Resource display method, device, electronic device and storage medium

Abstract

The present disclosure provides a resource display method, apparatus, electronic device, and storage medium, relating to the fields of artificial intelligence technology, particularly large models, deep learning, big data, and intelligent recommendation. A specific implementation scheme comprises: determining a target period that matches a candidate resource from at least two candidate periods; determining an object's period preference value for the target period; determining an object's resource preference value for the candidate resource based on the candidate resource's target resource characteristics and the object's target object characteristics; determining a display state for the candidate resource based on the resource preference value and the period preference value; and displaying the resource based on the display state.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, in particular to the fields of large models, deep learning, big data, intelligent recommendation, etc. More specifically, the present disclosure provides a resource display method, device, electronic device, storage medium and computer program product. Background Art

[0002] Recommendation systems can recommend resources to users based on their browsing history. However, current recommendation systems primarily recommend resources based on users' short-term interests, and the accuracy of recommendations needs to be improved. Summary of the Invention

[0003] The present disclosure provides a resource display method, device, electronic device, storage medium, and computer program product.

[0004] According to one aspect of the present disclosure, a resource display method is provided, comprising: determining a target period that matches a candidate resource from at least two candidate periods; determining a period preference value of an object for the target period; determining a resource preference value of the object for the candidate resource based on target resource characteristics of the candidate resource and target object characteristics of the object; determining a display status of the candidate resource based on the resource preference value and the period preference value; and displaying the resource based on the display status.

[0005] According to another aspect of the present disclosure, a resource display device is provided, comprising: a target period determination module, a first determination module, a second determination module, a state determination module, and a display module. The target period determination module is used to determine a target period that matches a candidate resource from at least two candidate periods. The first determination module is used to determine the period preference value of an object for the target period. The second determination module is used to determine the resource preference value of an object for the candidate resource based on the target resource characteristics of the candidate resource and the target object characteristics of the object. The state determination module is used to determine the display state of the candidate resource based on the resource preference value and the period preference value; and the display module is used to display the resource based on the display state.

[0006] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method provided by the present disclosure.

[0007] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method provided by the present disclosure.

[0008] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements the method provided in the present disclosure when executed by a processor.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0011] Figure 1 is a schematic diagram of an application scenario of the resource display method and device according to an embodiment of the present disclosure;

[0012] Figure 2 is a schematic flow chart of a resource display method according to an embodiment of the present disclosure;

[0013] Figure 3 is a schematic diagram of a resource display method according to an embodiment of the present disclosure;

[0014] Figure 4 is a schematic structural block diagram of a resource display device according to an embodiment of the present disclosure; and

[0015] Figure 5 It is a structural block diagram of an electronic device used to implement the resource display method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0016] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0017] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0018] In the technical solution disclosed herein, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0019] In some embodiments, the recommendation system primarily uses short-term (e.g., one month) user behavior data to estimate the user's interest in candidate resources and then determines which resources to recommend to the user based on that interest. The recommendation system lacks long-term (e.g., one year) user behavior data when making resource recommendations.

[0020] In real-world applications, for example, a user may have preferred comedy resources over the past year, but in the past month, they've become more interested in and browsed a lot of food resources. Because the recommendation system primarily relies on short-term behavioral data, it often recommends food resources to the user, but rarely recommends martial arts resources. However, users are not completely uninterested in martial arts resources, so this recommendation method is less accurate and results in a poor user experience.

[0021] The disclosed embodiments aim to provide a resource display method, which uses a period preference value to influence whether candidate resources are displayed, so that resources that meet the user's interests at different periods have a certain display rate, avoids recommending only resources that are of short-term interest to the object, and realizes the regulation of resources that meet the object's interests at different periods, thereby improving the accuracy of recommendations and thus enhancing the user experience.

[0022] The technical solution provided by the embodiments of the present disclosure can be applied to resource recommendation scenarios such as videos. The technical solution provided by the present disclosure will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Figure 1 Schematic diagram of an application scenario of the resource display method and device according to an embodiment of the present disclosure.

[0024] It should be noted that Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments or scenarios.

[0025] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0026] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Terminal devices 101, 102, and 103 can be various electronic devices with display screens and support web browsing, including but not limited to smartphones, tablet computers, laptop computers, and desktop computers, etc.

[0027] Server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using terminal devices 101, 102, and 103. The background management server may analyze and process received data such as user requests, and provide feedback (e.g., resources to be displayed based on user attributes) to the terminal device.

[0028] It should be noted that the resource display method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the resource display device provided in the embodiment of the present disclosure can generally be set in the server 105. The resource display method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the resource display device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0029] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0030] Figure 2 is a schematic flow chart of a resource display method according to an embodiment of the present disclosure.

[0031] like Figure 2 As shown, the resource display method 200 may include operations S210 to S240.

[0032] In operation S210 , a target period matching a candidate resource is determined from at least two candidate periods.

