CN113326436B - Method, device, electronic equipment and storage medium for determining recommended resources - Google Patents
Method, device, electronic equipment and storage medium for determining recommended resources Download PDFInfo
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Abstract
The disclosure discloses a method, a device, electronic equipment and a storage medium for determining recommended resources, which are applied to the technical field of artificial intelligence, and are particularly applied to the technical field of intelligent recommendation and the technical field of deep learning. The specific implementation scheme of the method for determining recommended resources is as follows: for each resource in the recalled plurality of resources, determining a predicted click rate of each resource for the target object by adopting a click rate prediction model; determining click rate weights of predicted click rates for the target objects based on the first position information of the target objects and the attribute information of each resource; determining the click rate of each resource for the target object based on the predicted click rate and the click rate weight; and determining recommended resources for the target object in the plurality of resources based on click rates of the plurality of resources for the target object.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence, and in particular, to the field of intelligent recommendation technology and deep learning technology, and more particularly, to a method, an apparatus, an electronic device, and a storage medium for determining recommended resources.
Background
With the development of network technology, intelligent recommendation technology is rapidly developed in order to provide users with information satisfying demands and points of interest. More common personalized recommendation algorithms are popular ranking list based recommendation algorithms, content based recommendation algorithms, social network based recommendation algorithms, and the like. The methods generally adopt a general coarse ordering model and a general fine ordering model, select the resources ranked in the first several bits from recalled resources, and recommend the resources to a user.
Disclosure of Invention
A method, an apparatus, an electronic device and a storage medium for determining recommended resources are provided, which improve recommendation accuracy and user experience.
According to one aspect of the present disclosure, there is provided a method of determining recommended resources, comprising: for each resource in the recalled plurality of resources, determining a predicted click rate of each resource for the target object by adopting a click rate prediction model; determining click rate weights of predicted click rates for the target objects based on the first position information of the target objects and the attribute information of each resource; determining the click rate of each resource for the target object based on the predicted click rate and the click rate weight; and determining recommended resources for the target object in the plurality of resources based on click rates of the plurality of resources for the target object.
According to another aspect of the present disclosure, there is provided an apparatus for determining recommended resources, including: the click rate prediction module is used for determining the predicted click rate of each resource aiming at the target object by adopting a click rate prediction model aiming at each resource in the recalled plurality of resources; the weight determining module is used for determining click rate weight of the predicted click rate for the target object based on the first position information of the target object and the attribute information of each resource; the click rate determining module is used for determining the click rate of each resource aiming at the target object based on the predicted click rate and the click rate weight; and a recommended resource determining module for determining recommended resources for the target object in the plurality of resources based on click rates of the plurality of resources for the target object.
According to another aspect of the present disclosure, there is provided an electronic device 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 method of determining recommended resources provided by the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of determining recommended resources provided by the present disclosure.
According to 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 determining recommended resources provided by the present disclosure.
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 an application scenario diagram of a method and apparatus for determining recommended resources according to an embodiment of the present disclosure;
FIG. 2 is a flow diagram of a method of determining recommended resources according to an embodiment of the disclosure;
FIG. 3 is a schematic diagram of determining click rate weights for predicted click rates for target objects in accordance with an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of determining an association value between click rate and matching location for each resource according to an embodiment of the present disclosure;
FIG. 5 is a block diagram of an apparatus for determining recommended resources according to an embodiment of the disclosure; and
FIG. 6 is a block diagram of an electronic device for implementing a method of determining recommended resources in an embodiment of the 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.
The present disclosure provides a method of determining recommended resources, including a click rate prediction stage, a weight determination stage, a click rate determination stage, and a recommended resource determination stage. In the click rate prediction stage, a click rate prediction model is adopted for each resource in the recalled plurality of resources to determine the predicted click rate of each resource for the target object. In the weight determination stage, click rate weights of the predicted click rates for the target objects are determined based on the first position information of the target objects and the attribute information of each resource. In the click rate determination phase, the click rate of each resource for the target object is determined based on the predicted click rate and the click rate weight. In the recommended resource determination phase, recommended resources for the target object in the plurality of resources are determined based on click rates of the plurality of resources for the target object.
An application scenario of the method and apparatus provided by the present disclosure will be described below with reference to fig. 1.
