CN111858517A - Method, apparatus, device and computer storage medium for determining resource value attributes - Google Patents

Method, apparatus, device and computer storage medium for determining resource value attributes Download PDF

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Publication number
CN111858517A
CN111858517A CN202010600885.4A CN202010600885A CN111858517A CN 111858517 A CN111858517 A CN 111858517A CN 202010600885 A CN202010600885 A CN 202010600885A CN 111858517 A CN111858517 A CN 111858517A
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value
resource
resources
determining
similar
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黄雪原
李传勇
张铮
施鹏
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Beijing Baidu Netcom Science and Technology Co Ltd
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Beijing Baidu Netcom Science and Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/17—Details of further file system functions
    • G06F16/176—Support for shared access to files; File sharing support
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors

Abstract

The application discloses a method, a device, equipment and a computer storage medium for determining a resource value attribute, and relates to the technical field of cloud computing and big data. The specific implementation scheme is as follows: acquiring resources with transaction times larger than or equal to a preset time threshold value in a preset time range to form a candidate reference resource set; determining resources, the similarity of which with the resource basic characteristics of the resources to be predicted meets the preset requirement, from the candidate reference resource set as similar value resources; and determining the value attribute value of the resource to be predicted based on the value attribute value and the market performance characteristics of the similar value resource. Compared with a manual determination mode, the mode provided by the application is not limited by the experience of the user, and the reasonable value attribute value meeting the market demand can be automatically determined.

Description

Method, apparatus, device and computer storage medium for determining resource value attributes
Technical Field
The application relates to the technical field of data processing, in particular to the technical field of cloud computing and big data.
Background
Resource sharing platforms have become the main form of knowledge service platforms, for example, document sharing platforms such as library class, video sharing platforms of course class, and so on. Taking a document sharing platform of a library class as an example, after a user uploads a document, the user needs to set various attributes of the document, and the user faces the setting of value attributes. After the value attribute of the document is set, other users need to pay according to the value attribute of the document to finish the downloading of the document. Currently, the resource value attribute is completely set manually by a user.
Disclosure of Invention
In view of the foregoing, the present application provides a method, apparatus, device, and computer storage medium for automatically determining a resource value attribute.
In a first aspect, the present application provides a method for determining a value attribute of a resource, comprising:
acquiring resources with transaction times larger than or equal to a preset time threshold value in a preset time range to form a candidate reference resource set;
determining resources, the similarity of which with the resource basic characteristics of the resources to be predicted meets the preset requirement, from the candidate reference resource set as similar value resources;
and determining the value attribute value of the resource to be predicted based on the value attribute value and the market performance characteristics of the similar value resource.
In a second aspect, the present application further provides an apparatus for determining a resource value attribute, including:
the candidate resource acquisition unit is used for acquiring a candidate reference resource set formed by resources with transaction times larger than or equal to a preset time threshold value in a preset time length range;
the similar resource determining unit is used for determining resources, the similarity of which with the resource basic characteristics of the resources to be predicted meets the preset requirement, from the candidate reference resource set, and the resources are used as similar value resources;
And the value attribute determining unit is used for determining the value attribute value of the resource to be predicted based on the value attribute value and the market performance characteristics of the similar value resource.
In a third aspect, the present application provides an electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method described above.
In a fourth aspect, the present application provides a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method described above.
According to the technical scheme, the value attribute value of the resource to be predicted is determined through the value attribute value and the market performance of the resource similar to the resource to be predicted. Compared with a manual determination mode, the method is not limited by the experience of the user, and the reasonable value attribute value meeting the market demand can be automatically determined.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 illustrates an exemplary system architecture for a method or apparatus for determining a value of a resource attribute to which embodiments of the invention may be applied;
FIG. 2 is a flowchart of a method for determining a resource value attribute according to a second embodiment of the present application;
FIG. 3 is a flowchart of a method for pricing documents to be forecasted based on market performance of the documents according to a third embodiment of the present application;
FIG. 4 is a schematic diagram of a price-demand curve provided in the third embodiment of the present application;
fig. 5 is a structural diagram of an apparatus for determining a resource value attribute according to a fourth embodiment of the present application;
FIG. 6 is a block diagram of an electronic device used to implement embodiments of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those 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 application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the current resource sharing platform, the value attribute setting of resources completely depends on manual setting of users, and for users with poor market experience, reasonable value attributes are difficult to set, and even the value attributes cannot be set. Even users with a high market experience have difficulty maximizing value due to lack of value attribute comparisons with other similar documents.