[0033] For example, a subject's interests can be categorized into short-term and long-term, or short-term, medium-term, and long-term, based on their length. For example, long-term can mean more than one year, short-term can mean less than seven days, and medium-term can mean between seven days and one year. For example, if a subject has consistently browsed comedy resources over the past two years, has browsed more martial arts resources over the past six months, and has browsed more food resources over the past week, then comedy is the subject's long-term interest, martial arts is the subject's medium-term interest, and food is the subject's short-term interest. The subject can be a user.

[0034] For example, the similarity between resources and candidate resources in each candidate period can be determined, and based on the similarity, a target period can be determined to match the candidate resources. For example, a first similarity can be calculated between resources that match the subject's long-term interests and the candidate resources, a second similarity can be calculated between resources that match the subject's medium-term interests and the candidate resources, and a third similarity can be calculated between resources that match the subject's short-term interests and the candidate resources. The first, second, and third similarities are then compared, and the candidate period corresponding to the greatest similarity is selected as the target period.

[0035] In operation S220 , a period preference value of the subject for the target period is determined.

[0036] For example, the period preference values ​​of each candidate period may be pre-configured. For example, the period preference values ​​of long-term, medium-term, and short-term interests may be pre-configured to be 0.2, 0.3, and 0.5, respectively.

[0037] For another example, the period preference value of the target period may be determined according to the target resource characteristics of the candidate resource and the target object characteristics of the object. This determination method will be described in detail below.

[0038] In operation S230 , a resource preference value of the object for the candidate resource is determined based on the target resource feature of the candidate resource and the target object feature of the object.

[0039] It should be noted that users are aware of and agree to the acquisition and use of target object characteristics, and that this is in compliance with relevant laws and regulations and does not violate public order and good morals.

[0040] For example, the target resource features and target object features can be input into a pre-trained deep learning model, and the deep learning model can determine the resource preference value. The higher the resource preference value, the higher the object's satisfaction with the candidate resource.

[0041] In operation S240, the display status of the candidate resource is determined according to the resource preference value and the period preference value.

[0042] In operation S250 , resources are displayed based on the display status.

[0043] For example, if the resource preference value and the period preference value are higher than corresponding thresholds, the display state is determined to be display, and normal display is performed. Otherwise, the display state is determined to be non-display, and no display is performed.

[0044] For another example, an evaluation value may be determined based on the resource preference value and the period preference value. If the evaluation value is higher than a corresponding threshold, the display state is determined to be display; otherwise, the display state is determined to be non-display.

[0045] According to the resource display method provided by the embodiment of the present disclosure, the method stratifies the interests of an object, for example, dividing the object's interests into long-term interests, medium-term interests, and short-term interests, then determines a target period that matches the candidate resources, then determines the object's period preference value for the target period, and determines the display status of the candidate resources based on the resource preference value and the period preference value. In this way, the display status of the candidate resources can be influenced by the period preference value. For example, when the period preference value of the target period is high, the possibility of displaying the candidate resources can be increased. When the period preference value of the target period is low, the possibility of displaying the candidate resources is reduced. Therefore, the resources displayed to the user can be made to match the user's preferences for interests in different periods, thereby improving the user experience. In addition, when multiple candidate resources cover multiple periods, the display status of the candidate resources is influenced by the period preference value of each candidate resource. In this way, during the actual display process, the candidate resources of each period have a certain display rate, avoiding recommending only resources of short-term interest to the object, thereby improving the accuracy of the recommendation and thus improving the user experience.

[0046] In some embodiments, the object characteristics of the object or the target resource characteristics of the candidate resource can be determined by the candidate resource set. Next, the candidate resource set and the process of determining the candidate resource set are described.

[0047] Exemplarily, there are at least two candidate resource sets, each candidate resource set is associated with a candidate period, and the candidate periods associated with the candidate resource sets are different from each other. In addition, the resources in the candidate resource sets meet the satisfactory interaction condition and are in the candidate period associated with the historical interaction time.

[0048] For example, the candidate resource set associated with the object's long-term interests is called a long-term interest satisfactory resource set. The resources in the long-term interest satisfactory resource set are, for example, resources that the user has interacted with in the past year, and all resources meet the satisfactory interaction conditions. The candidate resource set associated with the object's medium-term interests is called a medium-term interest satisfactory resource set. The resources in the medium-term interest satisfactory resource set are, for example, resources that the user has interacted with in the past six months, and all resources meet the satisfactory interaction conditions. The candidate resource set associated with the object's short-term interests is called a short-term interest satisfactory resource set. The resources in the short-term interest satisfactory resource set are, for example, resources that the user has interacted with in the past week, and all resources meet the satisfactory interaction conditions.

[0049] When determining the candidate resource set associated with the candidate period, resources that have interacted with the object during the associated candidate period can be grouped according to resource category to obtain multiple resource groups. For each resource group, the number of resources in the resource group that meet the satisfactory interaction criteria and the total number of interactions between each resource in the resource group and the object are used to determine whether the resource group meets the interest criteria. If the resource group meets the interest criteria, it indicates that the object is interested in the resource group. The resources in the resource group that meet the satisfactory interaction criteria can then be added to the candidate resource set associated with the candidate period.

