CN119338547B - Method, device, equipment and storage medium for recommending entry resources - Google Patents
Method, device, equipment and storage medium for recommending entry resourcesInfo
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- CN119338547B CN119338547B CN202411322806.2A CN202411322806A CN119338547B CN 119338547 B CN119338547 B CN 119338547B CN 202411322806 A CN202411322806 A CN 202411322806A CN 119338547 B CN119338547 B CN 119338547B
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Abstract
本公开提供了一种入口资源的推荐方法、装置、设备及存储介质，涉及资源分发、资源推荐、人工智能等技术领域。具体实现方案为：获取用户的特征和待推荐的各入口资源的特征；基于所述用户的特征和各所述入口资源的特征，采用预先训练的入口资源价值评估模型，评估各入口资源的价值；基于各所述入口资源的价值，向所述用户推荐至少一个入口资源。本公开的技术，能够有效提高入口资源的推荐的准确性和推荐效率。
The present disclosure provides a method, apparatus, device, and storage medium for recommending portal resources, relating to technical fields such as resource distribution, resource recommendation, and artificial intelligence. A specific implementation scheme comprises: obtaining user characteristics and the characteristics of each portal resource to be recommended; using a pre-trained portal resource value assessment model to assess the value of each portal resource based on the user characteristics and the characteristics of each portal resource; and recommending at least one portal resource to the user based on the value of each portal resource. The technology disclosed herein can effectively improve the accuracy and efficiency of portal resource recommendations.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to the technical fields of resource distribution, resource recommendation, artificial intelligence and the like, and particularly relates to a recommendation method, device and equipment of portal resources and a storage medium.
Background
Based on the increasing demands for consumption efficiency and popularity of immersive consumption habits, more and more users enjoy immersive consumption of resources.
In the existing immersive recommendation scene, a user enters the immersive recommendation scene through an entrance resource, and the entrance resource is very important as a very critical resource for screening. Typically, multiple portal resources recalled from the repository may be ranked, and then the portal resources are screened and recommended according to the ranking.
Disclosure of Invention
The disclosure provides a recommendation method, device, equipment and storage medium of portal resources.
According to an aspect of the present disclosure, there is provided a recommendation method for an ingress resource, including:
acquiring characteristics of a user and characteristics of each entry resource to be recommended;
Based on the characteristics of the user and the characteristics of each portal resource, evaluating the value of each portal resource by adopting a pre-trained portal resource value evaluation model;
at least one portal resource is recommended to the user based on the value of each portal resource.
According to another aspect of the present disclosure, there is provided a training method of an inlet resource value evaluation model, including:
generating a training data set, wherein the training data set comprises the characteristics of a training user, the characteristics of two training entrance resources and the real size relation of the values of the two training entrance resources;
Based on the training data set, predicting the predicted magnitude relation of the values of the two training entrance resources by adopting an entrance resource value evaluation model;
and adjusting parameters of the portal resource value evaluation model based on the predicted size relation and the real size relation of the values of the two training portal resources.
According to still another aspect of the present disclosure, there is provided a recommendation device for portal resources, including:
The acquisition module is used for acquiring the characteristics of the user and the characteristics of each entry resource to be recommended;
The evaluation module is used for evaluating the value of each entrance resource by adopting a pre-trained entrance resource value evaluation model based on the characteristics of the user and the characteristics of each entrance resource;
And the recommending module is used for recommending at least one entrance resource to the user based on the value of each entrance resource.
According to still another aspect of the present disclosure, there is provided a training apparatus of an inlet resource value evaluation model, including:
the generation module is used for generating a training data set, wherein the training data set comprises the characteristics of a training user, the characteristics of two training entrance resources and the real size relation of the values of the two training entrance resources;
The prediction module is used for predicting the predicted magnitude relation of the values of the two training entrance resources by adopting an entrance resource value evaluation model based on the training data set;
And the adjustment module is used for adjusting the parameters of the portal resource value evaluation model based on the predicted size relation and the real size relation of the values of the two training portal resources.