The general value determination method generally comprises three categories of cost guidance, competition guidance and demand guidance, a resource sharing platform belongs to the field of knowledge payment, and a knowledge payment product has the obvious characteristic that the marginal cost is 0, so that the demand of a user, namely the purchase intention fluctuates along with the change of a value attribute value of the same resource, and the cost is relatively fixed. Therefore, the method for determining the value attribute of the demand direction is adopted in the application, and the idea of taking the value perception of the product of the consumer as a starting point is taken.
The technical contents provided by the present application are described in detail below with reference to examples.
The first embodiment,
FIG. 1 illustrates an exemplary system architecture to which an embodiment of the present invention may be applied to a method or apparatus for determining a value of a resource attribute.
As shown in fig. 1, the system architecture may include terminal devices 101 and 102, a network 103, and a server 104. The network 103 serves as a medium for providing communication links between the terminal devices 101, 102 and the server 104. Network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may interact with server 104 through network 103 using terminal devices 101 and 102. Various applications, such as an application client of a resource sharing platform, a client of a web browser application, a client of a communication application, and the like, may be installed on the terminal devices 101 and 102.
Terminal devices 101 and 102 may be various electronic devices capable of resource sharing, and may include, but are not limited to, a smart phone, a tablet computer, a notebook computer, a PC, a wearable device, a smart speaker, a smart television, and so on. The device for determining the resource value attribute provided by the present invention may be configured and operated in the server 104. It may be implemented as a plurality of software or software modules (for example, for providing distributed services), or as a single software or software module, which is not specifically limited herein.
For example, the device for determining the value attribute of the resource is configured and operated in the server 104, and then after the user uploads the resource to the server 104 through the terminal device 101 by using the resource sharing platform, the server 104 determines the value attribute value of the resource by using the method provided by the present application, and recommends the value attribute value to the user of the terminal device 101. The user may set the value attribute of the resource according to the value attribute value. When other users desire to download the resource on the resource sharing platform, such as the terminal device 102, the resource can be downloaded only by completing payment according to the value attribute of the resource.
The server 104 may be a single server, or may be a server group formed by a plurality of servers, and may provide cloud computing services in the cloud. It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Example II,
Fig. 2 is a flowchart of a method for determining a resource value attribute according to the second embodiment of the present application, where resources related to the present application may be media resources such as documents, videos, and audios, and may even be resources such as installation packages and templates. The value attribute value of a resource may be a price embodied in currency, or may be embodied in other forms such as points, volumes, cards, virtual currency, etc. within a platform. As shown in fig. 2, the method mainly comprises the following steps:
in 201, resources with transaction times larger than or equal to a preset time threshold within a preset time range are acquired to form a candidate reference resource set.
In the application, when the value attribute of the resource is determined, the value attribute values of historical transactions of other similar resources need to be referred to, so that the resource with a certain transaction frequency needs to be screened out as a candidate reference resource set. The preset time threshold value can be set according to an empirical value, the resource transaction scale of the platform and the like. For example, a resource may be taken that has a number of trades greater than or equal to 2 within a year.
In 202, from the candidate reference resource set, a resource whose similarity with the resource basic feature of the resource to be predicted meets a preset requirement is determined as a similar value resource.
The resource to be predicted involved in the step is the resource needing to determine the value attribute, and in some scenes, the resource can be newly uploaded by a user on the platform, or the resource without the value attribute set in the platform.
Generally, there are multiple value influencing factors for each type of resource, and those factors have the largest influence on the value, which needs to be selected as the resource basic characteristics for determining the value resources similar to the resource to be predicted. The value influence factors of the resources are mainly divided into 4 aspects:
in a first aspect: belongs to the field of technology. May be embodied by title, content classification, etc.