[0050] Taking the long-term interest satisfaction resource set as an example, you can first filter out several resources that the subject has interacted with within a year from the database, and then group them according to the resource category, for example, one resource group is martial arts resources, and another resource group is comedy resources. Then determine whether the subject is satisfied with the resource group. If satisfied, add the resources that meet the satisfactory interaction conditions to the long-term interest satisfaction resource set. The method for determining the medium-term interest satisfaction resource set and the short-term interest satisfaction resource set is similar to that of the long-term interest satisfaction resource set. The difference lies in the time range of the several resources used. For example, in the process of determining the medium-term interest satisfaction resource set, you can filter out several resources that the subject has interacted with within six months. In the process of determining the short-term interest satisfaction resource set, you can filter out several resources that the subject has interacted with within a week.

[0051] This embodiment groups the resources that the object interacts with in different time periods by category, then determines the resource group that the user is interested in, and filters the resources that the object is satisfied with from the resource group of interest and adds them to the corresponding candidate resource set. This can accurately determine the object's interest preferences at different time scales, thereby constructing a candidate resource set that meets the object's preferences, improving the accuracy of resource recommendations and enhancing the user experience.

[0052] According to another embodiment of the present disclosure, whether a resource group satisfies an interest condition can be determined in the following manner: if the ratio of the number of resources in a resource group that meet the satisfactory interaction condition to the total number of resources in the resource group is greater than or equal to a first ratio threshold, the resource group can be determined to meet the interest condition. As can be seen, in this embodiment, if the subject is relatively satisfied with most resources in the resource group, the subject can be considered interested in the resource group.

[0053] According to another embodiment of the present disclosure, whether a resource group meets the interest condition can be determined in the following manner: if the ratio between the number of resources that meet the satisfactory interaction condition and the total number of resources in the resource group in a certain resource group is greater than or equal to a first ratio threshold, and the total number of interactions is greater than or equal to a number threshold, then it can be determined that the resource group meets the interest condition. It should be noted that the number of resources in some resource groups is relatively small. For example, a certain resource group is an animation resource group. The resource group only includes 3 resources. The object has completed broadcasts or other positive behaviors for these two resources. At the same time, the number of interactions between the object and the resources in the resource group is 3. Although the object has a relatively high satisfaction rate with the resources in the animation resource group, due to the small number of interactions, the confidence level of the object's interest in the resource group is low, and the resource group is not suitable as a resource group of interest to the object. In this embodiment, the object is determined to be interested in the resource group only when the object is relatively satisfied with most resources in the resource group and the number of interactions between the object and the resources in the resource group is large. Therefore, it is possible to more accurately determine whether the object is interested in the resource group, avoid mistakenly determining a resource group with low confidence as the resource group of interest, and thus improve the accuracy of determining the candidate resource group based on the resource group of interest.

[0054] In one example, a satisfactory interaction condition includes: a ratio between the viewing time of a resource and the total viewing time of the resource is greater than or equal to a second ratio threshold. For example, in the case of a video resource, a higher ratio of the viewing time of the resource to the total viewing time indicates that the resource is closer to completion, and the subject can be considered to be relatively satisfied with the resource.

[0055] In another example, a satisfactory interaction condition includes: the browsing time of the resource is greater than or equal to a time threshold. For example, if the subject spends a long time browsing the resource, it can be considered that the subject is relatively satisfied with the resource.

[0056] In another example, a satisfactory interaction condition includes: the object inputs a predetermined category of operation data for a resource, and the predetermined category of operation data includes, for example, likes, favorites, shares, etc. If the object performs this type of operation on the resource, it can be considered that the object is relatively satisfied with the resource.

[0057] The candidate resource set and the process of determining the candidate resource set are described above.

[0058] Next, the process of determining the target object features of an object is described.

[0059] In one example, the attribute characteristics of an object can be used as the target object characteristics. The attribute characteristics may include, for example, age, region, resource tags of interest, etc. It should be noted that the acquisition and use of information such as age, region, and tags are known and agreed to by the user, and are in compliance with relevant laws and regulations and do not violate public order and good customs.

[0060] In another example, the target object characteristics can be determined based on the attribute characteristics of the object and the characteristics of each resource in at least two candidate resource sets. For example, the attribute characteristics of the object, the long-term interest satisfaction resource set, the medium-term interest satisfaction resource set, and the short-term interest satisfaction resource set can be input into a pre-trained encoder to obtain the target object characteristics. In this embodiment, the target object characteristics are determined from two aspects: on the one hand, the basic attributes of the object, and on the other hand, the object preferences reflected by historically satisfactory resources. In this way, by comprehensively considering the basic attributes of the user and the interest preferences in different time dimensions, the target object characteristics can be more accurately portrayed, and then whether to display the candidate resources can be more accurately determined, thereby improving the accuracy of the recommendation, making the recommended resources more in line with the actual needs of the object, and improving the object's satisfaction with the recommended resources.

