Recommending shared products
First Claim
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1. A computer-implemented method for shared product recommendation, wherein the method comprises:
- obtaining, by an electronic device, credit information of a user, wherein the credit information of the user is derived at least in part from a usage history of the user for a first shared product, wherein the usage history of the user comprises a history of whether the user has returned the first shared product on time or whether the user has damaged the first shared product;
inputting, by the electronic device, the credit information of the user to a recommendation model for calculation, wherein the recommendation model is a machine learning model, wherein the recommendation model is trained based on credit information of a plurality of sample users, wherein the credit information of the plurality of sample users is derived from usage history of the plurality of sample users for one or more shared products;
deriving, by the electronic device and based on the recommendation model, a shared product use probability; and
recommending, by the electronic device, a second shared product to the user based on the shared product use probability.
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Abstract
An electronic device obtains credit information of a user, where the credit information of the user is derived at least in part from a usage history of the user for a shared product. The electronic device inputs the credit information of the user to a recommendation model for calculation, where the recommendation model is a machine learning model. The electronic device derives, based on the recommendation model, a shared product use probability. The electronic device recommends the shared product to the user based on the shared product use probability.
10 Citations
20 Claims
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1. A computer-implemented method for shared product recommendation, wherein the method comprises:
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obtaining, by an electronic device, credit information of a user, wherein the credit information of the user is derived at least in part from a usage history of the user for a first shared product, wherein the usage history of the user comprises a history of whether the user has returned the first shared product on time or whether the user has damaged the first shared product; inputting, by the electronic device, the credit information of the user to a recommendation model for calculation, wherein the recommendation model is a machine learning model, wherein the recommendation model is trained based on credit information of a plurality of sample users, wherein the credit information of the plurality of sample users is derived from usage history of the plurality of sample users for one or more shared products; deriving, by the electronic device and based on the recommendation model, a shared product use probability; and recommending, by the electronic device, a second shared product to the user based on the shared product use probability. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:
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obtaining, by an electronic device, credit information of a user, wherein the credit information of the user is derived at least in part from a usage history of the user for a first shared product, wherein the usage history of the user comprises a history of whether the user has returned the first shared product on time or whether the user has damaged the first shared product; inputting, by the electronic device, the credit information of the user to a recommendation model for calculation, wherein the recommendation model is a machine learning model, wherein the recommendation model is trained based on credit information of a plurality of sample users, wherein the credit information of the plurality of sample users is derived from usage history of the plurality of sample users for one or more shared products; deriving, by the electronic device and based on the recommendation model, a shared product use probability; and recommending, by the electronic device, a second shared product to the user based on the shared product use probability. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A computer-implemented system, comprising:
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one or more computers; and one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising; obtaining, by an electronic device, credit information of a user, wherein the credit information of the user is derived at least in part from a usage history of the user for a first shared product, wherein the usage history of the user comprises a history of whether the user has returned the first shared product on time or whether the user has damaged the first shared product; inputting, by the electronic device, the credit information of the user to a recommendation model for calculation, wherein the recommendation model is a machine learning model, wherein the recommendation model is trained based on credit information of a plurality of sample users, wherein the credit information of the plurality of sample users is derived from usage history of the plurality of sample users for one or more shared products; deriving, by the electronic device and based on the recommendation model, a shared product use probability; and recommending, by the electronic device, a second shared product to the user based on the shared product use probability. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification
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Current AssigneeAdvanced New Technologies Company Limited (Ant Group Co., Ltd.)
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Original AssigneeAdvanced New Technologies Company Limited (Ant Group Co., Ltd.)
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InventorsLi, Qianyuan
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Primary Examiner(s)Chang, Edward
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Application NumberUS16/806,919Publication NumberTime in Patent Office386 DaysField of Search705 35US Class CurrentCPC Class CodesG06N 20/00 Machine learningG06Q 30/0631 Item recommendationsG06Q 30/0645 Rental transactions; Leasin...G06Q 40/03 Credit; Loans; Processing t...
