CN110838019A - Method and device for determining trial supply distribution crowd - Google Patents
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Abstract
The invention discloses a method and a device for determining a group of trial products, and relates to the technical field of computers. One embodiment of the method comprises: acquiring behavior data of each user, including receiving test article record data; setting the value of the recorded data of the test article to be a first numerical value, inputting the behavior data into a trained probability model, and determining a first purchase probability under the condition of receiving the test article; setting the value of the recorded data of the taken test article as a second numerical value, inputting the behavior data into the trained probability model, and determining a second purchase probability under the condition of not taking the test article; and determining the evaluation index of each user according to the first purchase probability and the second purchase probability, and determining the trial supply distribution crowd according to the evaluation index of each user. The embodiment can avoid the defects in the prior art, and the reasonable evaluation indexes are given by combining the distribution effect of the test articles, so that the crowd for distributing the test articles can be automatically screened out.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for determining a group of trial products.
Background
The current solution to the problem of trial supply distribution is generally two ways:
the method comprises the steps of adopting and selling to issue query conditions, screening users meeting the requirements of adopting and selling on purchasing records, user portrait, purchasing browsing records and the like of brands, categories or SKUs (Stock Keeping units) from a system, and issuing test articles along with main commodities when corresponding users subsequently place orders on a platform for purchase again.
And secondly, the system calculates the probability of purchasing commodities again by a user according to data information such as user portrait, purchasing records and purchasing browsing records of brand, product class or SKU (stock keeping unit) and the like by a machine learning method, screens the users according to the purchasing probability of the users, and issues trial products together with main commodities when the corresponding users purchase commodities on the platform again.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
the effect of the trial supply distribution is not sensed, users are screened according to experience only by adopting a marketing mode, and the effect difference of activities recorded by different people is probably larger; and the second mode screens the users according to the purchasing probability, but does not show the effect of the trial product release on the purchasing behaviors of the users.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for determining a group of trial distribution people, which can avoid the defects in the prior art, provide a reasonable evaluation index in combination with the function of trial distribution, and automatically screen out the group of trial distribution people.
To achieve the above object, according to an aspect of an embodiment of the present invention, a method for determining a trial distribution population is provided.
The method for determining the trial supply distribution crowd according to the embodiment of the invention comprises the following steps:
acquiring behavior data of each user, including receiving test article record data;
setting the value of the recorded data of the test article to be a first numerical value, inputting the behavior data into a trained probability model, and determining a first purchase probability under the condition of receiving the test article; setting the value of the recorded data of the taken test article as a second numerical value, inputting the behavior data into the trained probability model, and determining a second purchase probability under the condition of not taking the test article;
and determining the evaluation index of each user according to the first purchase probability and the second purchase probability, and determining the trial supply distribution crowd according to the evaluation index of each user.
Optionally, the evaluation index of each user is determined according to the following formula according to the first purchase probability and the second purchase probability:
t＝a×p1+(1-a)×(p1-p0)+
in the formula, t represents an evaluation index; p1 represents a first probability of purchase under the condition of drawing a test article; p0 represents a second probability of purchase without picking up the test article; (p1-p0)+Max (0, (p1-p 0)); a represents a weighting coefficient.
Optionally, the method for determining a trial method population according to the embodiment of the present invention further includes: receiving a test article dispensing request, the test article dispensing request comprising: the number of trial products and the issuing strategy; and determining the value of the weighting coefficient a according to the issuing strategy.
Optionally, determining a trial supply distribution population according to the evaluation index of each user includes:
selecting the first (n + b) users as trial supply distribution crowds according to the sequence of the evaluation indexes from high to low; wherein n represents the number of the test articles; b represents a constant.
Optionally, before inputting the behavior data into the trained probabilistic model, the method further includes: determining that a trained probability model exists; and the number of the first and second groups,
and if the trained probability model does not exist, training the probability model according to the behavior data of each user by a machine learning method.
Optionally, the behavioural data further comprises at least one of: purchase log data, plus shopping cart log data, browsing log data, attention log data.
According to yet another aspect of an embodiment of the present invention, there is provided an apparatus for determining a group of trial items.