According to still another aspect of the present disclosure, there is provided an electronic apparatus including:
At least one processor, and
A memory communicatively coupled to the at least one processor, wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the aspects and methods of any one of the possible implementations described above.
According to yet another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method of the aspects and any possible implementations as described above.
According to yet another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of the aspects and any of the possible implementations described above.
According to the technology disclosed by the invention, the accuracy and the recommendation efficiency of the recommendation of the entrance resources can be effectively improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present disclosure;
FIG. 2 is a schematic diagram according to a second embodiment of the present disclosure;
FIG. 3 is a schematic diagram according to a third embodiment of the present disclosure;
FIG. 4 is a schematic diagram according to a fourth embodiment of the present disclosure;
FIG. 5 is a schematic diagram according to a fifth embodiment of the present disclosure;
FIG. 6 is a schematic diagram according to a sixth embodiment of the present disclosure;
FIG. 7 is a schematic diagram according to a seventh embodiment of the present disclosure;
FIG. 8 is a schematic diagram according to an eighth embodiment of the present disclosure;
Fig. 9 is a block diagram of an electronic device for implementing the methods of embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It will be apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.
It should be noted that, the terminal device according to the embodiments of the present disclosure may include, but is not limited to, a smart device such as a mobile phone, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA), a wireless handheld device, a Tablet Computer (Tablet Computer), and the like, and the display device may include, but is not limited to, a device with a display function such as a Personal Computer, a television, and the like.
In addition, the term "and/or" is merely an association relation describing the association object, and means that three kinds of relations may exist, for example, a and/or B, and that three kinds of cases where a exists alone, while a and B exist alone, exist alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Fig. 1 is a schematic diagram of a first embodiment of the present disclosure, and as shown in fig. 1, the present embodiment provides an entry resource recommendation method applied to recommendation of an entry resource in a resource recommendation scenario, which may specifically include the following steps:
s101, acquiring characteristics of a user and characteristics of each entry resource to be recommended;
s102, based on the characteristics of the user and the characteristics of each portal resource, evaluating the value of each portal resource by adopting a pre-trained portal resource value evaluation model;
S103, recommending at least one portal resource to the user based on the value of each portal resource.
The execution body of the portal resource recommendation method in this embodiment may be a portal resource recommendation device, where the portal resource recommendation device may be an electronic entity, or may also be a software integrated application, and by adopting a portal resource value evaluation model, the portal resource recommendation is accurately and effectively performed.
In this embodiment, the resource recommendation system has detected that the user is browsing the current home page before entering the resource recommendation. At this time, the resource recommendation system needs to recall some entry resources to be recommended from the resource library, and perform coarse and fine ranking to obtain the ranking of the entry resources to be recommended. The specific processes of resource recall and coarse and fine scheduling can refer to the related technology, and are not repeated here.
In general, the entry resources may be recommended directly based on the ranking of the entry resources after coarse and fine ranking, but the recommended manner of the entry resources does not consider the value of each entry resource, resulting in low accuracy of the recommended entry resources.
In order to overcome the technical problems, a pre-trained portal resource value evaluation model is introduced in the embodiment, and at least one portal resource is recommended to a user more accurately based on the value of each portal resource by evaluating the value of each portal resource to be recommended.
In this embodiment, each of the portal resources to be recommended may be a plurality of portal resources in the prior art, for example, the first 100 or 200 portal resources in the ranking may be selected.
The portal resource recommendation method of the embodiment can be applied to recommendation of portal resources in an immersive resource recommendation scene, and can also be applied to recommendation of portal resources in other resource recommendation scenes, and is not limited herein.
According to the portal resource recommendation method, the value of each portal resource to be recommended is evaluated by adopting the portal resource value evaluation model, so that at least one portal resource can be more accurately recommended to a user based on the value of each portal resource, and the accuracy and the recommendation efficiency of portal resource recommendation can be effectively improved.
