Method and device for recommending option configuration, refrigerator equipment and computer readable storage medium

Abstract

The invention relates to the technical field of intelligent equipment, and discloses an option configuration recommendation method, which comprises the following steps: under the condition that target equipment performs option configuration, obtaining an acceptance rate of an option to be configured; determining a target recommendation mode of the to-be-configured option according to the acceptance rate of the to-be-configured option; and executing the target recommendation mode, and determining a recommendation configuration result of the to-be-configured option on the target equipment. When option configuration is carried out on target equipment, the acceptance rate of each option to be configured is firstly obtained, so that the success probability of recommendation of the option to be configured can be pre-judged. And then, determining a target recommendation mode of each option to be configured, so that recommendation resources can be reasonably allocated in combination with the acceptance rate. Therefore, the recommendation efficiency and the recommendation accuracy of the option configuration result can be considered, the configuration experience of the user can be guaranteed, and the recommendation cost of the equipment can be reduced. The invention further discloses an option configuration recommendation device, refrigerator equipment and a computer readable storage medium.

Description

Technical Field

[0001] The present application relates to the technical field of smart devices, for example, to a method and device for recommending option configurations, a refrigerator device, and a computer-readable storage medium. Background Art

[0002] At present, the functions of refrigerator screens are becoming more and more abundant, such as the addition of many voice-related functions. Correspondingly, there are more and more places where users need to configure options. And with the iteration of software, the operations that users need to perform are becoming more complicated and numerous, which in turn reduces user satisfaction. Based on this, the relevant technology proposes a function configuration recommendation method, including: obtaining the user's operation information during the use of an application software system; judging whether a preset feature appears in the operation information; if so, based on the correspondence between the preset feature and the function configuration item of the application software system, recommending the function configuration item corresponding to the feature to the user, wherein the function configuration item is configured to realize the corresponding function provided by the product or service of the application software system.

[0003] In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in the related art:

[0004] Although the related technology can recommend appropriate function configuration items to users according to their operations, it cannot effectively recommend the configuration results of the function configuration items, and therefore cannot well guarantee the user's configuration experience. In addition, invalid recommendations will waste resources and increase equipment costs.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0007] The embodiments of the present disclosure provide a method and apparatus for recommending option configurations, a refrigerator device, and a computer-readable storage medium, which can take into account both the recommendation efficiency and the recommendation accuracy of option configuration results, thereby ensuring the user's configuration experience and reducing the recommendation cost of the device.

[0008] In some embodiments, the method includes: when the target device is performing option configuration, obtaining the acceptance rate of the option to be configured; determining the target recommendation method for the option to be configured based on the acceptance rate of the option to be configured; and executing the target recommendation method to determine the recommended configuration result of the option to be configured on the target device.

[0009] In some embodiments, the device includes: a processor and a memory storing program instructions, and the processor is configured to execute the above-mentioned method for recommending option configuration when running the program instructions.

[0010] In some embodiments, the refrigerator device includes: a refrigerator body, provided with a screen-end system; the above-mentioned recommended device for optional configuration is installed on the refrigerator body and electrically connected to the screen-end system.

[0011] In some embodiments, the computer-readable storage medium stores program instructions, and when the program instructions are executed, the computer is used to execute the above-mentioned method for recommending option configuration.

[0012] The method, device, refrigerator device, and computer-readable storage medium for recommending option configurations provided by the embodiments of the present disclosure can achieve the following technical effects:

[0013] When the target device is configuring options, the embodiment of the present disclosure first obtains the acceptance rate of each option to be configured, so as to predict the success probability of recommending the option to be configured. Then the embodiment of the present disclosure determines the target recommendation method for each option to be configured accordingly, so as to reasonably allocate recommendation resources in combination with the acceptance rate. By executing each adaptive target recommendation method, the embodiment of the present disclosure can reduce the complexity of the recommendation algorithm for certain options to be configured, so that the corresponding recommended configuration results can be determined more quickly, which is conducive to improving the recommendation efficiency. For other options to be configured, the optimized recommendation algorithm is used to more accurately determine the corresponding recommended configuration results, which is conducive to improving the recommendation accuracy. Therefore, the embodiment of the present disclosure can take into account both the recommendation efficiency and the recommendation accuracy of the option configuration results, which can not only ensure the user's configuration experience, but also reduce the recommendation cost of the device.

[0014] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0016] Figure 1is a schematic diagram of a recommended method for option configuration provided by an embodiment of the present disclosure;

[0017] Figure 2 is a schematic diagram of a recommended method for another option configuration provided by an embodiment of the present disclosure;

[0018] Figure 3 is a schematic diagram of a recommended method for another option configuration provided by an embodiment of the present disclosure;

[0019] Figure 4 is a schematic diagram of a recommended method for another option configuration provided by an embodiment of the present disclosure;

[0020] Figure 5 is a schematic diagram of a recommended method for another option configuration provided by an embodiment of the present disclosure;

[0021] Figure 6 is a schematic diagram of a recommended method for another option configuration provided by an embodiment of the present disclosure;

[0022] Figure 7 is a schematic diagram of a recommended device for option configuration provided by an embodiment of the present disclosure;

[0023] Figure 8 It is a schematic diagram of a refrigerator device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0025] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0026] Unless otherwise stated, the term "plurality" means two or more.

