Abstract: A method including building a recommendation triggering model. The method can include receiving, via a user device of a user through a network, an add-to-cart command associated with an anchor item in a session by the user. The method further can include determining, in real-time after receiving the add-to-cart command, a recommendation for one or more complementary items based at least in part on: (a) the anchor item; and (b) a user profile of the user. The method also can include determining, in real-time after determining the recommendation, a recommendation confidence for the recommendation based at least in part on one or more of: (a) the user profile; (b) the anchor item; (c) the one or more complementary items; or (d) one or more feedbacks from the user associated with one or more prior recommendations in the session.

Abstract: A method including training two sets of item embeddings for items in an item catalog and a set of user embeddings for users, using a triple embeddings model, with triplets. The triplets each can include a respective first user of the users, a respective first item from the item catalog, and a respective second item from the item catalog, in which the respective first user selected the respective first item and the respective second item in a respective same basket. The method also can include generating an approximate nearest neighbor index for the two sets of item embeddings. The method additionally can include receiving a basket including basket items selected by a user from the item catalog. The method further can include grouping the basket items of the basket into categories based on a respective item category of each of the basket items. The method additionally can include randomly sampling a respective anchor item from each of the categories.

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 acts of receiving a user identifier, receiving an item identifier, determining user item quantity information related to quantities of the item previously selected by the user, determining a respective household size for each user, and determining aggregate household item quantity information related to quantities of the item previously selected by an aggregate of users of the same household size. If a first threshold level of the quantity of transactions is met, a recommended quantity is based on the user item quantity information, and if not, the recommended quantity is based on the aggregate household item quantity information. The user interface of the electronic device is updated to notify the user of the recommended quantity. Other embodiments are disclosed herein.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform generating a training dataset comprising training quadruplets; generating a respective text feature vector for each of the four respective items for the each of the training quadruplets using a vector encoder; transforming the respective text feature vector for each of the four respective items; training the shared trainable parameters of the feature representation transformation model; receiving, from a user device a selection of an anchor item from the item catalog; determining, for the anchor item, one or more similar items or one or more complementary items; and sending instructions to display the one or more of the one or more similar items or the one or more of the one or more complementary items on the user device. Other embodiments are disclosed.

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 an input identifying an anchor item; determining, using a quadruplet network associated with a neural network architecture, one or more item categories corresponding to complementary items associated with the anchor item; generating, using a ranking network associated with the neural network architecture, scores for the complementary items included in the one or more item categories; generating, using the ranking network associated with the neural network architecture, first ranking results for the complementary items based, at least in part, on the scores; and selecting one or more of the complementary items to be displayed based, at least in part, on the first ranking results. Other embodiments are disclosed herein.

Abstract: This application relates to apparatus and methods for generating preference profiles that may be used to rank search results. In some examples, a computing device obtains browsing session data and determines items that were engaged, such as items that were viewed or clicked. The computing device obtains item property data, such as product descriptions, for the items, and applies a dependency parser to the item property data to identify portions that include certain words, such as nouns or adjectives, which are then identified as attributes. The computing device generates attribute data identifying portions of the item property data as item attributes. In some examples, the computing device applies one or more machine learning algorithms to the session data and/or search query to identify item attributes. The computing device may generate a profile that includes the item attributes, and may rank search results based on the attribute data, among other uses.

Abstract: This application relates to apparatus and methods for generating preference profiles that may be used to rank search results. In some examples, a computing device obtains browsing session data and determines items that were engaged, such as items that were viewed or clicked. The computing device obtains item property data, such as product descriptions, for the items, and applies a dependency parser to the item property data to identify portions that include certain words, such as nouns or adjectives, which are then identified as attributes. The computing device generates attribute data identifying portions of the item property data as item attributes. In some examples, the computing device applies one or more machine learning algorithms to the session data and/or search query to identify item attributes. The computing device may generate a profile that includes the item attributes, and may rank search results based on the attribute data, among other uses.

Abstract: A system and method of generating user personalized search results is disclosed. A search query including one or more words is received and a set of relevance-based search results is generated in response to the search query. One or more query attributes are generated for the search query. Historic data for a user associated with the search query is received and a set of personalized search results is generated from the set of relevance-based search results based on the query attributes and the historic data for the user. The historic data includes one or more items associated with the user.