Fig. 2 is a schematic diagram of a second embodiment of the disclosure, and the method for recommending portal resources according to the present embodiment, based on the technical solution of the embodiment shown in fig. 1, further describes the technical solution of the disclosure in more detail, as shown in fig. 2, and specifically may include the following steps:
s201, acquiring at least one of attribute characteristics of a user and historical consumption characteristics of the user;
In this embodiment, the attribute features of the user may include at least one of basic attribute features of the user including age, gender, occupation, etc. of the user and preference features of the user. The preference characteristics of the user include interests of the user, etc. The user's preference characteristics may be identified in the user's attribute information in the form of tags, for example, interests may include travel, entertainment, football, and the like. The attribute characteristics of the user have certain relevance with the resources, and can provide reference for the satisfaction degree prediction of the user on the resources. For example, different ages may have different resource preferences, such as middle and old ages like to consume video resources, middle and old aged office workers like to consume graphics and text resources, middle and old ages like to consume fashion resources, middle and old ages like to consume financial resources, and old ages like to consume health care resources, etc.
The historical consumption characteristics of the user include at least one of a consumption duty ratio of the user to consume the portal resources of the various genres, a click-through rate of the portal resources of the various genres, and a comprehensive consumption duration of the portal resources of the various genres for a plurality of historical time periods prior to the current period. The comprehensive consumption duration of each portal resource in each genre may include the consumption duration of the portal resource and the consumption duration of all resources immersively consumed after the user clicks through the portal resource. The total consumption time of the portal resources of a genre during a historical time period may be equal to the sum of the total consumption time of all portal resources of the genre during the historical time period.
If a certain genre of the resource to be recommended belongs to a resource genre with higher consumption proportion in a plurality of historical time periods, the corresponding value is higher. Otherwise, if the genre of the resource to be recommended belongs to the genre of the resource with the lowest consumption ratio in a plurality of historical time periods, the corresponding value is lower. Similarly, if the comprehensive consumption time of the resource to be recommended of a certain genre is relatively high in a plurality of historical time periods, the value of the resource of the genre is higher. Otherwise, if the integrated consumption time of the resources to be recommended of the genre is relatively low in a plurality of historical time periods, the corresponding value will be lower, and so on.
The length of the current period may be a preset time length defined in the resource recommendation scenario, e.g., the current period may be the current day, the current week, etc. The plurality of historical time periods may be configured according to requirements, and may include, for example, one historical day, three historical days, seven historical days, etc., and the length and number of the specific historical time periods are not limited herein. The consumption proportion of the resources of various genres consumed by the user in each historical time period, the click rate of the entrance resources of various genres and the comprehensive consumption duration of the entrance resources of various genres can be obtained by counting the historical consumption information of the user.
S202, acquiring at least one of the length, the title, the label, the genre and the acquired score of each entry resource;
When the genre of the entry resource is a video resource, the length of the entry resource refers to the duration of the video, and when the genre of the entry resource is an image-text resource, the length of the entry resource refers to the length of the text included in the entry resource and the number of the pictures included in the entry resource. The genre of the portal resource in this embodiment may include a short video and/or a small video, and the genre of the portal resource in this embodiment may include a dynamic graphic and/or a text graphic. Wherein the playing time length of the small video is smaller than that of the short video. The graphics include dynamic graphics and/or text graphics. Wherein the dynamic graphics must include a picture, and may include a small amount of text for describing the picture. The text graphic must include text and may include a small number of pictures for interpreting the text.
The genre characteristic of the resource in the entry of the embodiment may refer to the genre characteristic of the resource in the entry of the resource, and the genre of the resource in the entry of the resource can be identified as video or graphics.
And the label of the entrance resource is used for representing the category of the content of the entrance resource.