[0027] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0028] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0029] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0030] In the embodiments of the present disclosure, the target device refers to an intelligent device formed by introducing microprocessors, sensor technology, and network communication technology into home appliances, which has the characteristics of intelligent control, intelligent perception, intelligent application, and intelligent recommendation. The operation process of the target device often relies on the application and processing of modern technologies such as the Internet of Things, the Internet, and electronic chips. For example, the target device can be connected to a server to enable the server to analyze the data of the target device and make personalized recommendations. Optionally, in the embodiments of the present disclosure, the target device may include a refrigerator device.

[0031] At present, the functions of refrigerator screens are becoming more and more abundant, such as the addition of many voice-related functions. Correspondingly, there are more and more places where users need to configure options. And with the iteration of software, the operations that users need to perform are becoming more complicated and numerous, which in turn reduces user satisfaction. Based on this, the relevant technology proposes a function configuration recommendation method, including: obtaining the user's operation information during the use of an application software system; judging whether a preset feature appears in the operation information; if so, based on the correspondence between the preset feature and the function configuration item of the application software system, recommending the function configuration item corresponding to the feature to the user, wherein the function configuration item is configured to realize the corresponding function provided by the product or service of the application software system.

[0032] Although the related technology can recommend appropriate function configuration items to users according to their operations, it cannot effectively recommend the configuration results of the function configuration items, and therefore cannot well guarantee the user's configuration experience. In addition, invalid recommendations will waste resources and increase equipment costs.

[0033] Combination Figure 1 As shown, the embodiment of the present disclosure provides a method for recommending option configuration, including:

[0034] S101: When a target device is performing option configuration, the processor obtains an acceptance rate of the option to be configured.

[0035] S102: The processor determines a target recommendation method for the option to be configured according to the acceptance rate of the option to be configured.

[0036] S103: The processor executes a target recommendation method to determine a recommended configuration result of the to-be-configured option on the target device.

[0037] By adopting the method for recommending option configuration provided by the embodiment of the present disclosure, when the target device is configuring the options, the acceptance rate of each option to be configured is first obtained, so that the success probability of recommending the option to be configured can be predicted. Then the embodiment of the present disclosure determines the target recommendation method for each option to be configured accordingly, so that the recommendation resources can be reasonably allocated in combination with the acceptance rate. By executing each adaptive target recommendation method, the embodiment of the present disclosure can reduce the complexity of the recommendation algorithm for certain options to be configured, so that the corresponding recommended configuration results can be determined more quickly, which is conducive to improving the recommendation efficiency. For other options to be configured, the optimized recommendation algorithm is used to more accurately determine the corresponding recommended configuration results, which is conducive to improving the recommendation accuracy. Therefore, the embodiment of the present disclosure can take into account the recommendation efficiency and recommendation accuracy of the option configuration results, which can not only ensure the user's configuration experience, but also reduce the recommendation cost of the device.

[0038] Optionally, the recommended method of the option configuration may be executed in the target device, or in a server communicating with the target device. In the embodiment of the present disclosure, the solution is described with the processor in the target device as the execution subject.

[0039] Optionally, when the target device is performing option configuration, the processor obtains the acceptance rate of the options to be configured, including: when the target device is performing option configuration, the processor obtains the historical configuration times of the target device; when the historical configuration times of the target device is greater than 0, the processor obtains the acceptance rate of the options to be configured. In this way, when the target device is performing option configuration, the embodiment of the present disclosure first obtains the historical configuration times of the target device to determine whether the currently performed option configuration operation is the first configuration operation of the target device. Only when the historical configuration times of the target device is greater than 0, that is, the currently performed option configuration operation is not the first configuration operation of the target device, and the user has previously performed corresponding configuration operations on some options, the embodiment of the present disclosure obtains the acceptance rate of the options to be configured and predicts the success probability of recommending the options to be configured. It is then possible to determine the appropriate target recommendation method for each option to be configured, so as to reasonably allocate recommendation resources.

[0040] Optionally, the processor obtains the acceptance rate of the option to be configured, including: the processor obtains attribute information of the option to be configured; the processor inputs the attribute information of the option to be configured into the decision tree model to obtain the acceptance rate of the option to be configured. In this way, the embodiment of the present disclosure can obtain the attribute information of the option to be configured and input it into the trained decision tree model, so as to achieve accurate prediction of the acceptance rate of the option to be configured, which is conducive to predicting the success probability of recommending the option to be configured. In addition, it is possible to determine the appropriate target recommendation method for each option to be configured, so as to reasonably allocate recommendation resources.