In a second aspect: the amount of information. May be embodied by resource length, resource quality, producer information, etc. Where for documents, the resource length is typically expressed in pages, lines, numbers of words, etc. For video, audio, etc., it may be embodied as a duration, etc.
In a third aspect: time/exclusivity. May be embodied by upload time, repetition rate, etc.
In a fourth aspect: utility/usage scenarios. May be embodied by a format type of the resource, etc.
In the application, the correlation between the value influence factors and the value attributes of the resources can be analyzed by a maximum information coefficient method; and selecting the value influence factors of which the correlation with the value attributes meets the preset correlation requirement from the value influence factors as the types of the basic features of the resources.
The maximum information coefficient method is a method for detecting nonlinear correlation between variables, and is a mature method at present. In the application, the maximum information coefficient method is used for analyzing the relevance of various value influence factors and the value attributes. For example, the value influence factors of which the correlation is greater than a preset threshold or is ranked in the first several are selected as the types of the resource basic features.
After analysis, the types of resource base features may include at least one of: content summaries, format types, upload time and resource length. Where the content summaries may be, for example, titles, summaries, texts of the resources, etc. The resource length can be embodied as document page number, word number and the like, and can also be embodied as video time length, audio time length and the like.
When the similar value resources are determined, weighting processing can be performed on the similarity of each resource basic feature of the resources and each resource basic feature of the resources to be predicted respectively aiming at each resource in the candidate reference resource set to obtain the total similarity; and determining resources with the value of the total similarity meeting the preset requirement from the candidate reference resource set as similar value resources. The method can comprehensively consider the similarity of the resource to be predicted and the similar value resource on the basic characteristics of each resource, and can flexibly adjust each weighting coefficient according to actual requirements.
Preferably, before the similarity calculation based on the resource base features is performed on each resource in the candidate reference resource set, each resource in the candidate reference set may be first subjected to rough recall and/or filtering based on one feature or a partial feature in the resource base features.
Taking document resources as an example, after analysis by the maximum information coefficient method, the obtained document title, format type, uploading time and page number are the ones with the strongest correlation with the document price, and then the document title, format type, uploading time and page number can be used as the basic attributes of the document.
The following steps are then performed:
s1, recalling the first 50 documents with the title similarity with the document to be predicted based on the document titles from the candidate reference document set; if the document is not recalled, subsequent processing cannot be performed.
And S2, selecting the document with the title similarity of more than or equal to 90% with the document to be predicted based on the title similarity. If the selected document reaches more than 3, continuing with S3; otherwise, the value attribute determination process for the document to be predicted is directly ended, and subsequent processing cannot be performed.
S3, aiming at each document in each candidate reference document set, respectively calculating the page number similarity sim between the document and the document to be predictedpageFormat type similarity simtypeSimilarity with uploading time simtimeThen, the weighted summation is carried out according to the following formula to obtain the total similarity simcomp：
simcomp＝rpage*simpage+rtype*simtype+rtime*simtime
Wherein r ispage、rtypeAnd rtimeThe weighting coefficients can be respectively embodied according to the correlation between the page number, the format type, the uploading time and the document price, and can be derived from the analysis result of the maximum information coefficient method. For example, the values may be: 0.146, 0.118, 0.213.
It should be noted that the above values are examples listed in the embodiments of the present application, and are not limited to the above values.
Specifically, the page number similarity sim of the documentpageThe following formula may be used to determine:
Figure BDA0002558564580000071
wherein page represents the number of pages of the document to be predicted, and pagesimRepresenting pages of documents of similar value, pagedismaxRepresenting the maximum value of the absolute difference of the page numbers of all similar value documents and the document to be predicted.
Format type similarity sim of documenttypeThe following formula may be used to determine:
Figure BDA0002558564580000072
wherein type represents the format type of the document to be predicted, and typesimRepresenting the format type of the similar value document. The format type may be, for example, doc format, ppt format, txt format, pdf format, or the like.