The device for determining the trial supply distribution crowd according to the embodiment of the invention comprises the following components:
the probability generation module is used for acquiring behavior data of each user, including the data of the test article receiving records; setting the value of the recorded data of the test article to be a first numerical value, inputting the behavior data into a trained probability model, and determining a first purchase probability under the condition of receiving the test article; setting the value of the recorded data of the taken test article as a second numerical value, inputting the behavior data into the trained probability model, and determining a second purchase probability under the condition of not taking the test article;
the user evaluation module determines the evaluation index of each user according to the first purchase probability and the second purchase probability;
and the user screening module is used for determining the trial supply distribution crowd according to the evaluation index of each user.
Optionally, the user evaluation module determines the evaluation index of each user according to the following formula according to the first purchase probability and the second purchase probability:
t＝a×p1+(1-a)×(p1-p0)+
in the formula, t represents an evaluation index; p1 represents a first probability of purchase under the condition of drawing a test article; p0 represents a second probability of purchase without picking up the test article; (p1-p0)+Max (0, (p1-p 0)); a represents a weighting coefficient.
Optionally, the apparatus for determining a group of trial products issuing persons according to the embodiment of the present invention further includes: the strategy issuing module is used for issuing a trial supply issuing request to the user evaluation module, wherein the trial supply issuing request comprises: the number of trial products and the issuing strategy; the user evaluation module is further configured to: and determining the value of the weighting coefficient a according to the issuing strategy.
Optionally, the user screening module determines the trial supply distribution crowd according to the evaluation index of each user, including:
selecting the first (n + b) users as trial supply distribution crowds according to the sequence of the evaluation indexes from high to low; wherein n represents the number of the test articles; b represents a constant.
Optionally, before the probability generation module inputs the behavior data into the trained probability model, the user evaluation module is further configured to: determining that a trained probability model exists; and the number of the first and second groups,
and if the trained probability model does not exist, training the probability model according to the behavior data of each user by a machine learning method.
Optionally, the behavioural data further comprises at least one of: purchase log data, plus shopping cart log data, browsing log data, attention log data.
According to another aspect of the embodiments of the present invention, there is provided an electronic device for determining a group of trial items.
The electronic equipment for determining the trial supply distribution crowd according to the embodiment of the invention comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method for determining a group of trial items provided by the first aspect of the embodiment of the present invention.
According to yet another aspect of an embodiment of the present invention, a computer-readable medium is provided.
According to an embodiment of the present invention, a computer readable medium is stored with a computer program, which when executed by a processor, implements the method for determining a group of trial items provided by the first aspect of the embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: the method comprises the steps of determining the evaluation index of each user according to the first purchase probability of the user under the condition of receiving the trial products and the second purchase probability of the user under the condition of not receiving the trial products, further determining the trial product distribution crowd, avoiding the defects in the prior art, giving out reasonable evaluation indexes by combining the distribution effect of the trial products, and automatically screening the trial product distribution crowd.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of a main flow of a method for determining a trial supply distribution population according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the major modules of an apparatus for identifying a group of trial products issuing persons according to an embodiment of the present invention;
FIG. 3 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 4 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. 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 invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
According to one aspect of an embodiment of the present invention, a method for determining a group of trial items is provided.
Fig. 1 is a schematic view of a main flow of a method for determining a group of trial products according to an embodiment of the present invention, and as shown in fig. 1, the method for determining the group of trial products according to the embodiment of the present invention includes: step S101, step S102, and step S103.
And S101, acquiring behavior data of each user, including the data of the test article receiving records.
User behavior data refers to data relating to the behavior of a user. Optionally, the behavioural data further comprises at least one of: purchase log data, plus shopping cart log data, browsing log data, attention log data. In the actual application process, the behavior data may be regarded as implicit evaluation of the user on the product itself, or may be embodied by user figures such as user stickiness or loyalty (the user figures are also called user profile, and may be regarded as an attribute description of the personality characteristics of the actual user in the data world). The invention does not place behavioral data into the user representation.
It should be noted that the behavior data in the embodiment of the present invention may be behavior data of a user purchasing a certain brand product, a certain category of product, or a certain product, or may also be behavior data of purchasing other products that need statistics.
S102, setting the value of the recorded data of the test article to be a first numerical value, inputting behavior data into a trained probability model, and determining a first purchase probability under the condition of receiving the test article; and setting the value of the recorded data of the received test article as a second numerical value, inputting the behavior data into the trained probability model, and determining a second purchase probability under the condition of not receiving the test article.