The score of an entry resource may refer to the score of the entry resource during coarse and fine scheduling.
Steps S201 to S202 are a specific implementation manner of step S101 in the embodiment shown in fig. 1. By the method, the characteristics of the user and the characteristics of the entrance resources can be comprehensively, abundantly and accurately obtained.
S203, based on the characteristics of the user and the characteristics of each portal resource, evaluating the value of each portal resource by adopting a pre-trained portal resource value evaluation model;
Specifically, for each portal resource, the user's characteristics and the characteristics of the portal resource are input into a portal resource value assessment model, which can predict and output the value of the portal resource.
S204, acquiring updated scores of the portal resources based on the values of the portal resources and the acquired scores of the portal resources;
For example, in a particular implementation, the value of the entry resource and the score of the entry resource may be multiplied as an updated score of the entry resource. Or the update scores of the portal resources can be updated based on the value of the portal resources and by adopting other mathematical algorithms, and the details are not repeated here.
S205, recommending at least one portal resource to the user based on the updated scores of the portal resources.
Specifically, according to the updated score of each entry resource, the entry resources may be ranked from large to small, and then according to the ranking, at least one entry resource in front is obtained for recommendation.
Steps S204-S205 are a specific implementation of step S103 in the embodiment shown in fig. 1.
The portal resource recommendation method can acquire rich and comprehensive user characteristics and characteristics of portal resources to be recommended, further adopts a portal resource value evaluation model, can accurately and efficiently evaluate the value of each portal resource based on the acquired characteristics, and further can recommend the portal resources more accurately and effectively based on the value of each portal resource.
Fig. 3 is a schematic diagram of a third embodiment of the disclosure, and as shown in fig. 3, the embodiment provides a training method of an inlet resource value evaluation model, which specifically may include the following steps:
s301, generating a training data set, wherein the training data set comprises the characteristics of a training user, the characteristics of two training entrance resources and the real size relation of the values of the two training entrance resources;
The execution subject of the training method of the portal resource value evaluation model in this embodiment is a training device of the portal resource value evaluation model, and the device may be an electronic entity or may also be an application of software integration.
In this embodiment, the training data set may be generated based on historical consumption information of the user.
The true magnitude relation of the values of the two training portal resources in the training data set in this embodiment is greater or less, and cannot be equal to.
S302, based on a training data set, predicting the predicted magnitude relation of the values of two training entrance resources by adopting an entrance resource value evaluation model;
S303, adjusting parameters of the portal resource value evaluation model based on the predicted size relation and the actual size relation of the values of the two training portal resources.
According to the training method of the portal resource value evaluation model, during training, the portal resource value evaluation model is trained based on the real magnitude relation of the values of two training portal resources in the training data set, so that the training difficulty of the portal resource value evaluation model can be effectively reduced, the training precision is ensured, and meanwhile, the training efficiency of the portal resource value evaluation model can be effectively improved.
Fig. 4 is a schematic diagram of a fourth embodiment of the disclosure, and as shown in fig. 4, the training method of the inlet resource value evaluation model of the present embodiment further introduces the technical solution of the disclosure in more detail on the basis of the technical solution of the embodiment shown in fig. 3. As shown in fig. 4, the training method of the inlet resource value evaluation model of the present embodiment may specifically include the following steps:
S401, constructing characteristics of a training user based on historical consumption information of the training user;
For example, specifically may include constructing at least one of a training user's attribute features and a user's historical consumption features based on the training user's historical consumption information.
The training user's attribute features include at least one of user's basic attribute features and user's preference features, and the training user's historical consumption features include at least one of consumption ratio of resources of various genres consumed by the user in a plurality of historical time periods before the training user consumes the corresponding portal resource features, click rate of portal resources of various genres, and comprehensive consumption duration of portal resources of various genres. Reference may also be made to the description of the embodiment shown in fig. 2, and details thereof are not repeated here.