[0061] The above describes the process of determining the target object features of an object.

[0062] Next, the process of determining target resource characteristics of candidate resources is described.

[0063] In one example, the features of the candidate resources may be used as the target resource features. For example, feature extraction may be performed based on the category, content, etc. of the candidate resources to obtain the features of the candidate resources.

[0064] In another example, a target resource set can be determined from at least two candidate resource sets based on the target period, and the resources in the target resource set meet the satisfactory interaction conditions and are in the target period with the historical interaction time. Then, the target resource characteristics are determined based on the characteristics of the candidate resources and the characteristics of each resource in the target resource set. It should be noted that in the process of determining whether the object is satisfied with the candidate resources, some resources related to the candidate resources can be screened first. In this embodiment, the screened related resources refer to the target resource set that is consistent with the candidate resource period. The object's satisfaction with these resources is equivalent to a posteriori, which can provide a reference for the resource preference value of the candidate resources. In this way, the object's resource preference value for the candidate resources can be determined more accurately based on the posteriori.

[0065] In the process of determining the characteristics of the target resource, for example, the target expert network can be determined from multiple candidate expert networks based on the target period, and then the characteristics of the candidate resources and the characteristics of each resource in the target resource set are input into the target expert network to obtain the characteristics of the target resource. For example, when the target period is long-term, the target resource set is a long-term interest satisfaction resource set, and the target expert network is a long-term expert network that is good at processing long-term interests. Compared with a general neural network to process the characteristics of the candidate resources and the characteristics of each resource in the target resource set, this embodiment sets up multiple candidate expert networks, and selects the corresponding target expert network from the multiple candidate expert networks according to different target periods, and then uses the target expert network to process the data. This embodiment can use a target expert network that is more adept at processing the interest characteristics of the period according to different periods such as long-term, medium-term or short-term, to improve the professionalism of determining the characteristics of the target resource in long-term, medium-term or short-term interests, and then more finely characterize the characteristics of the target resource, so that the recommendation results are more in line with the needs of the object, and the accuracy of resource recommendation is improved.

[0066] According to another embodiment of the present disclosure, the process of determining the display status of a candidate resource based on the resource preference value and the period preference value may include: determining a target value for the candidate resource based on the resource preference value and the period preference value, and then determining the display status based on the target value. This embodiment uses the resource preference value and the period preference value to determine the target value for the candidate resource, thereby accurately determining whether to display the candidate resource and improving the subject's satisfaction with the recommendation results.

[0067] In one example, if the period preference value is greater than a first threshold and less than a second threshold, the initial value of the candidate resource is adjusted based on the resource preference value and the period preference value to obtain a target value. For example, the product of the initial value, the resource preference value, and the period preference value can be determined as the target value.

[0068] In one example, if the period preference value is greater than or equal to a second threshold, the initial value is adjusted based on the resource preference value and the period preference value after weighting to obtain a target value; the period preference value after weighting is greater than the period preference value. For example, the period preference value can be increased by a predetermined ratio to obtain the weighted period preference value, or the sum of the period preference value and the first predetermined value can be used as the weighted period preference value. For example, if the period preference value is 0.8, the weighted period preference value after weighting is 0.88. The product of the initial value, the resource preference value, and the weighted period preference value can then be used as the target value. In this embodiment, if the period preference value for the target period is larger, it indicates that the subject is more likely to browse resources that match the interests of the target period. Therefore, the period preference value for the target period can be appropriately increased, thereby increasing the target value of candidate resources that match the interests of the target period, thereby increasing the probability that candidate resources that match the interests of the target period will be presented to the subject.

[0069] In another example, if the period preference value is less than or equal to a first threshold, the initial value is adjusted based on the resource preference value and the period preference value after weighting to obtain a target value; the period preference value after weighting is less than the period preference value. For example, the period preference value can be reduced by a predetermined ratio to obtain the period preference value after weighting, or the difference between the period preference value and a second predetermined value can be used as the period preference value after weighting. For example, if the period preference value is 0.15, the period preference value after weighting is 0.1. The product of the initial value, the resource preference value, and the period preference value after weighting can then be used as the target value. In this embodiment, if the period preference value for the target period is low, it indicates that the subject has a low intention to browse resources that match the interests of the target period. Therefore, the period preference value for the target period can be appropriately reduced, thereby lowering the target value for resources that match the interests of the target period. This can reduce the likelihood that resources that match the interests of the target period will be recommended to the subject.

[0070] It is understandable that in other examples, the period preference value may not be weighted up or down based on the size of the period preference value.

[0071] It is understood that there can be multiple candidate resources, and the target value of each candidate resource can be determined in the above manner. The multiple candidate resources can then be sorted according to their respective target values. Among the multiple candidate resources, the candidate resources whose order satisfies the predetermined order condition are then displayed. For example, the candidate resource ranked first or in the first several order can be displayed to the object, thereby preferentially displaying candidate resources with higher target values ​​to the object, thereby improving the user experience.