The first value is used for indicating that the behavior data is the behavior data under the condition of receiving the test article, and the value of the first value can be selectively set according to the actual situation, for example, the first value is set to be 1, and when the value of the record data of the received test article is 1, the behavior data is indicated under the condition of receiving the test article.
The second value is used for indicating that the behavior data is the behavior data under the condition of not accepting the test article, and the value of the second value can be selectively set according to the actual situation, for example, the first value is set to be 0, and when the value of the record data of the accepted test article is 0, the behavior data is the behavior data under the condition of not accepting the test article.
Before inputting the behavior data into the trained probabilistic model, the method may further include: determining that a trained probability model exists; and if the trained probability model does not exist, training the probability model according to the machine learning method according to the behavior data of each user.
Those skilled in the art can select a suitable algorithm to train the probability model according to different practical application scenarios. The probability model is trained by adopting a machine learning algorithm, so that the method is simple and convenient and has good accuracy. For example, the probability model is trained by using algorithms such as a logistic regression algorithm, an SVM (Support Vector Machine), and a neural network.
And S103, determining the evaluation index of each user according to the first purchase probability and the second purchase probability, and determining the trial supply distribution crowd according to the evaluation index of each user.
When the evaluation index of each user is determined according to the first purchase probability and the second purchase probability, the first purchase probability and the second purchase probability can be averaged, and the obtained average value is used as the evaluation index of the user; or the weighted sum is used as the evaluation index of the user by adopting a weighted sum method; or determining the evaluation index of the user by adopting a certain formula according to the first purchase probability and the second purchase probability.
Optionally, the evaluation index of each user is determined according to the following formula according to the first purchase probability and the second purchase probability:
t＝a×p1+(1-a)×(p1-p0)+
in the formula, t represents an evaluation index; p1 represents a first probability of purchase under the condition of drawing a test article; p0 represents a second probability of purchase without picking up the test article; (p1-p0)+Max (0, (p1-p 0)); a represents a weighting coefficient.
The first purchase probability of a user purchasing a certain brand of product, a certain product category or a certain specific product under the condition of getting a trial product can be used as an index influencing the repeated purchase rate. The difference value of the first purchase probability of the user for purchasing a certain brand of product, a certain commodity class or a second purchase probability of a specific commodity under the condition of getting the trial supplies and the condition of not getting the trial supplies reflects the influence of the behavior of issuing the trial supplies on the purchase probability of the user, and the habitual repurchase purchase probability is removed. Thus, in the examples of the present invention (p1-p0)+As an index for evaluating the effect of the trial, and a as a weighting factor to control the trade-off between strategies.
Optionally, the method for determining a trial method population according to the embodiment of the present invention further includes: receiving a test article dispensing request, the test article dispensing request comprising: the number of trial products and the issuing strategy; and determining the value of the weighting coefficient a according to the issuing strategy. The issuance policy may include at least one of: the method has the advantages of highest rate of repurchase, maximized function of the trial products (namely, the maximum influence of the trial product release on users), and three composite strategies (the weighted combination of the first two strategies, the weighting coefficient a can be set, and a is more than 0 and less than 1). If no trial product issuing record exists or only the highest re-purchasing rate is required, the a is considered to be 1; if the dispensing strategy is the most effective trial, a is considered to be 0.
Determining the value of the weighting coefficient a according to the issuing strategy, and further determining the evaluation index of the user; the trial supply distribution crowd can be determined according to the evaluation index and the number of the trial supplies of each user. For example, if there are 300 test supplies, the first 300 users can be screened as the population for distributing the test supplies in the order of the evaluation index from high to low.
Optionally, determining a trial supply distribution population according to the evaluation index of each user includes: selecting the first (n + b) users as trial supply distribution crowds according to the sequence of the evaluation indexes from high to low; wherein n represents the number of the test articles; b represents a constant. In some cases, the precondition for distributing the trial products is that the user orders again, and in order to ensure that the trial products can be completely distributed, b more users need to be screened out on the basis of the theoretical distribution amount n. The specific value of b is an empirical value, the default value is 0, and other values, such as 10, 50, and 10% n (when 10% n is not an integer, it can be rounded up or down), may also be assigned to b.
