Abstract: A method including automatically determining, by a machine learning model trained based at least in part on sample items stored in a sample database, a query embedding vector for a query image of a query item. The method further can include determining, based on a respective embedding distance between the query image of the query item and a respective image of each of the sample items, neighboring items from among the sample items. The respective embedding distance can be calculated based on the query embedding vector for the query image and a respective embedding vector for the respective image of each of the sample items. Each of the sample items can include the respective image and at least one respective item label. The method also can include determining a respective normalized weight for each of the neighboring items based on the respective embedding distance between the query image and the respective image of the each of the neighboring items.

Abstract: A method including pre-screening one or more second images from a database for a search result based on one or more substring distances between one or more first binary substrings for a first image and one or more second binary substrings for the one or more second images, comprises: determining the one or more substring distances between one or more substring pairs of the one or more first binary substrings and the one or more second binary substrings of the one or more second images, and upon determining that the one or more substring distances and the one or more second binary substrings are not greater than one or more substring distance thresholds, including the one or more second images in the search result. The method further can include after pre-screening, determining one or more image distances for one or more third images of the search result.

Abstract: A result ranking system can include a computing device that is configured to obtain a plurality of items based on a query and a value corresponding to each item of the plurality of items. The computing system can also be configured to retrieve a value distribution corresponding to the query and generated based on customer interaction data. The computing system can also, for each item of the plurality of items, determine a feature value based on the value distribution and the corresponding value of the item. The computing system can be further configured to transmit the plurality of items to a customer device for display in an arrangement based on the corresponding feature value.

Abstract: A method including determining neighboring items from among sample items based on a respective embedding distance between a query image of a query item and a respective image of each of the neighboring items. The each of the sample items can comprise the respective image and at least one respective item label. The method further can include determining a respective normalized weight for each of the neighboring items based on the respective embedding distance between the query image and the respective image of the each of the neighboring items. The method also can include determining a query item label of the query item based on a weighted majority vote by the neighboring items via the respective normalized weight for the each of the neighboring items. Other embodiments are disclosed.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions executed by the one or more processors can perform functions comprising: storing a category classification hierarchy that classifies items into a plurality of browse categories; monitoring user engagement metrics for each of the items; using the user engagement metrics to compute shelf importance signals for the items; executing a ranking engine that generates a ranked item listing for a browse category based, at least in part, on the shelf importance signals for the items; and transmitting the ranked item listing for the browse category to a computing device. Other embodiments are disclosed herein.

Abstract: A system including one or more processors and one or more non-transitory computer-readable storage devices storing computing instructions that, when run on the one or more processors, cause the one or more processors to execute operations: storing image hashes corresponding to images for items offered via an electronic platform; receiving, from a computing device, a search query via a search engine included on the electronic platform; executing a relevance function; executing a visual diversity ranking function that reorders the relevant set of search results to generate a ranked set of search results, wherein the visual diversity ranking function utilizes the image hashes corresponding to the subset of the items to reorder the relevant set of search results; and transmitting the ranked set of search results to the computing device. Other embodiments are disclosed herein.

Abstract: A method including receiving a request from a user to view a page. The page is one of a search results page or a browse shelf page. The method also can include obtaining a respective brand affinity score for the user for each of one or more product types associated with the request. The method additionally can include generating a respective brand affinity signal for the user for each respective item in a baseline list of items to be displayed on the page, based on the request and the respective brand affinity score for the user for a product type of the one or more product types associated with the respective item. The method further can include generating a reranking of the items to be displayed on the page, based on a machine learning model and based on factors comprising the respective brand affinity signals for the user for the items and other rerank signals. The method additionally can include outputting the reranking of the items. Other embodiments are described.

Abstract: A method including pre-screening second images from a database for a search result based on a first image and also based on respective substring distances between first binary substrings for the first image and respective second binary substrings for each of the second images. The method further can include after the pre-screening, determining a respective image distance for each third image of third images of the search result, wherein the respective image distance is between the first image and the each third image of the third images of the search result, and after determining the respective image distance for each third image of the third images of the search result, when the respective image distance for a particular one of the third images is greater than a predetermined image distance threshold, removing the particular one of the third images from the search result before the search result is displayed on a user device requesting the search result. Other embodiments are disclosed.

Abstract: A method including determining neighboring items from among sample items based on a respective embedding distance between a query image of a query item and a respective image of each of the neighboring items. The each of the sample items can comprise the respective image and at least one respective item label. The method further can include determining a respective normalized weight for each of the neighboring items based on the respective embedding distance between the query image and the respective image of the each of the neighboring items. The method also can include determining a query item label of the query item based on a weighted majority vote by the neighboring items via the respective normalized weight for the each of the neighboring items. Other embodiments are disclosed.