Fig. 1 is an application scenario diagram of a method and apparatus for determining recommended resources according to an embodiment of the present disclosure.
As shown in fig. 1, the application scenario 100 includes a terminal device 110, a server 120, and a database 140. Terminal device 110 may be communicatively coupled to server 120 via a network, which may include wired or wireless communication links. Server 120 may also access database 140 over a network, for example.
A user may interact with server 120 over a network, for example, using terminal device 110, to receive or send messages, etc. Terminal device 110 may be a terminal device with a display screen including, but not limited to, a smart phone, a tablet computer, a laptop portable computer, a desktop computer, and the like. For example, the terminal device 110 may transmit the request information 130 to the server 120 through a network to request multimedia resources such as audio, video, image, text, or any other resources.
The server 120 may, for example, recall the resources matching the request information 130 from the database 140 in response to the request information 130 sent by the terminal device 110, and feed back the matched resources as recommendation information 150 to the terminal device for presentation by the terminal device.
According to embodiments of the present disclosure, the server 120 may be a server providing various services, such as a background management server providing support for a website or client application browsed by a user using a terminal device. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
In an embodiment, after the server 120 recalls the resources from the database, the server 120 may also sort the recalled resources according to, for example, the matching degree between the recalled resources and the request information 130, and feed back a plurality of resources with the top sorting as the recommendation information 150 to the terminal device, so as to improve the accuracy of the recommendation information 150.
For example, the server 120 may determine the degree of matching based on the click rate prediction model 160, for example. The click rate estimation model 160 may be pre-trained by the server 120 or other electronic device communicatively coupled to the server 120.
It should be noted that the method for determining recommended resources provided in the present disclosure may be performed by the server 120. Accordingly, the apparatus for determining recommended resources provided by the present disclosure may be provided in the server 120.
It should be understood that the number and types of terminal devices, servers, and databases in fig. 1 are merely illustrative. There may be any number and type of terminal devices, servers, and databases as desired for implementation.
The method of determining recommended resources provided by the present disclosure will be described in detail below with reference to fig. 2 to 4.
FIG. 2 is a flow diagram of a method of determining recommended resources according to an embodiment of the disclosure.
As shown in fig. 2, the method 200 of determining recommended resources of this embodiment may include operations S210 to S240.
In operation S210, for each of the recalled plurality of resources, a click rate prediction model is employed to determine a predicted click rate for each resource for the target object.
According to the embodiment of the disclosure, the plurality of resources may be multimedia resources such as video, audio, images, text, etc., and may also be car rental service resources, home appliance maintenance service resources, etc. that provide convenience to life. The plurality of resources may be recalled in response to request information sent by the client application in the terminal device at the time of startup, or may be recalled in response to request information sent by the terminal device according to the query keyword. The types of the plurality of resources correspond to request information.
According to the embodiment of the disclosure, the resources marked by the keyword can be recalled from the database according to the keyword in the request information, and a plurality of recalled resources can be obtained. Alternatively, a plurality of resources with higher heat may be recalled from the database according to the heat of each resource in the database. Or, a batch of resources can be coarsely screened from massive information in the database according to the historical behavior data of the target object, and the resources are used as a plurality of recalled resources. It will be appreciated that the above-described methods of recalling multiple resources, opportunities for recalling multiple resources, and types of multiple resources are merely examples to facilitate an understanding of the present disclosure, and the present disclosure is not limited in this regard.
After recalling the plurality of resources, a click rate prediction model may be employed to determine a predicted click rate for each resource. The click rate estimation model may include, for example, a logistic regression model, a deep neural network model, or a model constructed based on an ensemble learning (Ensemble Learning) method. The model constructed based on the integrated learning method can comprise a Boosting model, a Bagging model and the like, and the integrated learning method is used for obtaining a click rate estimation model by training a plurality of classifiers and combining the classifiers, so that the click rate estimation model has higher precision. The embodiment can encode basic attribute, interest attribute, environment information and the like of the target object, encode the historical click rate of resources, the content of the resources and the like, take the information obtained after encoding as the input of a click rate estimation model, and output the information after processing the click rate estimation model to obtain the predicted click rate of each resource for the target object.
In operation S220, a click rate weight of the predicted click rate for the target object is determined based on the first location information of the target object and the attribute information of each resource.