[0041] Optionally, the processor determines the target recommendation mode of the to-be-configured option according to the acceptance rate of the to-be-configured option, including: when the acceptance rate of the to-be-configured option is greater than or equal to the preset acceptance rate, the processor determines the target recommendation mode of the to-be-configured option to be a collaborative filtering recommendation mode; or, when the acceptance rate of the to-be-configured option is less than the preset acceptance rate, the processor determines the target recommendation mode of the to-be-configured option to be a default recommendation mode. In this way, for some to-be-configured options whose acceptance rate is less than the preset acceptance rate, the probability of the user accepting the recommendation result is low, and it is easy to fail to recommend and cause resources to be wasted. Therefore, the embodiment of the present disclosure adopts the default recommendation mode to directly recommend the default configuration result to the user, thereby reducing the complexity of the recommendation algorithm, so as to more quickly determine the recommended configuration results of these to-be-configured options, which is conducive to improving the recommendation efficiency. For other to-be-configured options whose acceptance rate is greater than or equal to the preset acceptance rate, the user has a higher probability of successfully accepting the recommendation result. The embodiment of the present disclosure adopts the collaborative filtering recommendation mode to intelligently predict the user's selection results for these to-be-configured options, so that a more accurate recommendation algorithm can be used to more accurately determine the appropriate configuration results of these to-be-configured options, which is conducive to improving the recommendation accuracy. Therefore, the disclosed embodiment can take into account both the recommendation efficiency and the recommendation accuracy of the option configuration results, which can not only ensure the user's configuration experience but also reduce the recommendation cost of the device.

[0042] Combination Figure 2 As shown, the embodiment of the present disclosure provides another method for recommending option configuration, including:

[0043] S201: When a target device is performing option configuration, the processor obtains an acceptance rate of the option to be configured.

[0044] S202: When the acceptance rate of the option to be configured is greater than or equal to a preset acceptance rate, the processor determines that the target recommendation method of the option to be configured is a collaborative filtering recommendation method.

[0045] S203: The processor executes a collaborative filtering recommendation method to determine a recommended configuration result of the to-be-configured option on the target device.

[0046] S204: When the acceptance rate of the option to be configured is less than a preset acceptance rate, the processor determines that the target recommendation method of the option to be configured is a default recommendation method.

[0047] S205: The processor executes a default recommendation method to determine a recommended configuration result of the option to be configured on the target device.

[0048] According to the method for recommending option configuration provided by the embodiment of the present disclosure, when the target device performs option configuration, the acceptance rate of each to-be-configured option is first obtained, so that the success probability of recommending the to-be-configured option can be predicted. Then, the embodiment of the present disclosure determines the target recommendation method of each to-be-configured option based on this, so that the recommendation resources can be reasonably allocated in combination with the acceptance rate. For some to-be-configured options whose acceptance rate is less than the preset acceptance rate, the probability of the user accepting the recommendation result is low, and the recommendation failure is easy to cause the waste of resources. Therefore, the embodiment of the present disclosure adopts the default recommendation method to directly recommend the default configuration result to the user, so that the complexity of the recommendation algorithm can be reduced, so as to more quickly determine the recommended configuration results of these to-be-configured options, which is conducive to improving the recommendation efficiency. For other to-be-configured options whose acceptance rate is greater than or equal to the preset acceptance rate, the success probability of the user accepting the recommendation result is higher. The embodiment of the present disclosure adopts the collaborative filtering recommendation method to intelligently predict the user's selection results for these to-be-configured options, so that a more accurate recommendation algorithm can be used to more accurately determine the appropriate configuration results of these to-be-configured options, which is conducive to improving the recommendation accuracy. Therefore, the disclosed embodiment can take into account both the recommendation efficiency and the recommendation accuracy of the option configuration results, which can not only ensure the user's configuration experience but also reduce the recommendation cost of the device.

[0049] Optionally, the recommended method of the option configuration may be executed in the target device, or in a server communicating with the target device. In the embodiment of the present disclosure, the solution is described with the processor in the target device as the execution subject.

[0050] Optionally, the preset acceptance rate can be determined based on the average acceptance rate of the configured options. Preferably, the preset acceptance rate is set to 80% to reasonably predict the success probability of recommending the options to be configured. The preset acceptance rate can also be adjusted according to the actual needs of the user, and can also be adjusted to any other reasonable value such as 70% or 90%.

[0051] Optionally, the processor executes a collaborative filtering recommendation method to determine the recommended configuration results of the options to be configured on the target device, including: the processor obtains similar devices of the target device; the processor determines the configuration results of the options to be configured on the similar devices, and uses the configuration results as the recommended configuration results of the target device. In this way, for certain options to be configured with a higher acceptance rate, the embodiment of the present disclosure adopts a collaborative filtering recommendation method. By obtaining similar devices of the target device, and then determining the configuration results of the options to be configured on the similar devices, and recommending them as the recommended configuration results of the target device. In this way, it is possible to intelligently predict the user's selection results for these options to be configured, so as to more accurately determine the appropriate configuration results for these options to be configured, which is conducive to improving the accuracy of the recommendation.