[0072] Figure 3 It is a schematic diagram of the resource display method according to an embodiment of the present disclosure.

[0073] In this embodiment, the model may include a routing network Router, multiple candidate expert networks Expert, an encoder Encoder, a first deep learning sub-model DNN1 and a second deep learning sub-model DNN2.

[0074] The target resource feature F_1 of the candidate resource can be determined first. For example, the feature F_3 of the candidate resource can be input into the routing network Router, which determines the correlation between the candidate resource and each candidate period and determines the candidate period with the highest correlation as the target period.

[0075] The candidate expert network Expert includes, for example, a long-term expert network Expert1, a medium-term expert network Expert2, and a short-term expert network Expert3. The candidate period corresponds one-to-one to the candidate expert network Expert. After determining the target period, the candidate expert network corresponding to the target period can be determined as the target expert network and activated. The candidate period also corresponds one-to-one to the candidate resource set. After determining the target period, the candidate resource set associated with the target period can also be determined as the target resource set. For example, if the candidate resource has the highest correlation with the long-term interest of the object, the long-term interest satisfaction resource set can be determined as the target resource set, and the long-term expert network Expert1 can be activated. Next, the feature F_3 of the candidate resource and the feature F_4 of each resource in the long-term interest satisfaction resource set can be spliced ​​and input into the long-term expert network Expert1 to obtain the target resource feature F_1 of the candidate resource.

[0076] The target object feature F_2 of the object can be determined. For example, the attribute feature Xuser of the object and the features of each resource in each candidate resource set can be input into the encoder Encoder to obtain the target object feature F_2. During the processing, each candidate resource set includes, for example, a long-term interest satisfaction resource set, a medium-term interest satisfaction resource set, and a long-term interest satisfaction resource set. The three candidate resource sets have a total of m resources, namely nid1~nidm. The period marking token includes long-term bias, medium-term bias, and short-term bias. All three are pre-set tokens used to mark the period in which the resource is located. Each resource corresponds to a period marking token, so there are a total of m period marking tokens. In addition, the period marking tokens corresponding to the resources of the same candidate period are the same, for example, the long-term bias corresponding to the resources of long-term interest are the same. Xseq is a pre-set sequence token. After processing by the encoder, the hidden features Hseq of the sequence can be obtained, the hidden features H1~Hm of each resource can be obtained, and the hidden features Huser of the object can be obtained. Among them, the hidden features Hseq of the sequence have a global vision, and the global vision of other hidden layer features is limited. Therefore, the hidden features Hseq of the sequence can be used as the target object features F_2 of the object, or other network structures can be used to map the hidden features Hseq of the sequence to the target object features F_2 of the object.

[0077] Next, the target resource feature F_1 of the candidate resource and the target object feature F_2 of the object can be input into the first deep learning sub-model DNN1 to obtain the resource preference value V_p1.

[0078] The target resource feature F_1 of the candidate resource and the target object feature F_2 of the object can be input into the second deep learning sub-model DNN2 to obtain the preference ratios for each of the multiple candidate time periods. The preference ratio for the target time period, among the multiple preference ratios, can be determined as the time period preference value V_p2. It should be noted that the second deep learning sub-model DNN2 can output the subject's preference ratios for long-term, medium-term, and short-term interests. However, when the target time period is long-term, only the preference ratio for long-term interests can be used subsequently, without using the other two preference ratios. The reason for determining the preference ratios for long-term, medium-term, and short-term interests through the second deep learning sub-model DNN2 is that different candidate resources may involve different time periods. Therefore, it is necessary to model the preference ratios for long-term, medium-term, and short-term interests, rather than just the preference ratio for a single time period. It should be noted that the subject's preferences for long-term, medium-term, and short-term interests change dynamically with their browsing behavior, rather than being fixed. For example, after continuously browsing multiple resources with short-term interests, a subject may no longer want to view resources with short-term interests. This embodiment determines the period preference value V_p2 of the target period based on the feature F_3 of the candidate resource. Compared with the method of pre-configuring the period preference value for each candidate period, this embodiment can dynamically adjust the period preference value V_p2, thereby improving the accuracy of recommendation.

[0079] After obtaining the resource preference value V_p1 and the period preference value V_p2, the target value V_tar of the candidate resource can be determined. For example, an initial value V_ini can be obtained first. This embodiment does not limit the method for determining the initial value V_ini. The initial value V_ini, the resource preference value V_p1, and the period preference value V_p2 are then multiplied, weighted, or calculated in some other manner, and the result is used as the target value V_tar.

[0080] Next, whether to display the candidate resources can be determined based on the target value V_tar. For example, multiple candidate resources can be sorted based on their respective target values ​​V_tar, and then the first or first several candidate resources can be displayed to the object.