According to yet another aspect of an embodiment of the present invention, there is provided an apparatus for determining a group of trial items.
Fig. 2 is a schematic diagram of the main blocks of the apparatus for determining a trial supply distribution population according to the embodiment of the present invention. As shown in fig. 2, the apparatus 200 for determining a group of trial products according to the embodiment of the present invention includes:
the probability generation module 201 is used for acquiring behavior data of each user, including the data of the test article receiving records; setting the value of the recorded data of the test article to be a first numerical value, inputting the behavior data into a trained probability model, and determining a first purchase probability under the condition of receiving the test article; setting the value of the recorded data of the taken test article as a second numerical value, inputting the behavior data into the trained probability model, and determining a second purchase probability under the condition of not taking the test article;
the user evaluation module 202 determines an evaluation index of each user according to the first purchase probability and the second purchase probability;
the user screening module 203 determines the trial supply distribution crowd according to the evaluation index of each user.
Optionally, the user evaluation module determines the evaluation index of each user according to the following formula according to the first purchase probability and the second purchase probability:
t＝a×p1+(1-a)×(p1-p0)+
in the formula, t represents an evaluation index; p1 represents a first probability of purchase under the condition of drawing a test article; p0 represents a second probability of purchase without picking up the test article; (p1-p0)+Max (0, (p1-p 0)); a represents a weighting coefficient.
Optionally, the apparatus for determining a group of trial products issuing persons according to the embodiment of the present invention further includes: a policy issuing module (not shown in the figure) for issuing a trial supply issuing request to the user evaluation module, wherein the trial supply issuing request includes: the number of trial products and the issuing strategy; the user evaluation module is further configured to: and determining the value of the weighting coefficient a according to the issuing strategy.
Optionally, the user screening module determines the trial supply distribution crowd according to the evaluation index of each user, including:
selecting the first (n + b) users as trial supply distribution crowds according to the sequence of the evaluation indexes from high to low; wherein n represents the number of the test articles; b represents a constant.
Optionally, before the probability generation module inputs the behavior data into the trained probability model, the user evaluation module is further configured to: determining that a trained probability model exists; and the number of the first and second groups,
and if the trained probability model does not exist, training the probability model according to the behavior data of each user by a machine learning method.
Optionally, the behavioural data further comprises at least one of: purchase log data, plus shopping cart log data, browsing log data, attention log data.
The method and apparatus for determining a group of trial products according to the embodiments of the present invention are described in detail below with reference to specific embodiments.
The first embodiment is as follows:
a user issues a trial supply issuing request through a strategy issuing module, wherein the number of the trial supplies is 10000, the issuing strategy is the maximum repeated purchase rate, and a is 1 at the moment;
after receiving the request issued by the strategy issuing module, the user evaluation module judges whether the probability calculation model of the brand, the category and the commodity exists or not; if not, the probability calculation model of the brand, the product class and the commodity does not exist;
training a model by using a logistic regression algorithm in a probability generation module according to data information such as user portrait, purchase records and purchase browsing records of brands, categories or SKUs (stock keeping units), and release records of the trial products, and respectively marking all the release records of the trial products as 1 to obtain a first probability p1 of purchasing the brands, the categories and the commodities of the user under the condition of releasing the trial products; marking all the trial product issuing records as 0, calculating a second probability p0 of purchasing the brand, the product class and the commodity under the condition of not issuing the trial products, and synchronizing the calculation result to the user evaluation module;
the evaluation index t of the user is calculated in the user evaluation module, wherein t is a multiplied by p1+ (1-a) × (p1-p0)+To obtain t ═ p 1;
after receiving the calculation result of the user evaluation module, the user screening module arranges the calculation result according to the evaluation indexes p1 corresponding to the users from large to small, and selects the former 10000 users as trial supply distribution crowds;
and (6) ending.