According to an embodiment of the present disclosure, a similarity between the first location information and the attribute information of each resource may be determined, and the click rate weight may be determined according to the similarity. For example, a single-hot encoding method may be used to obtain a first encoding of the first location information and obtain a second encoding of the attribute information. The pearson correlation coefficient, cosine similarity, or jaccard similarity coefficient between the first code and the second code is then taken as the similarity. The similarity may be, for example, in direct proportion to the click rate weight, whereby the click rate weight is derived based on the similarity.
According to an embodiment of the present disclosure, the attribute information of each resource may include second location information for each resource. The embodiment may determine matching information between the first location information and the second location information. Based on the matching information, a click rate weight of the predicted click rate for the target object may be determined. For example, the matching information may include a matching value, and the matching value is used as the click rate weight. Wherein the matching value may be determined according to the granularity of the matching location between the first location information and the second location information. For example, if the first location information and the second location information each include provincial level, municipal level, county level, district level, and business level location information, the matching value is a smaller value when only provincial level location information matches the second location information in the first location information. When the provincial level, the municipal level, the county level, the district level and the business district level position information in the first position information are matched with the second position information, the matching value is a larger value. I.e. the higher the level of the matching location, the smaller the matching value. Wherein for example, when two pieces of position information are identical, it may be determined that the two pieces of position information match.
It will be appreciated that the above-described method of determining click rate weights is merely exemplary to facilitate an understanding of the present disclosure, and that the present disclosure may also employ the principles of determining click rate weights described below to determine click rate weights, for example, and will not be described in detail herein.
In operation S230, a click rate of each resource for the target object is determined based on the predicted click rate and the click rate weight.
In operation S240, recommended resources for the target object among the plurality of resources are determined based on click rates of the plurality of resources for the target object.
According to embodiments of the present disclosure, the product between the click rate weight and the predicted click rate may be taken as the click rate for each resource for the target object. After obtaining the click rate of each of the plurality of resources for the target object, the plurality of resources may be ranked from high to low according to the click rate. The former resources are used as recommended resources for the target object.
It may be appreciated that, after recalling a plurality of resources, the embodiments of the present disclosure determine click rate weights based on the first location information and the resource attribute information of the target object, and determine a click rate of each resource based on the click rate weights, so that the determined click rate fully considers the geographic relevance of the resource and the target object. Therefore, the recommended resources determined based on the click rate can comprise the resources around the target object, so that the click rate of the resources and the user satisfaction can be improved conveniently. Furthermore, since the click rate weight is determined based on the matching information between the location information of the resource and the location information of the target object, the relevance of the determined click rate to the geographic location matching relationship can be improved, and the probability of describing the resource of the surrounding information of the target object as a recommended resource can be improved.
Fig. 3 is a schematic diagram of determining click rate weights for predicted click rates for target objects in accordance with an embodiment of the present disclosure.
According to the embodiment of the disclosure, the click rate of the resource for the matching position in the matching information can be considered, the association relation between the click rate of the resource and the matching position is determined based on the click rate, and the click rate weight is determined based on the association relation and the matching information, so that the accuracy of the determined click rate weight is improved. This is because the target object's interest in the resource is generally subject to the overall preference of the population in the same territory. The click rate weight is determined by considering the click rate of the resource at the matching position, so that the finally determined click rate of the resource can characterize group preference under the region.
Illustratively, the foregoing matching information may include a matching location in addition to the matching value. For example, if the first location information of the target object is XX business in L city and P county of a province, and the second location information in the attribute information of the resource is M city and Q county of a province, the matching location includes a province.
According to the embodiment of the disclosure, in determining the click rate weight, in addition to determining the matching information by the method described above, the association value between the click rate and the matching position of each resource may be determined based on the history click information and the history presentation information of each resource. Click rate weights are then determined based on the association and match values.
For example, information clicked by the object at the matching location may be selected from the historical click information, resulting in a click rate for each resource for the matching location. Similarly, the amount of presentation of each resource for the matching location can be obtained. And taking the ratio of the click quantity to the display quantity as the historical click rate of each resource for the matching position. Based on the historical click rate, an association value may be determined, e.g., a positive correlation between the historical click rate and the association value. For example, the historical click rate for each resource may be used as an associated value. Alternatively, the historical click rates of the plurality of resources for the matching positions may be normalized, and the value obtained by the normalization may be used as the correlation value. It will be appreciated that the above-described method of determining an associated value based on a historical click rate is merely an example to facilitate an understanding of the present disclosure, which is not limited in this disclosure.