[0052] Optionally, the processor executes a default recommendation method to determine the recommended configuration result of the option to be configured on the target device, including: the processor determines the preset configuration result as the recommended configuration result of the target device. In this way, for some options to be configured with a low acceptance rate, the embodiment of the present disclosure adopts a default recommendation method. By directly recommending the preset configuration results to the user, the complexity of the recommendation algorithm can be reduced, so that the recommended configuration results of these options to be configured can be determined more quickly, which is conducive to improving the recommendation efficiency.

[0053] Combination Figure 3 As shown, the embodiment of the present disclosure provides another method for recommending option configuration, including:

[0054] S301: When a target device is performing option configuration, the processor obtains an acceptance rate of the option to be configured.

[0055] S302: When the acceptance rate of the option to be configured is greater than or equal to a preset acceptance rate, the processor determines that the target recommendation method of the option to be configured is a collaborative filtering recommendation method.

[0056] S303: The processor obtains similar devices of the target device.

[0057] S304: The processor determines the configuration result of the to-be-configured option on the similar device, and uses the configuration result as the recommended configuration result for the target device.

[0058] S305: When the acceptance rate of the option to be configured is less than the preset acceptance rate, the processor determines that the target recommendation method of the option to be configured is the default recommendation method.

[0059] S306: The processor determines the preset configuration result as the recommended configuration result of the target device.

[0060] According to the method for recommending option configuration provided by the embodiment of the present disclosure, when the target device performs option configuration, the acceptance rate of each option to be configured is first obtained, so that the success probability of recommending the option to be configured can be predicted. Then, the embodiment of the present disclosure determines the target recommendation method of each option to be configured based on this, so that the recommendation resources can be reasonably allocated in combination with the acceptance rate. For some options to be configured whose acceptance rate is less than the preset acceptance rate, the probability of the user accepting the recommendation result is low, and the recommendation failure is easy to cause the waste of resources. Therefore, the embodiment of the present disclosure adopts the default recommendation method to directly recommend the preset configuration result to the user, so as to reduce the complexity of the recommendation algorithm, so as to more quickly determine the recommended configuration results of these options to be configured, which is conducive to improving the recommendation efficiency. For other options to be configured whose acceptance rate is greater than or equal to the preset acceptance rate, the success probability of the user accepting the recommended result is higher. The embodiment of the present disclosure adopts the collaborative filtering recommendation method, by obtaining similar devices of the target device, and then determining the configuration results of the options to be configured on the similar devices, and recommending them as the recommended configuration results of the target device. Therefore, it is possible to intelligently predict the user's selection results for these options to be configured, so as to more accurately determine the appropriate configuration results for these options to be configured, which is conducive to improving the recommendation accuracy. Therefore, the disclosed embodiment can take into account both the recommendation efficiency and the recommendation accuracy of the option configuration results, which can not only ensure the user's configuration experience, but also reduce the recommendation cost of the device.

[0061] Optionally, the recommended method of the option configuration may be executed in the target device, or in a server communicating with the target device. In the embodiment of the present disclosure, the solution is described with the processor in the target device as the execution subject.

[0062] Optionally, the processor obtains similar devices of the target device, including: the processor clusters the target device and devices of the same model as the target device to obtain devices of the same type as the target device; the processor calculates the similarity between the devices of the same type and the target device to obtain similar devices of the target device. In this way, the embodiment of the present disclosure can first classify the target device and devices of the same model as the target device through a clustering algorithm, and then calculate the similarity between the devices of the same type as the target device and the target device, so as to improve the recommendation accuracy and more quickly determine the similar devices of the target device.

[0063] Optionally, the processor clusters the target device and its same-model devices to obtain similar devices of the target device, including: the processor obtains a configuration data set of the target device and its same-model devices; the processor clusters the configuration data set according to the k-means algorithm to obtain multiple clusters of the target device and its same-model devices; the processor determines a target cluster that includes the target device, and determines the same-model devices within the target cluster as similar devices of the target device. In this way, the embodiment of the present disclosure can use the k-means algorithm to process the configuration data set of the target device and its same-model devices, so that data objects with high similarity can be divided into the same cluster, and data objects with high dissimilarity can be divided into different clusters. In this way, the target cluster that includes the target device can be determined, and then the similar devices of the target device in the target cluster can be accurately obtained, which is conducive to determining the similar devices of the target device.

[0064] Optionally, the processor clusters the configuration data set according to the k-means algorithm to obtain multiple clusters of the target device and its same-model devices, including: the processor determines the target cluster number k according to the elbow method; the processor clusters the configuration data set according to the target cluster number k according to the k-means algorithm to obtain k clusters of the target device and its same-model devices. In this way, the embodiment of the present disclosure can use the elbow method to determine the target cluster number k, so that the number of clusters to be divided can be reasonably limited, which is conducive to improving the accuracy of cluster division. Then the embodiment of the present disclosure uses the k-means algorithm to process the configuration data set of the target device and its same-model devices, so that the data objects can be divided into k clusters, and then the target cluster containing the target device can be obtained to obtain the same type of devices as the target device. By classifying the devices in advance, the embodiment of the present disclosure can make the subsequent recommendation algorithm more accurate.