[0081] It should be noted that the above-mentioned model is pre-trained, and the training process is similar to the inference process. The difference is that the training samples correspond to labels, and the labels may include a first label representing the true resource preference value and a second label representing the true period preference value. During the training process, after obtaining the resource preference value and the period preference value output by the model, the first loss value can be calculated based on the first loss function according to the difference between the resource preference value and the first label. The first loss function can be the cross entropy loss. The second loss value can also be calculated based on the second loss function according to the difference between the period preference value and the second label. The second loss function can be the KL divergence. Then, the total loss value can be determined based on the first loss value and the second loss value, and the parameters of the routing network Router, multiple candidate expert networks Expert, encoder Encoder, the first deep learning sub-model DNN1 and the second deep learning sub-model DNN2 can be adjusted based on the total loss value until the model converges.

[0082] Figure 4 It is a schematic structural block diagram of a resource display device according to an embodiment of the present disclosure.

[0083] like Figure 4 As shown, the resource display device 400 may include a target period determination module 410 , a first determination module 420 , a second determination module 430 , a state determination module 440 and a display module 450 .

[0084] The target period determination module 410 is configured to determine a target period that matches a candidate resource from at least two candidate periods.

[0085] The first determination module 420 is used to determine the subject's period preference value for the target period.

[0086] The second determining module 430 is configured to determine the resource preference value of the object for the candidate resource according to the target resource feature of the candidate resource and the target object feature of the object.

[0087] The status determination module 440 is used to determine the display status of the candidate resource according to the resource preference value and the period preference value.

[0088] The display module 450 is used to display resources based on the display status.

[0089] According to another embodiment of the present disclosure, the state determination module includes a target value determination submodule and a state determination submodule. The target value determination submodule is configured to determine a target value for a candidate resource based on a resource preference value and a period preference value. The state determination submodule is configured to determine a display state based on the target value.

[0090] According to another embodiment of the present disclosure, the target value determination submodule includes: a first adjustment unit, a second adjustment unit, and a third adjustment unit. The first adjustment unit is used to adjust the initial value of the candidate resource according to the resource preference value and the period preference value in response to detecting that the period preference value is greater than the first threshold and less than the second threshold, to obtain the target value. The second adjustment unit is used to adjust the initial value according to the resource preference value and the period preference value after the weighting process in response to detecting that the period preference value is greater than or equal to the second threshold, to obtain the target value; wherein the period preference value after the weighting process is greater than the period preference value. The third adjustment unit is used to adjust the initial value according to the resource preference value and the period preference value after the weighting process in response to detecting that the period preference value is less than or equal to the first threshold, to obtain the target value; wherein the period preference value after the weighting process is less than the period preference value.

[0091] According to another embodiment of the present disclosure, target resource characteristics are obtained by the following modules: a target resource set determination module and a target resource characteristic determination module. The target resource set determination module is configured to determine a target resource set from at least two candidate resource sets based on a target period. The target resource characteristic determination module is configured to determine the target resource characteristics based on the characteristics of the candidate resources and the characteristics of each resource in the target resource set; wherein the resources in the target resource set meet satisfactory interaction conditions and are within the target period relative to their historical interaction times.

[0092] According to another embodiment of the present disclosure, a target resource feature determination module includes an expert network determination submodule and a target resource feature determination submodule. The expert network determination submodule is configured to determine a target expert network from a plurality of candidate expert networks based on a target period. The target resource feature determination submodule is configured to input the features of the candidate resources and the features of each resource in the target resource set into the target expert network to obtain target resource features.

[0093] According to another embodiment of the present disclosure, the target object characteristics are obtained through the following modules: a target object characteristic determination module, which is used to determine the target object characteristics based on the attribute characteristics of the object and the characteristics of each resource in at least two candidate resource sets; wherein each candidate resource set is associated with a candidate period, and at least two candidate periods associated with at least two candidate resource sets are different from each other; the resources in the candidate resource sets meet the satisfactory interaction conditions and are in the candidate period associated with the historical interaction time.

[0094] According to another embodiment of the present disclosure, the candidate resource set associated with the candidate period is determined by the following modules: a grouping module, a determination module, and an adding module. The grouping module is used to group the resources that have interacted with the object and whose interaction time is in the associated candidate period according to the category of the resources to obtain multiple resource groups. The determination module is used to determine, for each resource group, whether the resource group meets the interest condition based on the number of resources in the resource group that meet the satisfactory interaction condition and the total number of interactions between each resource in the resource group and the object. The adding module is used to add the resources in the resource group that meet the satisfactory interaction condition to the candidate resource set associated with the candidate period in response to determining that the resource group meets the interest condition.

[0095] According to another embodiment of the present disclosure, the device also includes a determination module for determining that the resource group meets the interest condition in response to detecting that the ratio between the number of resources that meet the satisfactory interaction condition and the total number of resources in the resource group is greater than or equal to a first ratio threshold, and the total number of interactions is greater than or equal to a number threshold.

[0096] According to another embodiment of the present disclosure, the satisfactory interaction conditions include at least one of the following: the ratio between the browsing time of the resource and the total browsing time of the resource is greater than or equal to a second ratio threshold; the browsing time of the resource is greater than or equal to the time threshold; the object inputs a predetermined category of operation data for the resource.