Uploading time similarity sim of documenttimeThe following formula may be used to determine:
Figure BDA0002558564580000073
wherein, the time represents the uploading time of the document to be predicted, and the time represents the uploading time of the document to be predictedsimRepresenting the upload time of a similar value document, timedismaxRepresenting the maximum value of the absolute values of the uploading time differences of all similar value documents and the documents to be predicted, and the function abs () represents the absolute value.
At 203, a value attribute value of the resource to be forecasted is determined based on the value attribute values and market performance characteristics of the similar value resources.
In addition to referring to the value attribute values of similar resources, in order to maximize the profit as much as possible, therefore, it is necessary to further consider the market performance of the value attribute of the resource. The most remarkable market performance characteristic of the resource sharing platform is the conversion rate. The conversion rate cvr is defined as the cumulative transaction amount n after the resource is uploaded saleAnd the accumulated browsing volume nviewThe ratio of (a) to (b), namely:
Figure BDA0002558564580000081
before this step, the value attribute values of the similar value resources may be converted based on the resource length ratios of the similar value resources and the resources to be predicted, and the converted value attribute values of the similar value resources may be recorded for subsequent determination of the value attribute values of the values to be predicted.
Firstly, resources with the benefits meeting the preset benefit requirement in the same type of resources within the preset historical duration can be obtained; and analyzing the obtained conversion rate distribution of the resources so as to determine an optimal conversion rate interval. The optimal transformation interval reflects the optimal market performance of the same type of resources in history.
Then, based on the price-demand curve, the value attribute values (the converted value attribute values) of the similar value resources that can make the conversion rate in the optimal conversion rate interval are determined from the similar value resources. And obtaining the value attribute value of the resource to be predicted by using the determined value attribute value. Based on the determined value attribute, the value of the resource to be predicted can be enabled to bring great benefits to the user as much as possible.
If the value attribute value of the similar value resource which can enable the conversion rate to be in the optimal conversion rate interval is not determined from the similar value resources, respectively adjusting the value attribute (based on the value attribute value after the conversion) of each similar value resource to enable the conversion rate to approach the optimal conversion rate interval; and determining the value attribute of the resource to be predicted by using the adjusted average value of the value attributes of the similar value resources.
Taking a document sharing platform as an example, a specific implementation process of the step 203 is schematically described with reference to the third embodiment.
Example III,
Fig. 3 is a flowchart of a method for pricing a document to be predicted based on the document market performance according to a third embodiment of the present application, where as shown in fig. 3, the method may include the following steps:
at 301, one of the determined similar value documents is selected as the current similar value document.
The selection can be carried out sequentially or randomly.
At 302, the price of the current similar value document is reduced based on the number of pages.
Similar value documents can be understood as documents with similar quality, and what influences the final price is the amount of information, which is reflected in the document and is more typical of the number of pages.
In the embodiment of the application, the converted price can be obtained by simply multiplying the ratio of the number of pages of the document to be predicted to the number of pages of the similar value document by the price of the formal value document.
But because the user's sensitivity to price varies as the number of pages increases. For example, for a document with a small number of pages, say within 10 pages, a difference of 3 pages is more obvious, but for a document with a large number of pages, say several hundred pages, a difference of 3 pages is almost negligible. Thus, the present application provides another preferred method of conversion:
The number of pages of the document is first segmented and tolerances are set up. For example, the segment labels shown in table 1 below are used:
TABLE 1
Segmentation label Range of pages
1 [1,4]
2 [5,10]
3 [11,20]
4 [21,40]
5 [41,80]
6 [81,150]
7 [151,300]
8 >301
If the segmentation label of the document to be predicted is the same as that of the current similar value document, or the adjacent segmentation labels are different, and the number of pages is less than the preset tolerance tparaThen the proportional rate is convertedpricepageIs 1.
Otherwise, the following formula can be used to calculate the reduced proportion ratepricepage：
Figure BDA0002558564580000101
Wherein, paralabel represents the segment label of the document to be predicted, and issimSegment tags representing current similar value documents. Tolerance tparaEmpirical values may be used, such as the following:
Figure BDA0002558564580000102
in 303, judging whether the converted price of the current similar value document can enable the conversion rate to be in an optimal conversion rate interval [ a%, b% ] based on the price-demand curve, and if so, executing 304; if less than a%, 305 is performed, and if greater than b%, 309 is performed.