[0065] Optionally, the processor calculates the similarity between the same type of device and the target device to obtain similar devices of the target device, including: the processor calculates the Hamming distance between the same type of device and the target device; the processor determines the similar devices of the target device based on the Hamming distance between the same type of device and the target device. In this way, the embodiment of the present disclosure can grasp the similarity between the same type of device and the target device by calculating the Hamming distance between the same type of device and the target device. Among them, the Hamming distance represents the number of different values ​​between two vectors, which can be obtained by performing an XOR operation on the configuration data of the same type of device and the target device, and calculating the number of 1s in the XOR operation result. The smaller the Hamming distance, the higher the similarity between the same type of device and the target device. Therefore, the embodiment of the present disclosure can determine the similar device with the highest similarity to the target device from one or more same type of devices.

[0066] Optionally, the processor calculates the Hamming distance between the same type of device and the target device, including: the processor obtains the configuration results of the options to be configured on all the same type of devices; the processor calculates the Hamming distance between the same type of devices whose configuration results are known and the target device. In this way, before calculating the Hamming distance between the same type of devices and the target device, the embodiment of the present disclosure first determines the configuration results of the options to be configured on the same type of devices. Then the embodiment of the present disclosure only includes the same type of devices whose configuration results are known into the calculation objects, that is, only the same type of devices that have been effectively configured with the options to be configured and the target device are calculated, which is conducive to reducing the amount of calculation. And it can filter out some invalid devices to prevent them from being unable to obtain corresponding recommended configuration results as similar devices.

[0067] Optionally, the processor determines similar devices of the target device according to the Hamming distance between the similar devices and the target device, including: the processor sorts the similar devices in ascending order according to the Hamming distance; the processor determines the similar device corresponding to the minimum Hamming distance as the similar device of the target device. In this way, the embodiment of the present disclosure sorts the similar devices in ascending order according to the Hamming distance, and the smaller the Hamming distance, the higher the similarity between the similar device and the target device. Therefore, the embodiment of the present disclosure can determine the similar device corresponding to the minimum Hamming distance as the similar device with the highest similarity to the target device.

[0068] Optionally, the processor determines the similar device corresponding to the minimum Hamming distance as a similar device to the target device, including: when the minimum Hamming distance corresponds to multiple similar devices, the processor determines the configuration ratio of one or more configuration results of the options to be configured on the multiple similar devices; the processor determines the similar devices of the target device based on the configuration ratio of the configuration results. In this way, when the minimum Hamming distance corresponds to multiple similar devices, it indicates that there are multiple similar devices with the same and highest similarity as the target device, and the configuration results of the options to be configured on these similar devices may not be exactly the same, so the recommended configuration result of the target device may not be effectively determined. Therefore, the embodiment of the present disclosure calculates the configuration ratio corresponding to each of the different configuration results of the options to be configured on the similar devices, so that the appropriate similar devices and recommended configuration results can be determined accordingly.

[0069] Optionally, the processor determines similar devices of the target device according to the configuration ratio of the configuration results, including: the processor determines the similar device of the configuration result corresponding to the highest configuration ratio as the similar device of the target device. In this way, the higher the configuration ratio, the more similar devices adopt the configuration result. Therefore, the embodiment of the present disclosure uses the configuration result corresponding to the highest configuration ratio as the recommended configuration result of the target device, and determines the corresponding similar device as the similar device of the target device, so that it can effectively recommend to the user and give the appropriate configuration result, which is conducive to improving the accuracy of the recommendation.

[0070] Optionally, the processor determines similar devices of the target device based on the Hamming distance between the similar devices and the target device, and further includes: when the highest configuration ratio corresponds to multiple configuration results, the processor determines the similar device corresponding to the next smallest Hamming distance as a similar device of the target device. In this way, when the highest configuration ratio corresponds to multiple configuration results, it indicates that there are multiple configuration results with the same and highest configuration ratio corresponding to the minimum Hamming distance, that is, the number of similar devices using these different configuration results is the same, resulting in the inability to effectively determine the recommended configuration result of the target device. Therefore, the embodiment of the present disclosure further obtains the similar device corresponding to the next smallest Hamming distance, and determines it as a similar device of the target device, so that it can effectively recommend to the user and give a suitable configuration result, which is conducive to improving the accuracy of the recommendation.

[0071] Further, referring to the above steps, if the recommended configuration result of the target device cannot be effectively determined among the similar devices corresponding to the second smallest Hamming distance, the embodiment of the present disclosure determines the similar device corresponding to the next (third smallest) Hamming distance as the similar device of the target device, and repeats the above operation until the recommended configuration result of the target device can be effectively determined. If the recommended configuration result of the target device cannot be effectively determined until the similar device corresponding to the last (largest) Hamming distance, the embodiment of the present disclosure determines the preset configuration result as the recommended configuration result of the target device, thereby ensuring the effectiveness of the recommendation.