[0097] According to another embodiment of the present disclosure, the first determination module includes a preference ratio determination submodule and a period preference value determination submodule. The preference ratio determination submodule is configured to determine the preference ratios of multiple candidate periods based on target resource characteristics and target object characteristics. The period preference value determination submodule is configured to determine the preference ratio of the target period from the multiple preference ratios as the period preference value.

[0098] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, including at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned resource display method.

[0099] According to an embodiment of the present disclosure, the present disclosure further provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the above-mentioned resource display method.

[0100] According to an embodiment of the present disclosure, the present disclosure further provides a computer program product, including a computer program, which implements the above-mentioned resource display method when executed by a processor.

[0101] Figure 5is a block diagram of an electronic device used to implement the resource display method of an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0102] like Figure 5 As shown, device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. RAM 503 may also store various programs and data required for the operation of device 500. Computing unit 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to bus 504.

[0103] Various components in device 500 are connected to I / O interface 505, including: an input unit 506, such as a keyboard, mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, optical disk, etc.; and a communication unit 509, such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows device 500 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0104] The computing unit 501 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 501 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 501 performs the various methods and processes described above, such as the resource display method. For example, in some embodiments, the resource display method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the resource display method described above can be performed. Alternatively, in other embodiments, the computing unit 501 can be configured to perform the resource display method in any other suitable manner (e.g., via firmware).

[0105] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A 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 machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, 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.

[0110] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0111] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0112] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A resource display method, comprising: Determine a target period that matches the candidate resource from at least two candidate periods; determining a period preference value of the subject for the target period; determining a resource preference value of the object for the candidate resource according to a target resource feature of the candidate resource and a target object feature of the object; Determining a target value includes: in response to detecting that the period preference value is greater than a first threshold and less than a second threshold, adjusting the initial value of the candidate resource according to the resource preference value and the period preference value to obtain the target value; in response to detecting that the period preference value is greater than or equal to the second threshold, adjusting the initial value according to the resource preference value and the period preference value after weighting processing to obtain the target value, wherein the period preference value after weighting processing is greater than the period preference value; in response to detecting that the period preference value is less than or equal to the first threshold, adjusting the initial value according to the resource preference value and the period preference value after weighting processing to obtain the target value, wherein the period preference value after weighting processing is less than the period preference value; Determining a display status of the candidate resource according to the target value; The resource is displayed based on the display status.

2. The method according to claim 1, wherein The target resource characteristics are obtained by the following method: determining a target resource set from at least two candidate resource sets according to the target period; Determining the target resource characteristics according to the characteristics of the candidate resources and the characteristics of each resource in the target resource set; The resources in the target resource set meet satisfactory interaction conditions and are within the target period in terms of historical interaction time.

3. The method according to claim 2, wherein: The determining the target resource characteristics according to the characteristics of the candidate resources and the characteristics of each resource in the target resource set includes: determining a target expert network from a plurality of candidate expert networks according to the target period; The characteristics of the candidate resources and the characteristics of each resource in the target resource set are input into the target expert network to obtain the characteristics of the target resource.

4. The method according to claim 1, wherein The target object features are obtained by the following method: Determining the target object characteristics based on the attribute characteristics of the object and the characteristics of each resource in at least two candidate resource sets; Each candidate resource set is associated with a candidate period, and at least two candidate periods associated with at least two candidate resource sets are different from each other; the resources in the candidate resource sets meet satisfactory interaction conditions and are in the candidate period associated with the historical interaction time.

5. The method according to any one of claims 2 to 4, wherein: The candidate resource set associated with the candidate period is determined by the following method: Grouping the resources that the object has interacted with and whose interaction time is within the associated candidate period according to resource categories to obtain multiple resource groups; For each resource group, determining whether the resource group meets the interest condition according to the number of resources in the resource group that meet the satisfactory interaction condition and the total number of interactions between each resource in the resource group and the object; In response to determining that the resource group satisfies the interest condition, resources in the resource group that meet the satisfactory interaction condition are added to the candidate resource set associated with the candidate period.

6. The method according to claim 5, wherein: The method further comprises: In response to detecting that the ratio between the number of resources that meet the satisfactory interaction condition and the total number of resources in the resource group is greater than or equal to a first ratio threshold, and the total number of interactions is greater than or equal to a number threshold, it is determined that the resource group meets the interest condition.

7. The method according to any one of claims 2 to 4, wherein: The satisfactory interaction condition includes at least one of the following: The ratio of the browsing time of the resource to the total browsing time of the resource is greater than or equal to a second ratio threshold; The browsing time of the resource is greater than or equal to the time threshold; The object enters a predetermined category of operational data for the resource.

8. The method according to claim 5, wherein The satisfactory interaction condition includes at least one of the following: The ratio of the browsing time of the resource to the total browsing time of the resource is greater than or equal to a second ratio threshold; The browsing time of the resource is greater than or equal to the time threshold; The object enters a predetermined category of operational data for the resource.

