of an anchor product. The first taxonomy includes a plurality of levels for classifying products organized from a highest taxonomy level to a lowest taxonomy level. The method further includes determining a second taxonomy closest to the first taxonomy. The second taxonomy is associated with a group of products, the first taxonomy and the second taxonomy have at least a common highest taxonomy level, and the determination is made at least in part based on co-purchase data indicating that the anchor product and at least one product in the group of products are purchased together more often than products associated with other taxonomies are purchased with the anchor product. The method further includes determining a most similar product to the anchor product from the group of products of the second taxonomy and associating the anchor product and the most similar product with one another in a product collection.

Abstract: Methods and apparatus are disclosed for interactive job recommendation and application submission tools of employment website entities. An example system includes an app manager to review, via a processor during a session of a candidate, a resume of the candidate that includes candidate information. The app manager is to receive a first employment preference of the candidate upon prompting via a display and is to detect an employment command upon presenting a first employment opportunity.

Abstract: Method and systems are disclosed for distribution of candidate information to a recruiter list. An example apparatus includes a candidate manager to retrieve, via a processor, a first keyword provided by a candidate during a session of the candidate on an employment website. The apparatus includes a recruiter-candidate comparator to query, in real time during the session, a database including bids for candidates. The recruiter-candidate comparator is to retrieve, in response to identifying that at least one of the bids for candidates includes the first keyword, one or more of the bids for candidates from the database that include the first keyword and determine a recruiter list to include at least one recruiter of the one or more bids for candidates that include the first keyword. The apparatus includes a recruiter manager to distribute, in real time during the session, candidate information of the candidate to the recruiter list.