S402, constructing features and consumption information of two training portal resources based on historical consumption information of training users;
For example, the feature of two training portal resources is collected from the historical consumption information of the training user, which specifically may include:
At least one of a length, a title, a label, a genre, and an acquired score of each of the two training portal resources is collected from historical consumption information of the training user.
When the genre of the training portal resource is the image-text resource, the length of the training portal resource refers to the length of the text included in the training portal resource and the number of the included pictures.
The tags of the portal resources are trained to represent the categories of content of the portal resources. The detailed acquisition process may also refer to the related description of the embodiment shown in fig. 2, and will not be described herein.
S403, configuring the true size relation of the values of the two training portal resources based on consumption information of the two training portal resources;
For example, in this embodiment, the specific configuration procedure may include the following manner:
(1) Based on the consumption time of each training portal resource in the two training portal resources consumed by the training user, configuring the value of the training portal resource with long consumption time to be larger than the value of the training portal resource with short consumption time;
(2) Based on consumption step length of each training entrance resource in two training entrance resources consumed by a training user, configuring the value of the training entrance resource with a large consumption step length to be larger than the value of the training entrance resource with a small consumption step length;
(3) Configuring the value of the training portal resources clicked by the training user to be greater than the value of the training portal resources not clicked by the training user based on the click information of each training portal resource in the two training portal resources consumed by the training user, or
(4) Based on the sliding information of the training user after clicking the training portal resources in the two training portal resources, the value of the training portal resources with sliding after clicking the training user is configured to be greater than the value of the training portal resources without sliding after clicking the training user.
By the method, the true magnitude relation of the values of the two training portal resources can be accurately and reasonably identified.
Steps S401 to S403 are the generation process of the training data set in the embodiment shown in fig. 3. In this way, the training data set can be accurately and reasonably generated.
S404, predicting the value of each training entrance resource by adopting an entrance resource value evaluation model based on the characteristics of the training user and the characteristics of each training entrance resource;
s405, acquiring a predicted size relation of the values of the two training portal resources based on the values of each training portal resource in the two training portal resources;
s406, detecting whether the predicted size relation and the actual size relation of the values of the two training portal resources are consistent;
S407, in response to the fact that the predicted size relationship and the real size relationship of the values of the two training portal resources are inconsistent, parameters of the portal resource value evaluation model are adjusted, so that the predicted size relationship and the real size relationship of the values of the two training portal resources are consistent.
In addition, in this embodiment, if the predicted magnitude relation and the actual magnitude relation of the values of the two training portal resources are consistent, parameters of the portal resource value evaluation model are not adjusted at this time, and the portal resource value evaluation model is continuously trained by using the next training data set until the training deadline condition is met, and the training is ended, so as to obtain a trained portal resource value evaluation model.
According to the training method of the portal resource value evaluation model, during training, the portal resource value evaluation model is trained based on the real magnitude relation of the values of two training portal resources in the training data set, so that the training difficulty of the portal resource value evaluation model can be effectively reduced, the training precision is ensured, and meanwhile, the training efficiency of the portal resource value evaluation model can be effectively improved.
Fig. 5 is a schematic diagram of a fifth embodiment of the present disclosure, and as shown in fig. 5, the present embodiment provides a recommendation device 500 for portal resources, which is applied to recommendation of portal resources in a resource recommendation scenario, and includes:
The acquiring module 501 is configured to acquire characteristics of a user and characteristics of each entry resource to be recommended;
An evaluation module 502, configured to evaluate the value of each portal resource by using a pre-trained portal resource value evaluation model based on the characteristics of the user and the characteristics of each portal resource;
a recommending module 503, configured to recommend at least one portal resource to the user based on the value of each portal resource.
The recommendation device 500 for portal resources in this embodiment, by adopting the above modules to implement the implementation principle and the technical effect of the recommendation of portal resources, is the same as the implementation of the above related method embodiments, and detailed description of the above related method embodiments may be referred to, and will not be repeated here.