9. The method according to claim 6, wherein: The satisfactory interaction condition includes at least one of the following: The ratio of the browsing time of the resource to the total browsing time of the resource is greater than or equal to a second ratio threshold; The browsing time of the resource is greater than or equal to the time threshold; The object enters a predetermined category of operational data for the resource.

10. The method according to claim 1, wherein The determining of the subject's period preference value for the target period includes: Determining the preference ratios of the plurality of candidate periods according to the target resource characteristics and the target object characteristics; The preference ratio of the target period among the plurality of preference ratios is determined as the period preference value.

11. A resource display device, comprising: a target period determination module, configured to determine a target period that matches the candidate resource from at least two candidate periods; a first determining module, configured to determine the subject's period preference value for the target period; a second determining module, configured to determine a resource preference value of the object for the candidate resource based on a target resource feature of the candidate resource and a target object feature of the object; A target value determination submodule is used to determine the target value; A status determination submodule, configured to determine the display status of the candidate resource according to the target value; A display module, configured to display resources based on the display status; Wherein, the target value determination submodule includes: a first adjustment unit for adjusting the initial value of the candidate resource according to the resource preference value and the period preference value to obtain the target value in response to detecting that the period preference value is greater than a first threshold and less than a second threshold; a second adjusting unit configured to, in response to detecting that the period preference value is greater than or equal to the second threshold, adjust the initial value according to the resource preference value and the period preference value after the weighting process to obtain the target value; wherein the period preference value after the weighting process is greater than the period preference value; a third adjustment unit, configured to adjust the initial value according to the resource preference value and the period preference value after demotion, to obtain the target value in response to detecting that the period preference value is less than or equal to the first threshold; wherein the period preference value after demotion is less than the period preference value.

12. The device according to claim 11, wherein The target resource characteristics are obtained through the following modules: a target resource set determining module, configured to determine a target resource set from at least two candidate resource sets according to the target period; a target resource feature determination module, configured to determine the target resource feature based on the feature of the candidate resource and the feature of each resource in the target resource set; The resources in the target resource set meet satisfactory interaction conditions and are within the target period in terms of historical interaction time.

13. The device according to claim 12, wherein The target resource feature determination module includes: an expert network determination submodule, configured to determine a target expert network from a plurality of candidate expert networks according to the target period; The target resource feature determination submodule is used to input the features of the candidate resources and the features of each resource in the target resource set into the target expert network to obtain the target resource features.

14. The device according to claim 11, wherein The target object features are obtained through the following modules: a target object feature determination module, configured to determine the target object feature based on the attribute features of the object and features of each resource in at least two candidate resource sets; Each candidate resource set is associated with a candidate period, and at least two candidate periods associated with at least two candidate resource sets are different from each other; the resources in the candidate resource sets meet satisfactory interaction conditions and are in the candidate period associated with the historical interaction time.

15. The device according to any one of claims 12 to 14, wherein The candidate resource set associated with the candidate period is determined by the following modules: a grouping module, configured to group resources that the object has interacted with and whose interaction time is within the associated candidate period according to resource categories to obtain a plurality of resource groups; a determination module, configured to determine, for each resource group, whether the resource group meets the interest condition based on the number of resources in the resource group that meet the satisfactory interaction condition and the total number of interactions between each resource in the resource group and the object; An adding module is configured to, in response to determining that the resource group satisfies the interest condition, add the resources in the resource group that meet the satisfactory interaction condition to the candidate resource set associated with the candidate period.

16. The apparatus according to claim 15, further comprising: A determination module is used to determine that the resource group meets the interest condition in response to detecting that the ratio between the number of resources that meet the satisfactory interaction condition and the total number of resources in the resource group is greater than or equal to a first ratio threshold, and the total number of interactions is greater than or equal to a number threshold.

17. The device according to any one of claims 12 to 14, wherein: The satisfactory interaction condition includes at least one of the following: The ratio of the browsing time of the resource to the total browsing time of the resource is greater than or equal to a second ratio threshold; The browsing time of the resource is greater than or equal to the time threshold; The object enters a predetermined category of operational data for the resource.

18. The device according to claim 15, wherein The satisfactory interaction condition includes at least one of the following: The ratio of the browsing time of the resource to the total browsing time of the resource is greater than or equal to a second ratio threshold; The browsing time of the resource is greater than or equal to the time threshold; The object enters a predetermined category of operational data for the resource.

19. The device according to claim 16, wherein The satisfactory interaction condition includes at least one of the following: The ratio of the browsing time of the resource to the total browsing time of the resource is greater than or equal to a second ratio threshold; The browsing time of the resource is greater than or equal to the time threshold; The object enters a predetermined category of operational data for the resource.

20. The device according to claim 11, wherein The first determining module includes: a preference ratio determination submodule, configured to determine the preference ratios of the plurality of candidate periods according to the target resource characteristics and the target object characteristics; The period preference value determination submodule is configured to determine the preference proportion of the target period among the plurality of preference proportions as the period preference value.

21. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 10.

22. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 10.

23. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 10.

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