Abstract: A system and method of generating complimentary items from a catalog of items is disclosed. A plurality of item attributes for each of a plurality of items is received and a multimodal embedding representative of the plurality of attributes is generated for each of the plurality of items. The multimodal embedding is configured to predict at least a subset of the received plurality of item attributes for each of the plurality of items. A triplet network including a node representative of each of the plurality of items is generated. The triplet network is generated based on the multimodal embedding for each of the plurality of items. A plurality of complimentary items is generated from the plurality of items. The plurality of complimentary items are selected by the triplet network based on an anchor item selection received from a user.

Abstract: This application relates to apparatus and methods for automatically determining and providing item reviews to users. In some examples, a computing device obtains review data identifying one or more reviews for each of a plurality of items. The computing device determines keywords for each of the items based on parsing the review data corresponding to each of items. The computing device may obtain data identifying engagement of items for a user during a browsing session, such as items a user has clicked on. The computing device may also obtain data identifying previous purchase transactions, or previous review postings, for the user. The computing device then determines, based on the obtained data, which keywords may be of interest the user. In some examples, the keywords are used to identify reviews of an item for the user. In some examples, summaries of the reviews are generated and displayed to the user.

Abstract: This application relates to apparatus and methods for automatically determining and providing item reviews to users. In some examples, a computing device obtains review data identifying one or more reviews for each of a plurality of items. The computing device determines keywords for each of the items based on parsing the review data corresponding to each of items. The computing device may obtain data identifying engagement of items for a user during a browsing session, such as items a user has clicked on. The computing device may also obtain data identifying previous purchase transactions, or previous review postings, for the user. The computing device then determines, based on the obtained data, which keywords may be of interest the user. In some examples, the keywords are used to identify reviews of an item for the user. In some examples, summaries of the reviews are generated and displayed to the user.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital advertisements to targeted users. In some examples, a computing device receives campaign data identifying items to advertise on a website, and generates campaign user data identifying a user that has engaged all of the items on the website. The computing device may then determine a portion of the users based on a relationship between each user and the campaign user data, and may determine user-item values for each of the items for each user of the portion of users, where each user-item value identifies a relational value between the corresponding user and item. The computing device may then identify one or more of the items to advertise to each user of the portion of users based on the user-item values, and may transmit to a web server an indication of the items to advertise for each user.

Abstract: This application relates to apparatus and methods for automatically determining and providing digital advertisements to targeted users. In some examples, a computing device receives campaign data identifying items to advertise on a website, and generates campaign user data identifying a user that has engaged all of the items on the website. The computing device may then determine a portion of the users based on a relationship between each user and the campaign user data, and may determine user-item values for each of the items for each user of the portion of users, where each user-item value identifies a relational value between the corresponding user and item. The computing device may then identify one or more of the items to advertise to each user of the portion of users based on the user-item values, and may transmit to a web server an indication of the items to advertise for each user.

Abstract: A system, method and computer product for allowing a processing device to generate search engine results to locate personalized product substitutions, filter the search results based on product characteristics and customer and retailer preferences, and provide personalized substitution recommendations to online grocery shoppers.

Abstract: Many embodiments can include a system. In some embodiments, the system can comprise one or more processors and one or more non-transitory storage devices storing computing instructions are disclosed.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of accessing an online catalog for an online retailer comprising a plurality of digital images of a plurality of items for sale by the online retailer, training a two-branch a Siamese convolutional neural network (CNN) model to determine a similarity between two digital images of the plurality of digital images, receiving one or more digital images of a new item for the online catalog, determining, using the two-branch Siamese CNN model and the one or more digital images of the new item, a similar item of the plurality of items to which the new item is most similar, and coordinating a display of the new item on a webpage based on a ranking of the similar item.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of accessing an online catalog for an online retailer comprising a plurality of digital images of a plurality of items for sale by the online retailer, training a two-branch a Siamese convolutional neural network (CNN) model to determine a similarity between two digital images of the plurality of digital images, receiving one or more digital images of a new item for the online catalog, determining, using the two-branch Siamese CNN model and the one or more digital images of the new item, a similar item of the plurality of items to which the new item is most similar, and coordinating a display of the new item on a webpage based on a ranking of the similar item.

Abstract: A system, method and computer product for allowing a processing device to generate search engine results to locate personalized product substitutions, filter the search results based on product characteristics and customer and retailer preferences, and provide personalized substitution recommendations to online grocery shoppers.