According to embodiments of the present disclosure, the historical click rate may be affected by the difference between the actual and predetermined amounts of display of the resource. This is because the hot click rate of a resource may be high, and the displayed amount of the resource may affect the hot of the resource to some extent. Therefore, the embodiment can also adjust the value of the historical click rate based on the historical display information when determining the association value, so as to improve the accuracy of the determined association value.
In an embodiment, as shown in fig. 3, in determining the click rate weight, the embodiment 300 may determine, for each resource 320, the matching information 330 between the first location information 311 of the target object 310 and the second location information 321 in the attribute information of the resource 320. Subsequently, a confidence 340 for the matching location of the resource 320 may be determined based on the history exposure information 323 in the attribute information of the resource 320. Meanwhile, an actual click rate 350 for each resource for the matching location may be determined based on the history click information 322 and the history presentation information 323 in the attribute information of the resource 320. An association value 360 between the click rate and the matching location for each resource may then be determined based on the resulting confidence 340 and actual click rate 350. Finally, click rate weights 370 may be determined based on the association values 360 and the match values in the match information 330.
For example, when determining the confidence level, the display information of the object displayed to the matching position in the history display information may be selected first, so as to obtain the actual display amount of each resource for the matching position. A confidence level is then determined based on the ratio of the actual exposure to the predetermined exposure for the matching location. For example, the ratio between the actual exposure and the predetermined exposure may be positively correlated with the confidence. The predetermined display amount may be, for example, an average display amount of a plurality of resources, or may be any value set in advance. In one embodiment, the predetermined presentation amount is associated with a matching location. For example, the higher the level of the matching position, the higher the predetermined presentation amount.
For example, the age information of each resource may also be considered when determining the confidence level, the age information being part of the attribute information. This is because, in order to reduce the amount of calculation, the history display information is generally display information for a predetermined period of time, and the age information of the resource affects the display amount to some extent. After the actual display quantity is obtained, the embodiment can determine the confidence coefficient of each resource aiming at the matching position based on the actual display quantity, the preset display quantity and the time effect information, so that the accuracy of the determined confidence coefficient is improved, and the accuracy of the determined association value and click rate weight is improved. Wherein the age information may be represented by, for example, a time interval between the current time and the release time of the each resource. The age information may be inversely related to the confidence level.
For example, the weight of the ratio between the actual display amount and the predetermined display amount may be obtained based on the aging information, and the ratio between the weighted actual display amount and the predetermined display amount may be used as the confidence. The time information may be represented by a time interval between the release time and the current time of each resource, and the value of the time information may be inversely related to the value of the weight, for example. Or, the value of the time-effect information can be used as an adjusting factor, the value of the ratio between the actual display quantity and the preset display quantity can be adjusted, and the difference between the ratio and the adjusting factor is used as the confidence coefficient. Alternatively, the value of the exponential function whose difference between the ratio and the adjustment factor is a variable may be used as a confidence level, which is not limited in the present disclosure.
For example, after the actual click rate and the confidence are obtained, an association value between the click rate and the matching location for each resource may be determined based on the product of the confidence and the actual click rate. Wherein the product between the confidence and the actual click rate is, for example, positively correlated with the correlation value. The product may be used directly as a confidence, for example, or the correlation value may be determined based on any positive correlation function, which is not limiting to the present disclosure.
For example, after the association value and the matching value are obtained, the product between the association value and the matching value may be used as the click rate weight, for example. Alternatively, the average between the associated value and the matching value may be used as the click rate weight. The embodiment does not limit the method of determining the click rate weight based on the association value and the matching value, as long as both the association value and the matching value are positively correlated with the click rate weight.
Fig. 4 is a schematic diagram of determining an association value between a click rate and a matching location for each resource according to an embodiment of the present disclosure.
According to the embodiment of the disclosure, when determining the association value between the click rate and the matching position of each resource based on the actual click rate and the confidence, for example, the reference click rate may also be considered in order to improve the situation that the association value is inaccurate due to low display amount of the resource. The reference click rate may be obtained in advance based on a historical click rate of each resource in the database for the matching location. For example, the reference click rate may be an average value of historical click rates of each resource in the database for the matching location, and the present disclosure does not limit the value of the reference click rate.