Fig. 6 is a schematic diagram of a sixth embodiment of the disclosure, and as shown in fig. 6, the recommendation device 600 for portal resources of the present embodiment further describes the technical solution of the disclosure in more detail on the basis of the technical solution of the embodiment shown in fig. 5. As shown in fig. 6, the recommendation device 600 for portal resources in this embodiment includes the same name and function modules as shown in fig. 5, that is, an acquisition module 601, an evaluation module 602, and a recommendation module 603.
In this embodiment, the obtaining module 601 is configured to:
Acquiring at least one of attribute characteristics of a user and historical consumption characteristics of the user;
the user's attribute features include at least one of user's basic attribute features and user's preference features, and the user's historical consumption features include at least one of consumption ratio of the user's consumption of the portal resources of various genres, click rate of the portal resources of various genres, and comprehensive consumption duration of the portal resources of various genres in a plurality of historical time periods before the current period.
Further alternatively, in one embodiment of the present disclosure, the obtaining module 601 is configured to:
Acquiring at least one of a length, a title, a label, a genre, and an acquired score of each of the portal resources;
when the genre of the entry resource is an image-text resource, the length of the entry resource refers to the length of a text included in the entry resource and the number of pictures included in the entry resource;
the tag of the portal resource is used for representing the category of the content of the portal resource.
Further alternatively, as shown in fig. 6, in one embodiment of the present disclosure, the recommendation module 603 includes:
An updating unit 6031 for acquiring updated scores of the portal resources based on the value of the portal resources and the acquired scores of the portal resources;
And a recommending unit 6032 for recommending at least one portal resource to the user based on the updated score of each portal resource.
Further alternatively, in an embodiment of the present disclosure, the updating unit 6031 is configured to:
for each of the portal resources, multiplying the value of the portal resource by the score of the portal resource as an updated score for the portal resource.
The recommendation device 600 for portal resources in this embodiment, by adopting the above modules to implement the implementation principle and the technical effect of the recommendation for portal resources, is the same as the implementation of the above related method embodiments, and detailed description of the above related method embodiments may be referred to, and will not be repeated here.
Fig. 7 is a schematic diagram of a seventh embodiment of the disclosure, and as shown in fig. 7, the embodiment provides a training apparatus 700 of an inlet resource value evaluation model, including:
a generating module 701, configured to generate a training data set, where the training data set includes features of a training user, features of two training portal resources, and a true magnitude relation of values of the two training portal resources;
a prediction module 702, configured to predict a predicted magnitude relation of values of the two training portal resources by using a portal resource value evaluation model based on the training data set;
and the adjustment module 703 is configured to adjust parameters of the portal resource value evaluation model based on the predicted size relationship and the actual size relationship of the values of the two training portal resources.
The training device 700 for the inlet resource value evaluation model according to the present embodiment implements the implementation principle and the technical effect of training the inlet resource value evaluation model by using the above modules, and is the same as the implementation of the above related method embodiments, and details of the above related method embodiments may be referred to in the description of the related method embodiments, which is not repeated herein.
Fig. 8 is a schematic diagram of an eighth embodiment of the disclosure, and as shown in fig. 8, the training apparatus 800 for an inlet resource value evaluation model of the present embodiment further describes the technical solution of the disclosure in more detail on the basis of the technical solution of the embodiment shown in fig. 7. As shown in fig. 8, the recommendation device 800 for portal resources in this embodiment includes the same name and function modules shown in fig. 7, namely a generating module 801, a predicting module 802 and an adjusting module 803.
In this embodiment, the generating module 801 is configured to:
constructing characteristics of the training user based on the historical consumption information of the training user;
based on the historical consumption information of the training user, constructing the characteristics and consumption information of the two training portal resources;
And configuring the real size relation of the values of the two training portal resources based on the consumption information of the two training portal resources.