Example two:
a user issues a trial supply issuing request through a strategy issuing module, wherein the number of the trial supplies is 10000, the issuing strategy is the maximum repeated purchase rate, and a is 1 at the moment;
after receiving the request issued by the strategy issuing module, the user evaluation module judges that the probability calculation model of the brand, the category and the commodity exists;
corresponding probabilities p0 and p1 are extracted from the user evaluation module, and an evaluation index t of the user is calculated, wherein t is a x p1+ (1-a) x (p1-p0)+To obtain t ═ p 1;
after receiving the calculation result of the user evaluation module, the user screening module arranges the calculation result according to the evaluation indexes p1 corresponding to the users from large to small, and selects the former 10000 users as trial supply distribution crowds;
and (6) ending.
Example three:
a user issues a trial product issuing request through a strategy issuing module, wherein the number of the trial products is 20000, the issuing strategy is that the effect of the trial products is maximized, but the corresponding brands, categories or SKUs have no trial product issuing records, and a is 1 at the moment;
after receiving the request issued by the strategy issuing module, the user evaluation module judges that the probability calculation model of the brand, the category and the commodity exists;
corresponding probabilities p0 and p1 are extracted from the user evaluation module, and an evaluation index t of the user is calculated, wherein t is a x p1+ (1-a) x (p1-p0)+To obtain t ═ p 1;
the user screening module receives the calculation result of the user evaluation module, then arranges the calculation result according to the evaluation indexes p1 corresponding to the users from big to small, and selects the front 20000 users as the trial distribution crowd;
and (6) ending.
Example four:
a user issues a trial supply issuing request through a strategy issuing module, wherein the number of the trial supplies is 10000, the issuing strategy is the maximum effect of the trial supplies, and a is 1;
after receiving the request issued by the strategy issuing module, the user evaluation module judges that the probability calculation model of the brand, the category and the commodity exists;
the corresponding probabilities p0 and p1 are taken out from the user evaluation module, and the evaluation index t of the user is calculated, wherein t is a×p1+(1-a)×(p1-p0)+To obtain t ═ p 1;
after receiving the calculation result of the user evaluation module, the user screening module arranges the calculation result according to the evaluation indexes p1 corresponding to the users from large to small, and selects the former 10000 users as trial supply distribution crowds;
and (6) ending.
Example five:
a user issues a trial supply issuing request through a strategy issuing module, wherein the number of the trial supplies is 10000, the issuing strategy is a composite strategy, and a is 0.1 at the moment;
after receiving the request issued by the strategy issuing module, the user evaluation module judges that the probability calculation model of the brand, the category and the commodity exists;
corresponding probabilities p0 and p1 are extracted from the user evaluation module, and an evaluation index t of the user is calculated, wherein t is a x p1+ (1-a) x (p1-p0)+To obtain t ═ p 1;
after receiving the calculation result of the user evaluation module, the user screening module arranges the calculation result according to the evaluation indexes p1 corresponding to the users from large to small, and selects the former 10000 users as trial supply distribution crowds;
and (6) ending.
According to another aspect of the embodiments of the present invention, there is provided an electronic device for determining a group of trial items.
The electronic equipment for determining the trial supply distribution crowd according to the embodiment of the invention comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method for determining a group of trial items provided by the first aspect of the embodiment of the present invention.
Fig. 3 illustrates an exemplary system architecture 300 for a method of determining a trial product distribution population or an apparatus for determining a trial product distribution population to which embodiments of the present invention may be applied.
As shown in fig. 3, the system architecture 300 may include terminal devices 301, 302, 303, a network 304, and a server 305. The network 304 serves as a medium for providing communication links between the terminal devices 301, 302, 303 and the server 305. Network 304 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal device 301, 302, 303 to interact with the server 305 via the network 304 to receive or send messages or the like. The terminal devices 301, 302, 303 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 301, 302, 303 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 305 may be a server providing various services, such as a background management server (for example only) providing support for shopping-like websites browsed by users using the terminal devices 301, 302, 303. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the method for determining the group of trial products provided by the embodiment of the present invention is generally executed by the server 305, and accordingly, the device for determining the group of trial products is generally disposed in the server 305.