The price-demand curve is an existing, relatively classical curve describing the inverse relationship between price and demand (conversion). As shown in FIG. 4, the vertical axis P represents the price and the horizontal axis Q represents the conversion. The price Pa corresponds to the conversion rate of a percent, the price Pb corresponds to the conversion rate of b percent, and the range from a percent to b percent is an optimal conversion rate interval.
After analyzing the conversion rate of some documents with the best profit in one year, the optimal conversion rate interval [ a%, b% ] of the documents can be determined, for example [ 1%, 2.5% ].
At 304, the reduced price of the current similar value document is determined as the pricing of the document to be predicted, i.e. the value attribute value. And ending the flow.
In 305, the converted price of the current similar value document is adjusted to make the conversion rate approach a%, and the adjusted price of the current similar value document is recorded.
The adjusted price may be determined using the following formula:
Figure BDA0002558564580000103
wherein price represents adjusted price, priceiRepresenting the reduced price of the current similar value document, cvriAnd expressing the conversion rate corresponding to the price after the current similar value document is converted.
In 306, judging whether other similar value documents which are not selected exist, if so, executing 307; otherwise, 308 is performed.
In 307, one of the similar value documents that has not been selected is selected as the current similar value document, and the process goes to step 302.
In 308, after averaging the recorded prices of all similar value documents, the pricing of the document to be predicted is obtained, and the process is ended.
In the step, it is stated that all similar value documents have no converted value and the conversion rate falls into the optimal conversion rate interval, in this case, the recorded prices (i.e. adjusted prices) of all similar value documents are averaged, and the average value is used as the pricing of the document to be predicted.
In 309, the converted price of the current similar value document is adjusted to make the conversion rate approach b%, and the adjusted price of the current similar value document is recorded, and step 306 is executed.
The adjusted price may be determined using the following formula:
Figure BDA0002558564580000111
the above is a detailed description of the method provided in the present application, and the following is a detailed description of the apparatus provided in the present application with reference to the embodiments.
Example four,
Fig. 5 is a structural diagram of an apparatus for determining resource value attributes according to a fourth embodiment of the present disclosure, where the apparatus may be disposed at a server, and may be an application located at the server, or may also be a functional unit such as a Software Development Kit (SDK) or a plug-in the application located at the server, or may also be located at a computer terminal with strong computing power, which is not particularly limited in this embodiment of the present disclosure. As shown in fig. 5, the apparatus may include: the candidate resource obtaining unit 01, the similar resource determining unit 02, and the value attribute determining unit 03 may further include a basic feature determining unit 04, a value converting unit 05, a conversion rate analyzing unit 06, and a value attribute providing unit 07. The main functions of each component unit are as follows:
The candidate resource acquiring unit 01 is configured to acquire a candidate reference resource set formed by resources whose transaction times are greater than or equal to a preset time threshold within a preset time range.
The preset time threshold value can be set according to an empirical value, the resource transaction scale of the platform and the like. For example, a resource may be taken that has a number of trades greater than or equal to 2 within a year.
And the similar resource determining unit 02 is configured to determine, from the candidate reference resource set, a resource whose similarity with the resource basic feature of the resource to be predicted meets a preset requirement as a similar value resource.
And the value attribute determining unit 03 is configured to determine a value attribute value of the resource to be predicted based on the value attribute value and the market performance characteristics of the similar value resource.
The basic feature determining unit 04 is configured to analyze a correlation between a value influence factor of the resource and a value attribute by using a maximum information coefficient method; and selecting the value influence factors of which the correlation with the value attributes meets the preset correlation requirement from the value influence factors as the types of the basic features of the resources.
The determined type of the resource basic feature may include at least one of the following:
content summaries, format types, upload time and resource length.
Specifically, the similar resource determining unit 02 may perform weighting processing on the similarity between each resource basic feature of the resource and each resource basic feature of the resource to be predicted, respectively, for each resource in the candidate reference resource set, to obtain a total similarity; and determining resources with the value of the total similarity meeting the preset requirement from the candidate reference resource set as similar value resources.