Optionally, in an embodiment of the disclosure, the generating module 801 is configured to:
Collecting at least one of attribute characteristics of the training user and historical consumption characteristics of the user based on the historical consumption information of the training user;
The training user attribute features comprise at least one of basic attribute features of the user and preference features of the user, and the training user historical consumption features comprise at least one of consumption proportion of resources of various genres consumed by the user in a plurality of historical time periods before the training user consumes corresponding entrance resource features, clicking rate of the entrance resources of various genres and comprehensive consumption duration of the entrance resources of various genres.
Optionally, in an embodiment of the disclosure, the generating module 801 is configured to:
Collecting at least one of the length, title, label, genre, and acquired score of each of the two training portal resources from the historical consumption information of the training user;
When the genre of the training portal resource is an image-text resource, the length of the training portal resource refers to the length of texts included in the training portal resource and the number of included pictures;
the tag of the training portal resource is used for representing the category of the content of the portal resource.
Optionally, in an embodiment of the disclosure, the generating module 801 is configured to:
Based on the consumption time of each training portal resource in the two training portal resources consumed by the training user, configuring the value of the training portal resource with long consumption time to be larger than the value of the training portal resource with short consumption time;
based on the consumption step length of each training portal resource in the two training portal resources consumed by the training user, configuring the value of the training portal resource with a large consumption step length to be larger than the value of the training portal resource with a small consumption step length;
configuring the value of the training portal resources clicked by the training user to be greater than the value of the training portal resources not clicked by the training user based on the click information of each training portal resource in the two training portal resources consumed by the training user, or
Based on the sliding information of the training user after clicking the training portal resources in the two training portal resources, the value of the training portal resources with sliding after clicking the training user is configured to be larger than the value of the training portal resources without sliding after clicking the training user.
Further alternatively, as shown in fig. 8, in one embodiment of the present disclosure, the prediction module 802 includes:
A prediction unit 8021, configured to predict the value of each training portal resource by using the portal resource value evaluation model based on the feature of the training user and the feature of each training portal resource;
an obtaining unit 8022, configured to obtain a predicted magnitude relation of the values of the two training portal resources based on the value of each of the two training portal resources.
Further alternatively, as shown in fig. 8, in one embodiment of the present disclosure, the adjusting module 803 includes:
A detecting unit 8031, configured to detect whether the predicted size relationship and the actual size relationship of the values of the two training portal resources are consistent;
an adjusting unit 8032, configured to adjust parameters of the value evaluation model of the portal resources in response to the inconsistent predicted size relationship and the actual size relationship of the values of the two training portal resources, so that the predicted size relationship and the actual size relationship of the values of the two training portal resources are consistent.
The training device 800 for the inlet resource value evaluation model according to the present embodiment implements the implementation principle and the technical effect of training the inlet resource value evaluation model by using the above modules, and is the same as the implementation of the above related method embodiment, and details of the above related method embodiment may be referred to in the description of the above related method embodiment, which is not repeated herein.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 9 shows a schematic block diagram of an example electronic device 900 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 9, the apparatus 900 includes a computing unit 901 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 902 or a computer program loaded from a storage unit 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other by a bus 904. An input/output (I/O) interface 905 is also connected to the bus 904.
Various components in the device 900 are connected to the I/O interface 905, including an input unit 906 such as a keyboard, a mouse, etc., an output unit 907 such as various types of displays, speakers, etc., a storage unit 908 such as a magnetic disk, an optical disk, etc., and a communication unit 909 such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunications networks.
The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 performs the respective methods and processes described above, for example, the above-described methods of the present disclosure. For example, in some embodiments, the above-described methods of the present disclosure may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 900 via the ROM 902 and/or the communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of the above-described methods of the present disclosure described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the above-described methods of the present disclosure in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be a special or general purpose programmable processor, operable to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user, for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a Local Area Network (LAN), a Wide Area Network (WAN), and the Internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
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