According to an embodiment of the present disclosure, as shown in fig. 4, for each resource 410, the embodiment 400 may first determine an actual click rate 420 based on the historical click information 411 and the historical presentation information 412 for that resource 410 in determining an association value between the click rate and the matching location for each resource. And the confidence 430 is determined based on the history presentation information 412 using the methods described previously. The weighted click rate 450 for the matching location for the resource 410 may then be determined based on the resulting actual click rate 420, confidence 430, and predetermined reference click rate 440. Finally, an evaluation value of the click rate of the resource 410 for the matching position is determined based on the ratio between the weighted click rate 450 and the predetermined reference click rate 440, and the evaluation value is taken as an association value 460.
For example, the confidence may be used as a weight of the actual click rate, a difference between a predetermined value and the confidence may be used as a weight of the predetermined reference click rate, and a weighted sum of the actual click rate and the predetermined reference click rate may be calculated and used as a weighted click rate. Alternatively, the product of the confidence and the actual click rate may be directly used as the weighted click rate, which is not limited by the present disclosure.
For example, when determining the evaluation value, the evaluation value may be mapped to a certain value interval based on a ratio between the weighted click rate 450 and the predetermined reference click rate 440, so as to implement limitation on a value range of the click rate weight, and control the adjustment effect of the click rate weight on the predicted click rate.
For example, the embodiment may take, as the final evaluation value, a smaller value of the ratio between the weighted click rate 450 and the predetermined reference click rate 440 and a first predetermined value to limit the evaluation value to within the first predetermined value. Alternatively, after obtaining the smaller value between the ratio and the first predetermined value, the larger value of the smaller value and the second predetermined value may be taken as the final evaluation value, so as to define the evaluation value between the second predetermined value and the first predetermined value. Alternatively, the ratio between the weighted click rate 450 and the predetermined reference click rate 440 may be amplified first, and the evaluation value may be determined based on the magnitude relation between the amplified value and the predetermined value.
According to the embodiment of the disclosure, when the matching information includes at least two matching positions with at least two levels, for each matching position, the evaluation value of the click rate of each resource for each matching position may be determined by using the foregoing method, so as to obtain at least two evaluation values of the click rate of each resource for at least two matching positions respectively. In this way, when determining the association value, the maximum value of the at least two evaluation values may be taken as the association value between the click rate of each resource and the matching position. For example, if the matching location in the matching information includes a province a and a city M, the embodiment may obtain the evaluation value of each resource for the province a based on the historical click information and the historical display information of the resource for the province a. Similarly, each resource may be evaluated for M city. And taking the evaluation value with larger value of the evaluation value for the A province and the evaluation value for the M city as the association value between the click rate of each resource and the matching position.
According to the embodiment of the disclosure, when the matching information includes at least two matching locations with at least two levels, the evaluation value of the click rate of each resource for the matching location with the smallest granularity among the at least two matching locations may also be determined only by adopting the foregoing method, and the evaluation value is used as the association value between the click rate of each resource and the matching location.
Based on the above method for determining recommended resources, the present disclosure further provides an apparatus for determining recommended resources, which will be described in detail below with reference to fig. 5.
Fig. 5 is a block diagram of an apparatus for determining recommended resources according to an embodiment of the present disclosure.
As shown in fig. 5, the apparatus 500 for determining recommended resources of this embodiment may include a click rate prediction module 510, a weight determination module 520, a click rate determination module 530, and a recommended resource determination module 540.
The click rate prediction module 510 is configured to determine, for each of the recalled plurality of resources, a predicted click rate for the target object for each resource using a click rate prediction model. In an embodiment, the click rate prediction module 510 may be used to perform the operation S210 described above, which is not described herein.
The weight determining module 520 is configured to determine a click rate weight of the predicted click rate for the target object based on the first location information of the target object and the attribute information of each resource. In an embodiment, the weight determining module 520 may be configured to perform the operation S220 described above, which is not described herein.
The click rate determination module 530 is configured to determine a click rate of each resource for the target object based on the predicted click rate and the click rate weight. In an embodiment, the click rate determination module 530 may be used to perform the operation S230 described above, which is not described herein.