Preferably, before the similarity calculation based on the resource base features is performed on each resource in the candidate reference resource set, each resource in the candidate reference set may be first subjected to rough recall and/or filtering based on one feature or a partial feature in the resource base features.
And the value converting unit 05 is configured to convert the value attribute values of the similar value resources based on the resource length ratios of the similar value resources and the resources to be predicted, record the converted value attribute values of the similar value resources, and provide the value attribute values to the value attribute determining unit 03 for subsequently determining the value attribute values of the values to be predicted.
The conversion rate analysis unit 06 is configured to obtain a resource with a benefit meeting a preset benefit requirement in the same type of resource within a preset historical time; and analyzing the obtained conversion rate distribution of the resources to determine an optimal conversion rate interval.
The value attribute determining unit 03 may determine, based on the price-demand curve, a value attribute value of a similar value resource that enables the conversion rate to be in the optimal conversion rate interval from among the similar value resources; and determining the value attribute value of the resource to be predicted by using the determined value attribute value.
If the value attribute value of the similar value resource which can enable the conversion rate to be in the optimal conversion rate interval is not determined from the similar value resources, the value attribute determining unit 03 adjusts the value attribute of each similar value resource respectively to enable the conversion rate to approach the optimal conversion rate interval; and determining the value attribute of the resource to be predicted by using the adjusted average value of the value attributes of the similar value resources.
And the value attribute providing unit 07 is used for providing the determined value attribute of the resource to be predicted to the user publishing the resource, so as to be used as a reference for setting the value attribute of the resource to be predicted by the user.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 6, it is a block diagram of an electronic device according to the method for determining a resource value attribute of the embodiment of the present application. 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 phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the method of determining a resource value attribute provided herein. A non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the method of determining a value attribute of a resource as provided herein.
The memory 602, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method of determining a resource value attribute in embodiments of the present application. The processor 601 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 602, that is, implementing the method of determining resource value attributes in the above method embodiments.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 602 optionally includes memory located remotely from the processor 601, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected by a bus or other means, and fig. 6 illustrates the connection by a bus as an example.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device, such as a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick, or other input device. The output devices 604 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
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 a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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 clients and servers. A client and server are generally 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.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (21)

1. A method of determining a resource value attribute, comprising:
acquiring resources with transaction times larger than or equal to a preset time threshold value in a preset time range to form a candidate reference resource set;
determining resources, the similarity of which with the resource basic characteristics of the resources to be predicted meets the preset requirement, from the candidate reference resource set as similar value resources;
and determining the value attribute value of the resource to be predicted based on the value attribute value and the market performance characteristics of the similar value resource.
2. The method of claim 1, further comprising:
analyzing the correlation between the value influence factors of the resources and the value attributes by a maximum information coefficient method;
and selecting the value influence factors of which the correlation with the value attributes meets the preset correlation requirement from the value influence factors as the types of the basic features of the resources.
3. The method of claim 1 or 2, the type of resource base feature comprising at least one of:
content summaries, format types, upload time and resource length.
4. The method according to claim 1, wherein determining, from the candidate reference resource set, a resource whose similarity with a resource base feature of a resource to be predicted meets a preset requirement as a similar value resource comprises:
respectively weighting the similarity of each resource basic feature of the resource and each resource basic feature of the resource to be predicted aiming at each resource in the candidate reference resource set to obtain the total similarity;
and determining the resources with the total similarity value meeting the preset requirement from the candidate reference resource set as similar value resources.
5. The method of claim 1, further comprising, prior to determining the value attribute value for the resource to be forecasted based on the value attribute value and market performance characteristics of the similar value resource:
and respectively converting the value attribute value of each similar value resource based on the resource length ratio of each similar value resource to the resource to be predicted, and recording the converted value attribute value of each similar value resource for subsequently determining the value attribute value of the value to be predicted.
6. The method of claim 1, wherein determining the value attribute value for the resource to be forecasted based on the value attribute value and market performance characteristics of the similar value resource comprises:
determining a value attribute value of the similar value resource which can enable the conversion rate to be in an optimal conversion rate interval from the similar value resources based on a price-demand curve;
and determining the value attribute value of the resource to be predicted by using the determined value attribute value.
