CN-122019849-A - Information recommendation method, device, equipment and medium

CN122019849ACN 122019849 ACN122019849 ACN 122019849ACN-122019849-A

Abstract

The disclosure provides an information recommendation method, device, equipment and medium, wherein the information recommendation method comprises the steps of obtaining first equipment information and equipment detection characteristics of the first equipment, obtaining first habit information according to the first equipment information, wherein the first habit information comprises at least one first condition characteristic associated with the first equipment information and first operation information associated with the first condition characteristic, determining the first condition characteristic matched with the equipment detection characteristics from the at least one first condition characteristic, taking the matched first condition characteristic as target condition characteristic, and taking the first operation information associated with the target condition characteristic as recommended target operation information. The technical problem that in the prior art, recommendation of operation information is not accurate enough and the intelligent effect of equipment is affected is solved.

Inventors

TIAN SHUANG
LU YAN
LIU BOYA
GAO XIAODONG

Assignees

北京小米移动软件有限公司

Dates

Publication Date: 20260512
Application Date: 20241112

Claims (14)

1. An information recommendation method, comprising: acquiring first equipment information and equipment detection characteristics of first equipment; Acquiring first habit information according to the first equipment information, wherein the first habit information comprises at least one first condition feature associated with the first equipment information and first operation information associated with the first condition feature; determining a first condition feature matched with the device detection feature from at least one first condition feature, and taking the matched first condition feature as a target condition feature; And taking the first operation information associated with the target condition characteristic as recommended target operation information.
2. The method of claim 1, wherein the first condition features comprise behavioral features and/or scene features, the behavioral features comprising features of object behavioral dimensions, the scene features comprising features of scene dimensions; wherein the determining a first condition feature matching the device detection feature from at least one of the first condition features, and taking the matching first condition feature as a target condition feature, comprises: selecting a behavior feature from at least one of the behavior features that matches the device detection feature, wherein the device detection feature is used to describe the behavior of the object, and/or, And selecting scene features matched with the device detection features from at least one scene feature, wherein the device detection features are used for describing a scene, and wherein the matched behavior features and/or the matched scene features are used as the target condition features.
3. The method of claim 1, wherein the obtaining first habit information based on the first device information comprises: acquiring a plurality of pieces of candidate habit information, wherein the candidate habit information comprises candidate equipment information, candidate condition features associated with the candidate equipment information and candidate operation information associated with the candidate condition features; Selecting candidate device information identical to the first device information from a plurality of candidate device information, and taking candidate habit information to which the candidate device information identical to the first device information belongs as the first habit information; Wherein the candidate condition feature in the belonging candidate habit information is used as the first condition feature, and the candidate operation information in the belonging candidate habit information is used as the first operation information.
4. A method according to claim 3, wherein the candidate habit information is pre-generated based on: Acquiring a plurality of candidate behavior templates and a plurality of object behavior data, wherein the candidate behavior templates comprise triggering characteristics, template condition characteristics corresponding to the triggering characteristics and execution information corresponding to the template condition characteristics; And generating the candidate habit information according to the candidate behavior templates and the object behavior data.
5. The method of claim 4, wherein generating the candidate habit information based on the plurality of candidate behavioral templates and a plurality of object behavioral data comprises: Determining a statistical reference value corresponding to each statistical dimension according to the plurality of object behavior data and the candidate behavior templates, wherein the statistical reference value is used for quantitatively describing the matching degree of the plurality of object behavior data and the candidate behavior templates in the statistical dimension; Selecting a target behavior template corresponding to the statistical dimension from a plurality of candidate behavior templates according to the statistical reference value; and generating the candidate habit information according to the target behavior template.
6. The method of claim 5, wherein determining a statistical reference value corresponding to each statistical dimension from the plurality of object behavior data and the candidate behavior templates comprises: accumulating a preset value for a first value based on each statistical dimension under the condition that the current object behavior data is matched with the first part of content of the candidate behavior template, so as to obtain a first target value, wherein the first value is determined based on the previous object behavior data; accumulating the preset value for a second value based on each statistical dimension under the condition that the current object behavior data is matched with the second part of the content of the candidate behavior template, so as to obtain a second target value, wherein the second value is determined based on the previous object behavior data; and determining a statistical reference value corresponding to each statistical dimension according to the first target value, the second target value and the statistical information corresponding to each statistical dimension.
7. The method of claim 6, wherein determining a statistical reference value corresponding to each statistical dimension based on the first target value, the second target value, and the statistical information corresponding to each statistical dimension comprises: Determining a first description value corresponding to each statistical dimension according to the first target value and the second target value; Determining a second description value corresponding to each statistical dimension according to the second target value and the statistical information corresponding to each statistical dimension; And determining a statistical reference value corresponding to each statistical dimension according to the first description value and the second description value.
8. The method of claim 6, wherein the step of providing the first layer comprises, The first part of content comprises the triggering characteristic, the template condition characteristic and the execution information; the second part of content comprises the triggering characteristic and the template condition characteristic.
9. The method of claim 5, wherein selecting a target behavioral template corresponding to the statistical dimension from a plurality of the candidate behavioral templates based on the statistical reference value comprises: And selecting a statistical reference value larger than a threshold value from a plurality of statistical reference values, and taking a candidate behavior template corresponding to the selected statistical reference value as the target behavior template.
10. The method of claim 5, wherein generating the candidate habit information based on the target behavior template comprises: generating the candidate condition features according to the template condition features in the target behavior template and at least part of object behavior data; Determining the candidate equipment information according to the triggering characteristics in the target behavior template; Generating the candidate operation information according to the execution information and at least part of object behavior data in the target behavior template; And generating the candidate habit information according to the candidate condition characteristics, the candidate equipment information and the candidate operation information.
11. An information recommendation device, characterized by comprising: the first acquisition module is used for acquiring the first equipment information and the equipment detection characteristics of the first equipment; The second acquisition module is used for acquiring first habit information according to the first equipment information, wherein the first habit information comprises at least one first condition feature associated with the first equipment information and first operation information associated with the first condition feature; A determining module, configured to determine a first condition feature matching the device detection feature from at least one of the first condition features, and take the matching first condition feature as a target condition feature; and the recommending module is used for taking the first operation information associated with the target condition characteristic as recommended target operation information.
12. An electronic device comprising a processor and a memory communicatively coupled to the processor; The memory stores computer-executable instructions; The processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-10.
13. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-10.
14. A computer program product comprising a computer program which, when executed by a processor, implements the method of any of claims 1-10.