The recommended resource determining module 540 is configured to determine recommended resources for the target object from the plurality of resources based on click rates of the plurality of resources for the target object. In an embodiment, the recommended resource determining module 540 may be used to perform the operation S240 described above, which is not described herein.
According to an embodiment of the present disclosure, the attribute information of each resource includes second location information for which the resource is directed. The weight determination module 520 may include a matching information determination sub-module and a weight determination sub-module. The matching information determination submodule is used for determining matching information between the first position information and the second position information. The weight determination submodule is used for determining click rate weights of predicted click rates aiming at target objects based on the matching information.
According to embodiments of the present disclosure, the attribute information of each resource further includes history click information and history presentation information of the resource. The matching information includes a matching position and a matching value. The weight determination module 520 may also include an association value determination submodule. The correlation value determination submodule is used for determining correlation values between the click rate of each resource and the matching position based on the historical click information and the historical display information. The weight determination submodule is specifically used for determining click rate weights of predicted click rates aiming at target objects based on the association values and the matching values.
According to an embodiment of the present disclosure, the association value determination submodule may include a confidence determination unit, a click rate determination unit, and an association value determination unit. The confidence determining unit is used for determining the confidence of each resource for the matching position based on the history display information. The click rate determining unit is used for determining the actual click rate of each resource aiming at the matching position based on the historical click information and the historical display information. The association value determining unit is used for determining an association value between the click rate of each resource and the matching position based on the actual click rate and the confidence.
According to an embodiment of the present disclosure, the attribute information of each resource further includes age information of the resource. The confidence determination unit may include a presentation amount determination subunit and a confidence determination subunit. The display amount determining subunit is configured to determine an actual display amount of each resource for the matching location based on the historical display information. The confidence determination subunit is configured to determine a confidence level for each resource for the matching location based on the actual exposure, the predetermined exposure associated with the matching location, and the age information.
According to an embodiment of the present disclosure, the association value determining unit may include a weight determining subunit and an association value determining subunit. The weighted determination subunit is configured to determine a weighted click rate for each resource for the matching location based on the actual click rate, the predetermined reference click rate for the matching location, and the confidence. The association value determining subunit is configured to determine an evaluation value of the click rate of each resource for the matching position based on a ratio between the weighted click rate and a predetermined reference click rate, and obtain an association value.
According to an embodiment of the present disclosure, the matching information includes at least two matching locations of at least two levels, and the evaluation value includes at least two evaluation values for each of the click rates of the resources for the at least two matching locations, respectively. The association value determining subunit is configured to determine a maximum evaluation value of the at least two evaluation values as an association value.
It should be noted that, in the technical solution of the present disclosure, the acquisition, storage, application, etc. of the related personal information of the user all conform to the rules of the related laws and regulations, and do not violate the popular regulations of the public order.
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. 6 illustrates a schematic block diagram of an example electronic device 600 that may be used to implement the method of determining recommended resources of 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. 6, the apparatus 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 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 601 performs the respective methods and processes described above, for example, a method of determining recommended resources. For example, in some embodiments, the method of determining recommended resources may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method of determining recommended resources described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method of determining recommended resources by any other suitable means (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), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and 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 a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a 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: local Area Networks (LANs), wide Area Networks (WANs), 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 can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual Private Server" or simply "VPS"). The server may also be a server of a distributed system or a server that incorporates 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.
Claims (14)
1. A method of determining recommended resources, comprising:
for each resource in the recalled plurality of resources, determining a predicted click rate of each resource for a target object by adopting a click rate prediction model;
determining click rate weights of the predicted click rates for the target objects based on the first position information of the target objects and the attribute information of each resource;
determining the click rate of each resource for the target object based on the predicted click rate and the click rate weight; and
Determining recommended resources for the target object in the plurality of resources based on click rates of the plurality of resources for the target object;
wherein the attribute information of each resource comprises second position information for the resource; determining the click rate weight of the predicted click rate for the target object includes:
determining matching information between the first location information and the second location information; and
and determining click rate weights of the predicted click rates for the target objects based on the matching information.
2. The method of claim 1, wherein the attribute information of each resource further comprises historical click information and historical presentation information; the matching information comprises a matching position and a matching value; determining the click rate weight of the predicted click rate for the target object further comprises:
determining an association value between the click rate of each resource and the matching location based on the historical click information and the historical presentation information,
wherein the click rate weight of the predicted click rate for the target object is determined based on the association value and the matching value.
