US 2022/0101199 A1 · POINT-OF-INTEREST RECOMMENDATION

Abstract

A training method for a point-of-interest recommendation model and a method for recommending a point of interest are provided. An implementation solution includes: obtaining training data including a plurality of point-of-interest recommendation requests; determining initialization parameters of the point-of-interest recommendation model; for a first point-of-interest recommendation request among the plurality of point-of-interest recommendation requests, determining a current return for the first point-of-interest recommendation request by utilizing the point-of-interest recommendation model, and determining, based on a second point-of-interest recommendation request initiated after the first point-of-interest recommendation request is completed, a target return for the first point-of-interest recommendation request by utilizing the point-of-interest recommendation model; and adjusting the initialization parameters of the point-of-interest recommendation model based on a difference between the current return and the target return for the first point-of-interest recommendation request, to obtain final parameters of the point-of-interest recommendation model.

Claims20

Dependent claims

A training method for a point-of-interest recommendation model, the method comprising:

obtaining, by a processor, training data that comprises a plurality of point-of-interest recommendation requests;
determining, by the processor, initialization parameters of the point-of-interest recommendation model;
for a first point-of-interest recommendation request among the plurality of point-of-interest recommendation requests, determining, by the processor, a current return for the first point-of-interest recommendation request by utilizing the point-of-interest recommendation model;
determining, by the processor, based on a second point-of-interest recommendation request among the plurality of point-of-interest recommendation requests initiated after the first point-of-interest recommendation request is completed, a target return for the first point-of-interest recommendation request by utilizing the point-of-interest recommendation model;
and
adjusting, by the processor, the initialization parameters of the point-of-interest recommendation model based on a difference between the current return and the target return for the first point-of-interest recommendation request, to obtain updated parameters of the point-of-interest recommendation model.

3 more elements

An electronic device, comprising:

at least one processor;
and
a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and when executed by the at least one processor, the instructions cause the at least one processor to:
obtain training data that comprises a plurality of point-of-interest recommendation requests;
determine initialization parameters of the point-of-interest recommendation model;
for a first point-of-interest recommendation request among the plurality of point-of-interest recommendation requests, determine a current return for the first point-of-interest recommendation request by utilizing the point-of-interest recommendation model;
determine, based on a second point-of-interest recommendation request among the plurality of point-of-interest recommendation requests initiated after the first point-of-interest recommendation request is completed, a target return for the first point-of-interest recommendation request by utilizing the point-of-interest recommendation model;
and
adjust the initialization parameters of the point-of-interest recommendation model based on a difference between the current return and the target return for the first point-of-interest recommendation request, to obtain updated parameters of the point-of-interest recommendation model.

6 more elements

A non-transitory computer-readable storage medium storing computer instructions that, when executed by at least one processor, cause the at least one processor to:

obtain training data that comprises a plurality of point-of-interest recommendation requests;
determine initialization parameters of the point-of-interest recommendation model;
for a first point-of-interest recommendation request among the plurality of point-of-interest recommendation requests, determine a current return for the first point-of-interest recommendation request by utilizing the point-of-interest recommendation model;
determine, based on a second point-of-interest recommendation request among the plurality of point-of-interest recommendation requests initiated after the first point-of-interest recommendation request is completed, a target return for the first point-of-interest recommendation request by utilizing the point-of-interest recommendation model;
and
adjust the initialization parameters of the point-of-interest recommendation model based on a difference between the current return and the target return for the first point-of-interest recommendation request, to obtain updated parameters of the point-of-interest recommendation model

3 more elements

Dependencies are inferred from the claim text.

Point-of-interest recommendation

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Point-of-interest recommendation

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Abstract

Claims20