7. The method of claim 6, further comprising:
acquiring resources with the benefits meeting the preset benefit requirement in the same type of resources within the preset historical duration;
and analyzing the obtained conversion rate distribution of the resources, and determining the optimal conversion rate interval.
8. The method of claim 6, further comprising:
if the value attribute value of the similar value resource which can enable the conversion rate to be in the optimal conversion rate interval is not determined from the similar value resources, respectively adjusting the value attribute of each similar value resource to enable the conversion rate to approach the optimal conversion rate interval;
and determining the value attribute of the resource to be predicted by using the adjusted average value of the value attributes of the similar value resources.
9. The method of any of claims 1 to 8, wherein the resource comprises a document.
10. The method of any of claims 1 to 8, further comprising:
and providing the determined value attribute of the resource to be predicted to a user issuing the resource to be predicted, so as to be used as a reference for setting the value attribute of the resource to be predicted by the user.
11. An apparatus for determining a resource value attribute, comprising:
the candidate resource acquisition unit is used for acquiring a candidate reference resource set formed by resources with transaction times larger than or equal to a preset time threshold value in a preset time length range;
the similar resource determining unit is used for determining resources, the similarity of which with the resource basic characteristics of the resources to be predicted meets the preset requirement, from the candidate reference resource set, and the resources are used as similar value resources;
and the value attribute determining unit is used for determining the value attribute value of the resource to be predicted based on the value attribute value and the market performance characteristics of the similar value resource.
12. The apparatus of claim 11, further comprising:
the basic characteristic determining unit is used for analyzing the correlation between the value influence factors of the resources and the value attributes by a maximum information coefficient method; and selecting the value influence factors of which the correlation with the value attributes meets the preset correlation requirement from the value influence factors as the types of the basic features of the resources.
13. The apparatus according to claim 11 or 12, the type of the resource base feature comprising at least one of:
content summaries, format types, upload time and resource length.
14. The apparatus according to claim 11, wherein the similar resource determining unit is specifically configured to:
respectively weighting the similarity of each resource basic feature of the resource and each resource basic feature of the resource to be predicted aiming at each resource in the candidate reference resource set to obtain the total similarity;
and determining the resources with the total similarity value meeting the preset requirement from the candidate reference resource set as similar value resources.
15. The apparatus of claim 11, further comprising:
and the value conversion unit is used for respectively converting the value attribute values of the similar value resources based on the resource length ratio of the similar value resources to the resource to be predicted, recording the converted value attribute values of the similar value resources, and providing the converted value attribute values to the value attribute determination unit for subsequently determining the value attribute value of the value to be predicted.
16. The apparatus according to claim 11, wherein the value attribute determination unit is specifically configured to:
Determining a value attribute value of the similar value resource which can enable the conversion rate to be in an optimal conversion rate interval from the similar value resources based on a price-demand curve;
and determining the value attribute value of the resource to be predicted by using the determined value attribute value.
17. The apparatus of claim 16, further comprising:
the conversion rate analysis unit is used for acquiring resources with the benefits meeting the preset benefit requirement in the same type of resources within the preset historical duration; and analyzing the obtained conversion rate distribution of the resources, and determining the optimal conversion rate interval.
18. The apparatus according to claim 16, wherein the value attribute determining unit is further configured to, if no value attribute value of a similar value resource that can bring the conversion rate into an optimal conversion rate interval is determined from the similar value resources, respectively adjust the value attribute of each similar value resource so that the conversion rate approaches the optimal conversion rate interval;
and determining the value attribute of the resource to be predicted by using the adjusted average value of the value attributes of the similar value resources.
19. The apparatus of any of claims 11 to 18, further comprising:
and the value attribute providing unit is used for providing the determined value attribute of the resource to be predicted to a user issuing the resource to be predicted, so as to be used as a reference for setting the value attribute of the resource to be predicted by the user.
20. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
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 1-10.
21. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-10.
CN202010600885.4A 2020-06-28 2020-06-28 Method, apparatus, device and computer storage medium for determining resource value attributes Pending CN111858517A (en)

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