3. The method of claim 2, wherein determining the association value between the click rate of each resource and the matching location comprises:
Determining the confidence of each resource for the matching position based on the history display information;
determining the actual click rate of each resource for the matching position based on the historical click information and the historical display information; and
and determining an association value between the click rate of each resource and the matching position based on the actual click rate and the confidence.
4. A method according to claim 3, wherein the attribute information of each resource further comprises age information of the resource; determining the confidence of each resource for the matching location includes:
determining the actual display quantity of each resource aiming at the matching position based on the history display information; and
and determining the confidence of each resource for the matching position based on the actual display amount, the preset display amount associated with the matching position and the time effect information.
5. The method of claim 3, wherein the determining an association value between the click rate of each resource and the matching location based on the actual click rate and the confidence comprises:
determining a weighted click rate for the matching location for each resource based on the actual click rate, a predetermined reference click rate for the matching location, and the confidence; and
And determining an evaluation value of the click rate of each resource aiming at the matching position based on the ratio of the weighted click rate to the preset reference click rate, and obtaining the association value.
6. The method of claim 5, wherein the matching information comprises at least two matching locations of at least two levels, the evaluation values comprising at least two evaluation values for the click rate of each resource for the at least two matching locations, respectively; the determining the association value between the click rate of each resource and the matching position further comprises:
and determining the maximum evaluation value of the at least two evaluation values as the association value.
7. An apparatus for determining recommended resources, comprising:
the click rate prediction module is used for determining the predicted click rate of each resource aiming at the target object by adopting a click rate prediction model aiming at each resource in the recalled plurality of resources;
the weight determining module is used for determining click rate weights of the predicted click rates for the target objects based on the first position information of the target objects and the attribute information of each resource;
the click rate determining module is used for determining the click rate of each resource aiming at the target object based on the predicted click rate and the click rate weight; and
A recommended resource determining module, configured to determine recommended resources for the target object in the plurality of resources based on click rates of the plurality of resources for the target object;
wherein the attribute information of each resource comprises second position information for the resource; the weight determination module comprises:
a matching information determination sub-module for determining matching information between the first location information and the second location information; and
and the weight determining sub-module is used for determining the click rate weight of the predicted click rate aiming at the target object based on the matching information.
8. The apparatus of claim 7, wherein the attribute information of each resource further comprises historical click information and historical presentation information; the matching information comprises a matching position and a matching value; the weight determination module further includes:
an association value determination sub-module for determining an association value between the click rate of each resource and the matching location based on the history click information and the history presentation information,
the weight determination submodule is used for determining click rate weights of the predicted click rates aiming at the target objects based on the association values and the matching values.
9. The apparatus of claim 8, wherein the association value determination submodule comprises:
a confidence determining unit, configured to determine, based on the history display information, a confidence of each resource for the matching location;
the click rate determining unit is used for determining the actual click rate of each resource aiming at the matching position based on the historical click information and the historical display information; and
and the association value determining unit is used for determining an association value between the click rate of each resource and the matching position based on the actual click rate and the confidence.
10. The apparatus of claim 9, wherein the attribute information of each resource further comprises age information of the resource; the confidence determining unit includes:
a display amount determining subunit, configured to determine, based on the historical display information, an actual display amount of each resource for the matching location; and
a confidence determining subunit configured to determine a confidence level of each resource for the matching location based on the actual exposure amount, a predetermined exposure amount associated with the matching location, and the aging information.
11. The apparatus of claim 9, wherein the association value determining unit comprises:
a weighted determining subunit configured to determine a weighted click rate for the matching location for each resource based on the actual click rate, a predetermined reference click rate for the matching location, and the confidence; and
and the association value determining subunit is used for determining an evaluation value of the click rate of each resource for the matching position based on the ratio between the weighted click rate and the preset reference click rate, and obtaining the association value.
12. The apparatus of claim 11, wherein the matching information comprises at least two matching locations of at least two levels, the evaluation values comprising at least two evaluation values for the click rate of each resource for the at least two matching locations, respectively; the association value determining subunit is specifically configured to:
and determining the maximum evaluation value of the at least two evaluation values as the association value.
13. An electronic device, comprising:
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 method of any one of claims 1-6.
14. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-6.
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