[0072] Combination Figure 4 As shown, the embodiment of the present disclosure provides another method for recommending option configuration, including:

[0073] S401: When the target device is performing option configuration, the processor obtains attribute information of the option to be configured.

[0074] S402: The processor inputs the attribute information of the option to be configured into the decision tree model to obtain the acceptance rate of the option to be configured.

[0075] S403: The processor determines a target recommendation method for the option to be configured according to the acceptance rate of the option to be configured.

[0076] S404: The processor executes a target recommendation method to determine a recommended configuration result of the to-be-configured option on the target device.

[0077] By adopting the method for recommending option configuration provided by the embodiment of the present disclosure, when the target device performs option configuration, the attribute information of the option to be configured is first obtained and input into the trained decision tree model, so that the acceptance rate of the option to be configured can be accurately predicted, which is conducive to predicting the success probability of recommending the option to be configured. Then the embodiment of the present disclosure determines the target recommendation method for each option to be configured accordingly, so that the recommendation resources can be reasonably allocated in combination with the acceptance rate. By executing each adaptive target recommendation method, the embodiment of the present disclosure can reduce the complexity of the recommendation algorithm for some options to be configured, so that the corresponding recommended configuration results can be determined more quickly, which is conducive to improving the recommendation efficiency. For other options to be configured, the optimized recommendation algorithm is used to more accurately determine the corresponding recommended configuration results, which is conducive to improving the recommendation accuracy. Therefore, the embodiment of the present disclosure can take into account the recommendation efficiency and recommendation accuracy of the option configuration results, which can not only ensure the user's configuration experience, but also reduce the recommendation cost of the device.

[0078] Optionally, the recommended method of the option configuration may be executed in the target device, or in a server communicating with the target device. In the embodiment of the present disclosure, the solution is described with the processor in the target device as the execution subject.

[0079] Optionally, the processor trains the decision tree model in the following manner, including: the processor obtains the attribute information and acceptance rate of the configured options; the processor trains the decision tree model according to the attribute information and acceptance rate of the configured options. In this way, the embodiment of the present disclosure can obtain the configuration data set of the target device and its same model device as the training data set, and extract the attribute information and acceptance rate of one or more configured options therefrom to train the decision tree model. Therefore, the embodiment of the present disclosure can use the attribute information of the configured options as the independent variable and the acceptance rate of the configured options as the dependent variable, thereby realizing the establishment and training of the decision tree model.

[0080] Optionally, the attribute information is system-specific information for each configuration option. Taking a refrigerator device as an example, the attribute information of a configuration option may include relevant function type, whether it is voice-related, whether it is recipe-related, whether it is food-related, whether it is related to special groups, the level, etc. The attribute information can reflect the basic attributes of each configuration option and has a potential correlation with the user's acceptance rate.

[0081] Optionally, the acceptance rate is the adoption ratio of the recommended configuration results of each configuration option. The acceptance rate of the configured options can be obtained by analyzing the historical configuration information of the target device and its same-model devices. Specifically, the devices whose recommended configuration results are consistent with the current configuration results are determined from the target device and its same-model devices. On these devices, the recommended configuration results for the configured options are recognized and adopted by the users. Then, the ratio of the number of these devices to the number of all devices is calculated, thereby obtaining the acceptance rate of the configured options. The acceptance rate of the options to be configured is predicted by the trained decision tree model with reference to the aforementioned steps, which is helpful to predict the success probability of recommending the options to be configured.

[0082] Combination Figure 5 As shown, the embodiment of the present disclosure provides another method for recommending option configuration, including:

[0083] S501: When a target device is performing option configuration, the processor obtains the historical configuration times of the target device.

[0084] S502: When the number of historical configurations of the target device is greater than 0, the processor obtains an acceptance rate of the option to be configured.

[0085] S503: The processor determines a target recommendation method for the option to be configured according to the acceptance rate of the option to be configured.

[0086] S504: The processor executes a target recommendation method to determine a recommended configuration result of the to-be-configured option on the target device.

[0087] According to the method for recommending option configuration provided by the embodiment of the present disclosure, when the target device performs option configuration, the historical configuration times of the target device are first obtained to determine whether the currently performed option configuration operation is the first configuration operation of the target device. Only when the historical configuration times of the target device is greater than 0, that is, the currently performed option configuration operation is not the first configuration operation of the target device, and the user has previously performed the corresponding configuration operation on some options, the embodiment of the present disclosure obtains the acceptance rate of the option to be configured to predict the success probability of recommending the option to be configured. Then the embodiment of the present disclosure determines the target recommendation mode of each option to be configured based on this, so that the recommendation resources can be reasonably allocated in combination with the acceptance rate. By executing each adaptive target recommendation mode, the embodiment of the present disclosure can reduce the complexity of the recommendation algorithm for some options to be configured, so that the corresponding recommended configuration results can be determined more quickly, which is conducive to improving the recommendation efficiency. For other options to be configured, the optimized recommendation algorithm is used to more accurately determine the corresponding recommended configuration results, which is conducive to improving the recommendation accuracy. Therefore, the embodiment of the present disclosure can take into account the recommendation efficiency and recommendation accuracy of the option configuration results, which can not only ensure the configuration experience of the user, but also reduce the recommendation cost of the device.

