# US 12,182,215 B2 · Systems and methods for personalizing search engine recall and ranking using machine learning techniques > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,182,215 B2 - Document ID: US12182215B2 - Status: Granted - Kind: B2 - Application number: 18/222,807 - Filed: 2023-07-17 - Published: 2023-11-09 - Granted: 2024-12-31 - Earliest claimed: 2021-01-30 - Assignees: Walmart Apollo, LLC - Applicants: Walmart Apollo, LLC - Inventors: Hyun Duk Cho; Sushant Kumar; Kannan Achan; Nimesh Sinha; Aysenur Inan - Web page: https://patentgrep.com/patent/US12182215B2 ## CPC - G06F 16/9535: Search customisation based on user profiles and personalisation - G06F 16/9532: Query formulation - G06N 3/0499: Feedforward networks - G06N 3/09: Supervised learning - G06N 20/00: Machine learning - G06Q 10/067: Enterprise or organisation modelling - G06Q 30/0201: Market modelling; Market analysis; Collecting market data - G06Q 30/0625: {by formulating product or service queries, e.g. using keywords or predefined options} - G06N 3/045: Combinations of networks - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06Q 30/0631: {Recommending goods or services} - G06Q 30/0633: {Managing shopping lists, e.g. compiling or processing purchase lists (shipping orders G06Q10/083; order filling G06Q10/087)} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - 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 ## Abstract A system can include 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: receiving, at a search engine, a user search query submitted by a user; generating, using a recall personalization model, a simulated query that supplements the user search query with a feature vector reflecting personalization preferences of the user; and generating, using the search engine, search results for the user search query based, at least in part, on the simulated query that accounts for the personalization preferences of the user. Other embodiments are disclosed herein. ## Claims ### Claim 1 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: receiving, at a search engine, a user search query submitted by a user; generating, using a recall personalization model, a simulated query that supplements the user search query with a feature vector reflecting personalization preferences of the user, wherein the recall personalization model utilizes an attribute selection component associated with the feature vector; and the recall personalization model is a machine learning model configured to convert the user search query into the simulated query; and generating, using the search engine, search results for the user search query based, at least in part, on the simulated query that accounts for the personalization preferences of the user. ### Claim 2 (depends on 1) The system of claim 1, wherein: the user search query identifies an item type category without additional descriptors, wherein the user search query comprises a generic search query; and the search engine further supplements the user search query with contextual features. ### Claim 3 (depends on 1) The system of claim 1, wherein the operations further comprise: generating, using the recall personalization model, the feature vector comprising contextual features associated with the user, wherein generating the feature vector comprises: appending the feature vector with one or more attribute keys associated with a brand attribute. ### Claim 4 (depends on 1) The system of claim 1, wherein the operations further comprise: generating, using the recall personalization model, the feature vector comprising contextual features associated with the user, wherein generating the feature vector comprises: appending the feature vector with one or more attribute keys associated with a price band attribute. ### Claim 5 (depends on 1) The system of claim 1, wherein: the attribute selection component is configured to determine a number of attribute keys to be incorporated into the feature vector comprising contextual features associated with the user; and the simulated query, as converted, is configured to account for the personalization preferences of the user, wherein the contextual features associated with the user are derived from historical data associated with the user, and wherein the simulated query comprises a simulated narrowing query. ### Claim 6 (depends on 5) The system of claim 5, wherein the attribute selection component comprises a statistical model that is configured to determine the number of attribute keys to be incorporated into the feature vector associated with the user. ### Claim 7 (depends on 6) The system of claim 6, wherein the statistical model determines the number of attribute keys based, at least in part, on a number of the personalization preferences of the user. ### Claim 8 (depends on 6) The system of claim 6, wherein the statistical model determines the number of attribute keys based, at least in part, on preference scores associated with attribute values of the number of attribute keys. ### Claim 9 (depends on 1) The system of claim 1, wherein: a plurality of attribute keys are pre-computed for the user; each of the plurality of attribute keys comprises a respective attribute value for the user and a respective preference score for the user; the respective preference score indicates a respective affinity of the user for the respective attribute value; and the plurality of attribute keys are incorporated into the feature vector associated with the user. ### Claim 10 (depends on 1) The system of claim 1, wherein: the search engine is configured to communicate with a personalized ranking model; and the personalized ranking model is configured to sort a recall set of the search results based on the personalization preferences of the user. ### Claim 11 A method implemented via execution of computing instructions configured to run at one or more processors and stored at non-transitory computer-readable media, the method comprising: receiving, at a search engine, a user search query submitted by a user; generating, using a recall personalization model, a simulated query that supplements the user search query with a feature vector reflecting personalization preferences of the user, generating, using a recall personalization model, a simulated query that supplements the user search query with a feature vector reflecting personalization preferences of the user, wherein the recall personalization model utilizes an attribute selection component associated with the feature vector; and the recall personalization model is a machine learning model configured to convert the user search query into the simulated query; and generating, using the search engine, search results for the user search query based, at least in part, on the simulated query that accounts for the personalization preferences of the user. ### Claim 12 (depends on 11) The method of claim 11, wherein: the user search query identifies an item type category without additional descriptors, wherein the user search query comprises a generic search query; and the search engine further supplements the user search query with contextual features. ### Claim 13 (depends on 11) The method of claim 11 further comprising: generating, using the recall personalization model, the feature vector comprising contextual features associated with the user, wherein generating the feature vector comprises: appending the feature vector with one or more attribute keys associated with a brand attribute. ### Claim 14 (depends on 11) The method of claim 11 further comprising: generating, using the recall personalization model, the feature vector comprising contextual features associated with the user, wherein generating the feature vector comprises: appending the feature vector with one or more attribute keys associated with a price band attribute. ### Claim 15 (depends on 11) The method of claim 11, wherein: the attribute selection component is configured to determine a number of attribute keys to be incorporated into the feature vector comprising contextual features associated with the user; and the simulated query, as converted, is configured to account for the personalization preferences of the user, wherein the contextual features associated with the user are derived from historical data associated with the user, and wherein the simulated query comprises a simulated narrowing query. ### Claim 16 (depends on 15) The method of claim 15, wherein the attribute selection component comprises a statistical model that is configured to determine the number of attribute keys to be incorporated into the feature vector associated with the user. ### Claim 17 (depends on 16) The method of claim 16, wherein the statistical model determines the number of attribute keys based, at least in part, on a number of the personalization preferences of the user. ### Claim 18 (depends on 16) The method of claim 16, wherein the statistical model determines the number of attribute keys based, at least in part, on preference scores associated with attribute values of the number of attribute keys. ### Claim 19 (depends on 11) The method of claim 11, wherein: a plurality of attribute keys are pre-computed for the user; each of the plurality of attribute keys comprises a respective attribute value for the user and a respective preference score for the user; the respective preference score indicates a respective affinity of the user for the respective attribute value; and the plurality of attribute keys are incorporated into the feature vector associated with the user. ### Claim 20 (depends on 11) The method of claim 11, wherein: the search engine is configured to communicate with a personalized ranking model; and the personalized ranking model is configured to sort a recall set of the search results based on the personalization preferences of the user.