# US 2021/0241343 A1 · Personalized item recommendations through large-scale deep-embedding architecture > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241343 A1 - Document ID: US20210241343A1 - Status: Published application - Kind: A1 - Application number: 16/777,555 - Filed: 2020-01-30 - Published: 2021-08-05 - Earliest claimed: 2020-01-30 - Assignees: Walmart Apollo, LLC - Applicants: Walmart Apollo, LLC - Inventors: Yokila Arora; Aditya Mantha; Shubham Gupta; Praveenkumar Kanumala; Stephen Dean Guo; Kannan Achan - Web page: https://patentgrep.com/patent/US20210241343A1 ## CPC - G06Q 30/0631: {Recommending goods or services} - G06N 20/00: Machine learning - G06Q 30/0603: {Catalogue creation or management} - G06Q 30/0633: {Managing shopping lists, e.g. compiling or processing purchase lists (shipping orders G06Q10/083; order filling G06Q10/087)} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method including receiving a basket including basket items selected by a user from an item catalog. The method also 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. The method further can include generating a respective list of complementary items for the respective anchor item for the each of the categories based on a respective score for each of the complementary items generated using two sets of trained item embeddings for items in the item catalog and using trained user embeddings for the user. The two sets of trained item embeddings and the trained user embeddings can be trained using a triple embeddings model with triplets. The triplets each can include a respective first user of 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 additionally can include building a list of personalized recommended items for the user based on the respective lists of the complementary items for the categories. The method further can include sending instructions to display, to the user on a user interface of a user device, at least a portion of the list of personalized recommended items. Other embodiments are disclosed. ## Claims ### Claim 1 A system comprising: 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: receiving a basket comprising basket items selected by a user from an item catalog; grouping the basket items of the basket into categories based on a respective item category of each of the basket items; randomly sampling a respective anchor item from each of the categories; generating a respective list of complementary items for the respective anchor item for the each of the categories based on a respective score for each of the complementary items generated using two sets of trained item embeddings for items in the item catalog and using trained user embeddings for the user, wherein the two sets of trained item embeddings and the trained user embeddings were trained using a triple embeddings model with triplets, and wherein the triplets each comprise a respective first user of 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; building a list of personalized recommended items for the user based on the respective lists of the complementary items for the categories; and sending instructions to display, to the user on a user interface of a user device, at least a portion of the list of personalized recommended items. ### Claim 2 (depends on 1) The system of claim 1, wherein building the list of personalized recommended items for the user further comprises: filtering the respective list of the complementary items for the each of the categories based on complementary subcategories; performing a weighted sampling of the respective list of the complementary items for the each of the categories to generate a sampled sub-list of the list of the complementary items for the each of the categories; and merging the sampled sub-lists for the categories to generate a unified list. ### Claim 3 (depends on 2) The system of claim 2, wherein filtering the respective list of the complementary items for the each of the categories based on the complementary subcategories further comprises: receiving lift scores for subcategories of the complementary items; and applying the lift scores for the complementary items to remove, from the respective list of the complementary items, popular co-bought items in the subcategories that are unrelated to a subcategory of the respective anchor item for the each of the categories. ### Claim 4 (depends on 2) The system of claim 2, wherein performing the weighted sampling of the respective list of the complementary items for the each of the categories further comprises: for the each of the categories, sampling a respective quantity of items from the respective list of the complementary items proportional to a respective quantity of the basket items in the each of the categories with respect to a total quantity of the basket items in the basket. ### Claim 5 (depends on 2) The system of claim 2, wherein building the list of personalized recommended items for the user further comprises: filtering out items from the unified list having subcategories that are identical to subcategories of the basket items. ### Claim 6 (depends on 5) The system of claim 5, wherein building the list of personalized recommended items for the user further comprises: sorting each item that remains in the unified list by the respective score of the item. ### Claim 7 (depends on 6) The system of claim 6, wherein building the list of personalized recommended items for the user further comprises: performing a category diversification across the unified list. ### Claim 8 (depends on 1) The system of claim 1, wherein a vector dimension for the trained user embeddings for the user and for each item in each of the two sets of trained item embeddings is 128. ### Claim 9 (depends on 1) The system of claim 1, wherein the computing instructions are further configured to perform, before receiving the basket: training the triple embeddings model with the triplets using an adaptive moment estimation optimizer to optimize a co-occurrence log-likelihood of each of the triplets. ### Claim 10 (depends on 1) The system of claim 1, wherein the portion of the list of personalized recommended items is displayed to the user on a checkout page for the basket, wherein the checkout page appears on the user interface of the user device. ### Claim 11 A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the method comprising: receiving a basket comprising basket items selected by a user from an item catalog; grouping the basket items of the basket into categories based on a respective item category of each of the basket items; randomly sampling a respective anchor item from each of the categories; generating a respective list of complementary items for the respective anchor item for the each of the categories based on a respective score for each of the complementary items generated using two sets of trained item embeddings for items in the item catalog and using trained user embeddings for the user, wherein the two sets of trained item embeddings and the trained user embeddings were trained using a triple embeddings model with triplets, and wherein the triplets each comprise a respective first user of 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; building a list of personalized recommended items for the user based on the respective lists of the complementary items for the categories; and sending instructions to display, to the user on a user interface of a user device, at least a portion of the list of personalized recommended items. ### Claim 12 (depends on 11) The method of claim 11, wherein building the list of personalized recommended items for the user further comprises: filtering the respective list of the complementary items for the each of the categories based on complementary subcategories; performing a weighted sampling of the respective list of the complementary items for the each of the categories to generate a sampled sub-list of the list of the complementary items for the each of the categories; and merging the sampled sub-lists for the categories to generate a unified list. ### Claim 13 (depends on 12) The method of claim 12, wherein filtering the respective list of the complementary items for the each of the categories based on the complementary subcategories further comprises: receiving lift scores for subcategories of the complementary items; and applying the lift scores for the complementary items to remove, from the respective list of the complementary items, popular co-bought items in the subcategories that are unrelated to a subcategory of the respective anchor item for the each of the categories. ### Claim 14 (depends on 12) The method of claim 12, wherein performing the weighted sampling of the respective list of the complementary items for the each of the categories further comprises: for the each of the categories, sampling a respective quantity of items from the respective list of the complementary items proportional to a respective quantity of the basket items in the each of the categories with respect to a total quantity of the basket items in the basket. ### Claim 15 (depends on 12) The method of claim 12, wherein building the list of personalized recommended items for the user further comprises: filtering out items from the unified list having subcategories that are identical to subcategories of the basket items. ### Claim 16 (depends on 15) The method of claim 15, wherein building the list of personalized recommended items for the user further comprises: sorting each item that remained in the unified list by the respective score of the item. ### Claim 17 (depends on 16) The method of claim 16, wherein building the list of personalized recommended items for the user further comprises: performing a category diversification across the unified list. ### Claim 18 (depends on 11) The method of claim 11, wherein a vector dimension for the trained user embeddings for the user and for each item in each of the two sets of trained item embeddings is 128. ### Claim 19 (depends on 11) The method of claim 11, further comprising, before receiving the basket: training the triple embeddings model with the triplets using an adaptive moment estimation optimizer to optimize a co-occurrence log-likelihood of each of the triplets. ### Claim 20 (depends on 11) The method of claim 11, wherein the portion of the list of personalized recommended items is displayed to the user on a checkout page for the basket, wherein the checkout page appears on the user interface of the user device.