[0088] Optionally, the recommended method of the option configuration may be executed in the target device, or in a server communicating with the target device. In the embodiment of the present disclosure, the solution is described with the processor in the target device as the execution subject.

[0089] Combination Figure 6 As shown, the embodiment of the present disclosure provides another method for recommending option configuration, including:

[0090] S601: When a target device is performing option configuration, the processor obtains the historical configuration times of the target device.

[0091] S602: When the number of historical configurations of the target device is greater than 0, the processor obtains an acceptance rate of the option to be configured.

[0092] S603: The processor determines a target recommendation method for the option to be configured according to the acceptance rate of the option to be configured.

[0093] S604: The processor executes a target recommendation method to determine a recommended configuration result of the to-be-configured option on the target device.

[0094] S605: When the number of historical configuration times of the target device is equal to 0, the processor executes a cold start recommendation method to determine a recommended configuration result of the to-be-configured option on the target device.

[0095] According to the method for recommending option configuration provided by the embodiment of the present disclosure, when the target device performs option configuration, the historical configuration times of the target device are first obtained to determine whether the currently performed option configuration operation is the first configuration operation of the target device. When the historical configuration times of the target device is equal to 0, it indicates that the currently performed option configuration operation is the first configuration operation of the target device, and the target device is a new device. Since there are usually many options to be configured on a new device, it takes a long time to recommend them one by one, so the recommendation efficiency is poor. And there are no or few configured options on the new device, so the data richness is not high and the recommendation accuracy is average. In this case, the embodiment of the present disclosure performs a cold start recommendation method to quickly obtain the recommended configuration results of the options to be configured on the target device, thereby improving the recommendation efficiency and ensuring the recommendation accuracy. When the historical configuration times of the target device is greater than 0, that is, the currently performed option configuration operation is not the first configuration operation of the target device, and the user has previously performed the corresponding configuration operations on some options, the embodiment of the present disclosure obtains the acceptance rate of the options to be configured to predict the success probability of recommending the options to be configured. Then the embodiment of the present disclosure determines the target recommendation method of each option to be configured based on this, so that the recommendation resources can be reasonably allocated in combination with the acceptance rate. By executing each adaptive target recommendation method, the disclosed embodiment can reduce the complexity of the recommendation algorithm for certain options to be configured, so that the corresponding recommended configuration results can be determined more quickly, which is conducive to improving the recommendation efficiency. For other options to be configured, the optimized recommendation algorithm is used to more accurately determine the corresponding recommended configuration results, which is conducive to improving the recommendation accuracy. Therefore, the disclosed embodiment can take into account the recommendation efficiency and recommendation accuracy of the option configuration results, which can not only ensure the user's configuration experience, but also reduce the recommendation cost of the device.

[0096] Optionally, the recommended method of the option configuration may be executed in the target device, or in a server communicating with the target device. In the embodiment of the present disclosure, the solution is described with the processor in the target device as the execution subject.

[0097] Optionally, the processor executes a cold start recommendation method to determine the recommended configuration result of the option to be configured on the target device, including: the processor obtains the configuration ratio of one or more configuration results of the option to be configured on the same model device; the processor determines the recommended configuration result of the option to be configured on the target device according to the configuration ratio of the configuration result. In this way, the embodiment of the present disclosure can count one or more configuration results of the option to be configured on the same model device, and determine the recommended configuration result of the option to be configured on the target device according to the configuration ratio of each configuration result, thereby improving the recommendation efficiency and ensuring the recommendation accuracy.

[0098] Optionally, the processor determines the recommended configuration result of the option to be configured on the target device according to the configuration ratio of the configuration results, including: the processor determines the configuration result corresponding to the highest configuration ratio as the recommended configuration result of the option to be configured on the target device. In this way, the higher the configuration ratio, the more devices of the same type adopt the configuration result. Therefore, the embodiment of the present disclosure uses the configuration result corresponding to the highest configuration ratio as the recommended configuration result of the option to be configured on the target device, thereby improving the recommendation efficiency and ensuring the recommendation accuracy.

[0099] Optionally, when the number of historical configurations of the target device is equal to 0, in step S605, the cold start recommendation method may not be executed, but the default recommendation method may be executed instead to determine the recommended configuration result of the option to be configured on the target device. In this way, the embodiment of the present disclosure can directly recommend the default configuration result to the user, thereby reducing the recommendation time, so as to more quickly determine the recommended configuration result of the option to be configured, which is conducive to further improving the recommendation efficiency.