Description

Information recommendation method, device, equipment and medium Technical Field The disclosure relates to the technical field of internet of things, and in particular relates to an information recommendation method, device, equipment and medium. Background In an internet of things application, a user may use application functions in the internet of things through a variety of devices. The equipment is, for example, a smart television, an air conditioner, an electronic device, a smart sound box, a smart clothes hanger and the like. Different users may have different usage habits when using the device. For example, when certain conditions (such as a condition that someone moves, or a temperature is too low, etc.) are satisfied, the user a may perform a operation on the device a, while the user B may perform B operation on the device B, where the device a and the device B may be the same device or different devices, and the a operation and the B operation may be different operation information. In the related art, the recommendation of the operation information is not accurate enough, and the intelligent effect of the equipment is affected. Disclosure of Invention The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art. Therefore, the disclosure provides an information recommendation method, an information recommendation device, an electronic device, a computer readable storage medium and a computer program product, which can improve the accuracy of operation information recommendation and the intelligent effect of the device. To achieve the above object, an embodiment of a first aspect of the present disclosure provides an information recommendation method, including obtaining first device information and a device detection feature of a first device, obtaining first habit information according to the first device information, where the first habit information includes at least one first condition feature associated with the first device information and first operation information associated with the first condition feature, determining a first condition feature matching the device detection feature from the at least one first condition feature, taking the matched first condition feature as a target condition feature, and taking the first operation information associated with the target condition feature as recommended target operation information. In order to achieve the aim, a second aspect of the present disclosure provides an information recommendation device, which comprises a first acquisition module, a second acquisition module and a recommendation module, wherein the first acquisition module is used for acquiring first equipment information and equipment detection features of first equipment, the second acquisition module is used for acquiring first habit information according to the first equipment information, the first habit information comprises at least one first condition feature associated with the first equipment information and first operation information associated with the first condition feature, the determination module is used for determining the first condition feature matched with the equipment detection feature from the at least one first condition feature, the matched first condition feature is used as a target condition feature, and the recommendation module is used for taking first operation information associated with the target condition feature as recommended target operation information. To achieve the above objective, an embodiment of a third aspect of the present disclosure provides an electronic device, which includes a processor and a memory communicatively connected to the processor, where the memory stores computer-executable instructions, and the processor executes the computer-executable instructions stored in the memory, so as to implement the information recommendation method provided by the embodiment of the first aspect of the present disclosure. To achieve the above object, an embodiment of a fourth aspect of the present disclosure provides a computer-readable storage medium having stored therein computer-executable instructions, which when executed by a processor, are configured to implement the information recommendation method provided by the embodiment of the first aspect of the present disclosure. To achieve the above object, an embodiment of a fifth aspect of the present disclosure proposes a computer program product, including a computer program, which when executed by a processor implements the information recommendation method proposed by the embodiment of the first aspect of the present disclosure. To achieve the above object, an embodiment of a sixth aspect of the present disclosure proposes a processor for invoking a computer to execute instructions to perform an information recommendation method as proposed in the embodiment of the first aspect of the present disclosure. The information recommendin