It should be understood that the number of terminal devices, networks, and servers in fig. 3 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 4, a block diagram of a computer system 400 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU)401 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the system 400 are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program performs the above-described functions defined in the system of the present invention when executed by a Central Processing Unit (CPU) 401.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor comprising: the probability generation module is used for acquiring behavior data of each user, including the data of the test article receiving records; setting the value of the recorded data of the test article to be a first numerical value, inputting the behavior data into a trained probability model, and determining a first purchase probability under the condition of receiving the test article; setting the value of the recorded data of the taken test article as a second numerical value, inputting the behavior data into the trained probability model, and determining a second purchase probability under the condition of not taking the test article; the user evaluation module determines the evaluation index of each user according to the first purchase probability and the second purchase probability; and the user screening module is used for determining the trial supply distribution crowd according to the evaluation index of each user. The names of these modules do not constitute a limitation to the module itself in some cases, and for example, the user filtering module may be further described as a "module for determining an evaluation index for each user according to the first purchase probability and the second purchase probability".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: the probability generation module is used for acquiring behavior data of each user, including the data of the test article receiving records; setting the value of the recorded data of the test article to be a first numerical value, inputting the behavior data into a trained probability model, and determining a first purchase probability under the condition of receiving the test article; setting the value of the recorded data of the taken test article as a second numerical value, inputting the behavior data into the trained probability model, and determining a second purchase probability under the condition of not taking the test article; determining an evaluation index of each user according to the first purchase probability and the second purchase probability; and determining the trial supply distribution crowd according to the evaluation index of each user.
According to the technical scheme of the embodiment of the invention, the evaluation index of each user is determined according to the first purchase probability of the user under the condition of receiving the test articles and the second purchase probability of the user under the condition of not receiving the test articles, so that the people who issue the test articles are determined, the defects in the prior art can be avoided, the reasonable evaluation index is given by combining the function of issuing the test articles, and the people who issue the test articles are automatically screened out.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (14)
1. A method for identifying a group of trial products for distribution, comprising:
acquiring behavior data of each user, including receiving test article record data;
setting the value of the recorded data of the received test article as a first numerical value, inputting behavior data into a trained probability model, and determining a first purchase probability under the condition of receiving the test article; setting the value of the recorded data of the received test article as a second numerical value, inputting the behavior data into a trained probability model, and determining a second purchase probability under the condition of not receiving the test article;
and determining the evaluation index of each user according to the first purchase probability and the second purchase probability, and determining the trial supply distribution crowd according to the evaluation index of each user.
2. The method of claim 1, wherein the evaluation index for each user is determined according to the following formula based on the first purchase probability and the second purchase probability:
t＝a×p1+(1-a)×(p1-p0)+
in the formula, t represents an evaluation index; p1 represents a first probability of purchase under the condition of drawing a test article; p0 represents a second probability of purchase without picking up the test article; (p1-p0)+Max (0, (p1-p 0)); a represents a weighting coefficient.
3. The method of claim 2, further comprising: receiving a test article issuing request, wherein the test article issuing request comprises: the number of trial products and the issuing strategy; and determining the value of the weighting coefficient a according to the issuing strategy.
4. The method of claim 1, wherein determining a trial distribution population based on the evaluation index for each user comprises:
selecting the first (n + b) users as trial supply distribution crowds according to the sequence of the evaluation indexes from high to low; wherein n represents the number of the test articles; b represents a constant.
5. The method of claim 1, wherein inputting behavioral data into the trained probabilistic model further comprises: determining that a trained probability model exists; and the number of the first and second groups,
and if the trained probability model does not exist, training the probability model according to the behavior data of each user by a machine learning method.
6. The method of claim 1 or 5, wherein the behavioral data further comprises at least one of: purchase log data, plus shopping cart log data, browsing log data, attention log data.
7. An apparatus for identifying a group of trial participants, comprising:
the probability generation module is used for acquiring behavior data of each user, including the data of the test article receiving records; setting the value of the recorded data of the received test article as a first numerical value, inputting behavior data into a trained probability model, and determining a first purchase probability under the condition of receiving the test article; setting the value of the recorded data of the received test article as a second numerical value, inputting the behavior data into a trained probability model, and determining a second purchase probability under the condition of not receiving the test article;
the user evaluation module determines the evaluation index of each user according to the first purchase probability and the second purchase probability;
and the user screening module is used for determining the trial supply distribution crowd according to the evaluation index of each user.