[0100] Combination Figure 7 As shown, the embodiment of the present disclosure provides a device 700 for recommending option configuration, including a processor 701 and a memory 702. Optionally, the device 700 may also include a communication interface 703 and a bus 704. The processor 701, the communication interface 703, and the memory 702 may communicate with each other through the bus 704. The communication interface 703 may be used for information transmission. The processor 701 may call the logic instructions in the memory 702 to execute the method for recommending option configuration of the above embodiment.

[0101] In addition, the logic instructions in the memory 702 described above may be implemented in the form of software functional units and when sold or used as independent products, may be stored in a computer-readable storage medium.

[0102] The memory 702 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the methods in the embodiments of the present disclosure. The processor 701 executes the function application and data processing by running the program instructions / modules stored in the memory 702, that is, implementing the recommended method for option configuration in the above embodiment.

[0103] The memory 702 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 702 may include a high-speed random access memory and may also include a non-volatile memory.

[0104] Combination Figure 8 As shown, an embodiment of the present disclosure provides a refrigerator device, including: a refrigerator body 800, and the above-mentioned recommended device 700 for option configuration. The refrigerator body 800 is provided with a screen-end system 801. The recommended device 700 for option configuration is installed on the refrigerator body 800 and is electrically connected to the screen-end system 801. The installation relationship described here is not limited to being placed inside the refrigerator body 800, but also includes installation connections with other components of the refrigerator device, including but not limited to physical connections, electrical connections or signal transmission connections, etc. Those skilled in the art can understand that the recommended device 700 for option configuration can be adapted to a feasible device body, thereby realizing other feasible embodiments.

[0105] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned method for recommending option configuration.

[0106] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes.

[0107] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0108] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0109] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0110] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for recommending option configuration, characterized in that: include: When the target device is performing option configuration, obtaining the acceptance rate of the option to be configured; Determine a target recommendation method for the options to be configured according to the acceptance rate of the options to be configured; Execute the target recommendation method to determine the recommended configuration results of the options to be configured on the target device.

2. The method according to claim 1, characterized in that According to the acceptance rate of the options to be configured, determine the target recommendation method of the options to be configured, including: When the acceptance rate of the option to be configured is greater than or equal to the preset acceptance rate, determining that the target recommendation method of the option to be configured is a collaborative filtering recommendation method; or, When the acceptance rate of the option to be configured is less than the preset acceptance rate, the target recommendation mode of the option to be configured is determined to be the default recommendation mode.

3. The method according to claim 2, characterized in that When the target recommendation method is a collaborative filtering recommendation method, the target recommendation method is executed to determine the recommended configuration result of the option to be configured on the target device, including: Get similar devices to the target device; Determine the configuration results of the to-be-configured options on similar devices, and use the configuration results as recommended configuration results for the target device.

4. The method according to claim 3, characterized in that Get similar devices of the target device, including: Cluster the target device and devices of the same model to obtain devices of the same type as the target device; Calculate the similarity between the same type of devices and the target device, and obtain similar devices of the target device.

5. The method according to claim 4, characterized in that Calculate the similarity between the target device and the same device, and obtain similar devices of the target device, including: Calculate the Hamming distance between similar devices and the target device; Determine similar devices to the target device based on the Hamming distance between similar devices and the target device.

6. The method according to claim 2, characterized in that When the target recommendation mode is the default recommendation mode, the target recommendation mode is executed to determine the recommended configuration results of the options to be configured on the target device, including: The preset configuration result is determined as the recommended configuration result for the target device.

7. The method according to any one of claims 1 to 6, characterized in that: Get the acceptance rate of the options to be configured, including: Get the property information of the options to be configured; The attribute information of the options to be configured is input into the decision tree model to obtain the acceptance rate of the options to be configured.

8. The method according to claim 7, characterized in that Train the decision tree model as follows, including: Get the attribute information and acceptance rate of the configured options; Train a decision tree model based on the attribute information and acceptance rates of the configured options.

9. The method according to any one of claims 1 to 6, characterized in that: When the target device is configuring options, the acceptance rate of the options to be configured is obtained, including: When the target device is performing option configuration, obtain the historical configuration times of the target device; When the number of historical configuration times of the target device is greater than 0, the acceptance rate of the option to be configured is obtained.

10. The method according to claim 9, characterized in that Also includes: When the number of historical configuration times of the target device is equal to 0, a cold start recommendation method is executed to determine a recommended configuration result of the to-be-configured option on the target device.

11. A recommendation device for option configuration, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for recommending option configuration according to any one of claims 1 to 10 when running the program instructions.

12. A refrigerator device, characterized in that: include: The refrigerator body is equipped with a screen terminal system; The recommendation device for option configuration as described in claim 11 is installed on the refrigerator body and electrically connected to the screen end system.

13. A computer-readable storage medium storing program instructions, characterized in that: When the program instructions are executed, the computer is used to execute the method for recommending option configuration according to any one of claims 1 to 10.

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