Abstract: This application relates to systems and methods for facilitating online search based on offline transaction data.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: generating, using a fingerprinting network associated with a neural network architecture, feature embeddings corresponding to images; receiving, at an electronic platform, a first non-compliant image; identifying first non-compliant content in the first non-compliant image; and identifying, using multi-modal search options and codes based on the feature embeddings, one or more of the images comprising non-compliant content corresponding to the first non-compliant content in the first non-compliant image. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving images at an electronic platform; generating, using a fingerprinting network associated with a neural network architecture, feature embeddings corresponding to the images; generating, using the fingerprinting network, codes based on the feature embeddings; storing the codes in one or more databases; receiving, using a search component associated with the electronic platform, a non-compliant image comprising non-compliant content; and searching the codes in the one or more databases to identify one or more of the images that include non-compliant content similar to, or the same as, the non-compliant content included in the non-compliant image. Other embodiments are disclosed herein.

Abstract: A method including pre-screening second images from a database for a search result based on a first image and also based on respective substring distances between first binary substrings for the first image and respective second binary substrings for each of the second images. The method further can include after the pre-screening, determining a respective image distance for each third image of third images of the search result, wherein the respective image distance is between the first image and the each third image of the third images of the search result, and after determining the respective image distance for each third image of the third images of the search result, when the respective image distance for a particular one of the third images is greater than a predetermined image distance threshold, removing the particular one of the third images from the search result before the search result is displayed on a user device requesting the search result. Other embodiments are disclosed.

Abstract: A method including bit-operation and sub-code/substring filtering for image searching using a full-text search engine. The method can include determining a first binary vector comprising first binary substrings for a first image. The method also can include obtaining a respective second binary vector comprising second binary substrings for each of second images from a database. The method additionally can include determining a respective substring distance for each of the binary substring for each of the second images. The respective substring distance can be between at least a pair of a first binary substring of the first binary substrings of the first binary vector and a respective corresponding second binary substring of the second binary substrings of the respective second binary vector for each of the second images.

Abstract: This application relates to systems and methods for facilitating online search based on offline transaction data.

Abstract: A result ranking system can include a computing device that is configured to obtain a plurality of items based on a query and a value corresponding to each item of the plurality of items. The computing system can also be configured to retrieve a value distribution corresponding to the query and generated based on customer interaction data. The computing system can also, for each item of the plurality of items, determine a feature value based on the value distribution and the corresponding value of the item. The computing system can be further configured to transmit the plurality of items to a customer device for display in an arrangement based on the corresponding feature value.

Abstract: This application relates to apparatus and methods for training machine learning models, and applying trained machine learning models to generate item ranking values. In some examples, user session data for multiple users is received. Based on the user session data, user engagement data is generated characterizing engagements of corresponding items for a search query. Further, a number of examines is determined for each of the corresponding items. The user engagement data for each item is normalized based on the corresponding number of examines, and ranking data is generated based on the normalized user engagement data. The ranking data characterizes a ranking of at least a subset of the items. Further, a machine learning model is trained based on the ranking data. In some examples, the trained machine learning model is applied to a query to generate a ranking of items, and the ranking data is transmitted to a web server.

Abstract: A method including determining neighboring items from among sample items based on a respective embedding distance between a query image of a query item and a respective image of each of the neighboring items. The each of the sample items can comprise the respective image and at least one respective item label. The method further can include determining a respective normalized weight for each of the neighboring items based on the respective embedding distance between the query image and the respective image of the each of the neighboring items. The method also can include determining a query item label of the query item based on a weighted majority vote by the neighboring items via the respective normalized weight for the each of the neighboring items. Other embodiments are disclosed.

Abstract: A method including bit-operation and sub-code/substring filtering for image searching using a full-text search engine. The method can include determining a first binary vector of first binary substrings for a first image. The method also can include obtaining a respective second binary vector comprising second binary substrings for each of second images from a database. The method additionally can include determining a respective sub string distance for each of the binary substring for each of the second images. The respective substring distance can be between at least a pair of a first binary sub string of the first binary sub strings of the first binary vector and a respective corresponding second binary sub string of the second binary sub strings of the respective second binary vector for each of the second images.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving images at an electronic platform; generating, using a fingerprinting network associated with a neural network architecture, feature embeddings corresponding to the images; generating, using the fingerprinting network, codes based on the feature embeddings; storing the codes in one or more databases; receiving, using a search component associated with the electronic platform, a non-compliant image comprising non-compliant content; and searching the codes in the one or more databases to identify one or more of the images that include non-compliant content similar to, or the same as, the non-compliant content included in the non-compliant image. Other embodiments are disclosed herein.