8. The apparatus of claim 7, wherein the user rating module determines a rating index for each user based on the first purchase probability and the second purchase probability according to the following formula:
t＝a×p1+(1-a)×(p1-p0)+
in the formula, t represents an evaluation index; p1 represents a first probability of purchase under the condition of drawing a test article; p0 represents a second probability of purchase without picking up the test article; (p1-p0)+Max (0, (p1-p 0)); a represents a weighting coefficient.
9. The apparatus of claim 8, further comprising: the strategy issuing module is used for issuing a trial supply issuing request to the user evaluation module, wherein the trial supply issuing request comprises: the number of trial products and the issuing strategy; the user evaluation module is further configured to: and determining the value of the weighting coefficient a according to the issuing strategy.
10. The apparatus of claim 7, wherein the user screening module determines the trial supply distribution population according to the evaluation index of each user, comprising:
selecting the first (n + b) users as trial supply distribution crowds according to the sequence of the evaluation indexes from high to low; wherein n represents the number of the test articles; b represents a constant.
11. The apparatus of claim 7, wherein before the probability generation module enters the behavior data into the trained probability model, the user evaluation module is further to: determining that a trained probability model exists; and the number of the first and second groups,
and if the trained probability model does not exist, training the probability model according to the behavior data of each user by a machine learning method.
12. The apparatus of claim 7 or 11, wherein the behavior data further comprises at least one of: purchase log data, plus shopping cart log data, browsing log data, attention log data.
13. An electronic device for determining a group of trial products issuing persons, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
14. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113313531A (en) * | 2021-06-17 | 2021-08-27 | 浙江锐驰网络科技有限公司 | E-commerce trial evaluation and recommendation system based on user requirements |
| CN116843383A (en) * | 2023-09-01 | 2023-10-03 | 中国人民大学 | Individualized excitation method and device based on counterfactual identification and estimation |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6021362A (en) * | 1998-02-17 | 2000-02-01 | Maggard; Karl J. | Method and apparatus for dispensing samples and premiums |
| US8364520B1 (en) * | 2008-08-15 | 2013-01-29 | Freeosk Marketing, Inc. | Method for measuring effectiveness of sampling activity and providing pre-market product feedback |
| CN103679497A (en) * | 2012-09-20 | 2014-03-26 | 阿里巴巴集团控股有限公司 | Trial commodity distributing method and device |
| CN106846041A (en) * | 2016-12-26 | 2017-06-13 | 携程计算机技术（上海）有限公司 | The distribution method and system of reward voucher |
| CN107016569A (en) * | 2017-03-21 | 2017-08-04 | 聚好看科技股份有限公司 | The targeted customer's account acquisition methods and device of a kind of networking products |
| CN107545452A (en) * | 2016-06-27 | 2018-01-05 | 百度在线网络技术（北京）有限公司 | A kind of resource put-on method and device |
-
2018
- 2018-08-17 CN CN201810942532.5A patent/CN110838019A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6021362A (en) * | 1998-02-17 | 2000-02-01 | Maggard; Karl J. | Method and apparatus for dispensing samples and premiums |
| US8364520B1 (en) * | 2008-08-15 | 2013-01-29 | Freeosk Marketing, Inc. | Method for measuring effectiveness of sampling activity and providing pre-market product feedback |
| CN103679497A (en) * | 2012-09-20 | 2014-03-26 | 阿里巴巴集团控股有限公司 | Trial commodity distributing method and device |
| CN107545452A (en) * | 2016-06-27 | 2018-01-05 | 百度在线网络技术（北京）有限公司 | A kind of resource put-on method and device |
| CN106846041A (en) * | 2016-12-26 | 2017-06-13 | 携程计算机技术（上海）有限公司 | The distribution method and system of reward voucher |
| CN107016569A (en) * | 2017-03-21 | 2017-08-04 | 聚好看科技股份有限公司 | The targeted customer's account acquisition methods and device of a kind of networking products |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113313531A (en) * | 2021-06-17 | 2021-08-27 | 浙江锐驰网络科技有限公司 | E-commerce trial evaluation and recommendation system based on user requirements |
| CN113313531B (en) * | 2021-06-17 | 2022-11-11 | 浙江良创信息科技有限公司 | E-commerce trial evaluation and recommendation system based on user requirements |
| CN116843383A (en) * | 2023-09-01 | 2023-10-03 | 中国人民大学 | Individualized excitation method and device based on counterfactual identification and estimation |
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