CN111582895A - Product gender preference prediction method, system, device and storage medium - Google Patents

Product gender preference prediction method, system, device and storage medium Download PDF

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CN111582895A
CN111582895A CN201910117473.2A CN201910117473A CN111582895A CN 111582895 A CN111582895 A CN 111582895A CN 201910117473 A CN201910117473 A CN 201910117473A CN 111582895 A CN111582895 A CN 111582895A
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张人方
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Beijing Jingdong Century Trading Co Ltd
Beijing Jingdong Shangke Information Technology Co Ltd
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Abstract

The invention discloses a method, a system, equipment and a storage medium for predicting product type preference. The method comprises the following steps: acquiring user behavior data; the user behavior data includes: browsing a time series of products; inputting user behavior data into an offline gender prediction model; the output parameters of the offline gender prediction model include: a gender number of the product; when the gender numerical value output by the off-line gender prediction model is judged to be in a preset range, inputting the time sequence into the real-time gender prediction model; the output parameters of the real-time gender prediction model comprise: a gender number of the product; and predicting the gender attribute of the product according to the gender numerical value output by the real-time gender prediction model. The method and the device utilize the stable offline gender prediction model and the high-flexibility real-time gender prediction model to predict the gender preference of the user to the product, are stable, and can accurately predict the current preference of the user when the preference tendency of the user changes.

Description

Product gender preference prediction method, system, device and storage medium
Technical Field
The invention relates to the technical field of internet, in particular to a method, a system, equipment and a storage medium for predicting product type preference.
Background
In recent years, with the rapid development of e-commerce platforms, product personalized recommendation technology is also greatly improved. The core content of the product recommendation system is a recommendation method, namely how to accurately recommend products consistent with the user interests to the user.
Currently, a product recommendation method mainly includes content-based recommendation, in which a system acquires a product consistent with a user's purchase interest from product characteristic information (product price, product type, product order amount, etc.) by a machine learning method. The product gender attribute is one of important characteristics affecting the effect of the recommendation system, and if a woman dress is recommended to a male user and a man pack is recommended to a female user, the shopping experience of the user is seriously affected, so that accurate prediction of the gender preference of the user on the product is one of the key factors determining the quality of the recommendation system.
In the prior art, historical behavior data of a user merchant detailed page is used as a training sample, a gender prediction model is obtained by the training model, click behavior data of a browsed commodity page of a target user in a period of time is collected and input into the gender prediction model, so that gender preference of the target user on a product is predicted. Since the gender preference of the user for the product is not one-layer constant, when the user changes the preference tendency, the gender prediction model cannot accurately predict the current preference of the user. And for the gender prediction model, the model needs to be retrained at a proper time point, so that the current application scene can be fit in time, and the use is very inconvenient.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a product gender preference prediction method, a system, equipment and a storage medium, aiming at overcoming the defects that the accuracy of predicting the gender preference of a user to a product by using a gender prediction model in the prior art is not high and retraining is often required.
The embodiment of the invention solves the technical problems through the following technical scheme:
a method of predicting product gender preferences, the method comprising:
acquiring user behavior data; the user behavior data includes: browsing a time series of products; the product is provided with an identifier representing gender attribute;
inputting the user behavior data into an offline gender prediction model; the output parameters of the off-line gender prediction model comprise: a gender number of the product; the gender value characterizes a gender attribute of the product;
judging whether the gender numerical value output by the off-line gender prediction model is in a preset range or not;
if yes, inputting the time sequence into a real-time gender prediction model; the output parameters of the real-time gender prediction model comprise: a gender number of the product;
and predicting the gender attribute of the product according to the gender numerical value output by the real-time gender prediction model.
Preferably, when the judgment result is negative, the gender attribute of the product is predicted according to the gender numerical value output by the off-line gender prediction model.
Preferably, the output parameters of the real-time gender prediction model further include: a preference degree;
the calculation formula of the preference degree is as follows:
Figure BDA0001970696880000021
Figure BDA0001970696880000022
wherein RV represents a preference; i is more than or equal to 1 and less than or equal to k; k represents the number of products in the time series; GenderValueiCharacterizing a gender attribute of the ith product; t is tiRepresenting the decay function, α representing the decay parameters.
Preferably, the step of obtaining user behavior data specifically includes:
acquiring the user behavior data based on Kafka;
and/or, after the step of predicting the gender attribute of the product, further comprising:
the prediction result is stored in Redis.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of predicting productivity preferences as in any one of the above when executing the computer program.
A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of predicting product preference as set forth in any one of the preceding claims.
A system for predicting productivity preferences, the system comprising:
the data acquisition module is used for acquiring user behavior data; the user behavior data includes: browsing a time series of products; the product is provided with an identifier representing gender attribute;
a calculation module for inputting the user behavior data into an offline gender prediction model; the output parameters of the off-line gender prediction model comprise: a gender number of the product; the gender value characterizes a gender attribute of the product;
the judgment module is used for judging whether the gender numerical value output by the off-line gender prediction model is in a preset range or not, and calling the calculation module when the gender numerical value output by the off-line gender prediction model is judged to be in the preset range;
the computing module is further configured to input the time series into a real-time gender prediction model; the output parameters of the real-time gender prediction model comprise: a gender number of the product;
and the prediction module is used for predicting the gender attribute of the product according to the gender numerical value output by the real-time gender prediction model.
Preferably, when the judgment module judges that the product is not a product, the prediction module is further configured to predict the gender attribute of the product according to the gender numerical value output by the offline gender prediction model.
Preferably, the output parameters of the real-time gender prediction model further include: a preference degree;
the calculation formula of the preference degree is as follows:
Figure BDA0001970696880000031
Figure BDA0001970696880000032
wherein RV represents a preference; i is more than or equal to 1 and less than or equal to k; k represents the number of products in the time series; GenderValueiCharacterizing a gender attribute of the ith product; t is tiRepresenting the decay function, α representing the decay parameters.
Preferably, the data obtaining module is specifically configured to obtain the user behavior data based on Kafka;
and/or, the prediction system further comprises: a storage module;
the storage module is used for storing the prediction result in Redis.
The embodiment of the invention has the positive improvement effects that: the embodiment of the invention predicts the gender preference of the user to the product by using the stable off-line gender prediction model and the high-flexibility real-time gender prediction model, is stable, can accurately predict the current preference of the user when the preference tendency of the user is changed, can fit the current application scene without retraining the model, and is convenient to use.
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Fig. 1 is a flowchart of a method for predicting gender preference of a product according to embodiment 1 of the present invention.
Fig. 2 is a schematic structural diagram of an electronic product according to embodiment 2 of the present invention.
Fig. 3 is a block diagram of a system for predicting gender preference of a product according to embodiment 4 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
The embodiment provides a method for predicting gender preference of a user on a product, and as shown in fig. 1, the method comprises the following steps:
step 101, user behavior data is obtained.
Wherein the user behavior data comprises: user ID, SKU (stock keeping unit), time series of products viewed (clicked) by the user, etc. The time sequence of browsing the products by the user comprises a gender attribute array of k (but not limited to 50) products with browsing time closest to the current time; for each product, a logo is provided, which is used to characterize the gender attribute of the product, for example, 0, 0.5,1 for male, neutral (both male and female) and female products, respectively.
In this embodiment, streaming data collection is specifically used to extract fields required for predicting user preferences, such as user ID, SKU, time series of products viewed by the user, and the like, from a kafka (an open source streaming platform) log.
Step 102, inputting the user behavior data into an offline gender prediction model.
The output parameters of the off-line gender prediction model comprise a gender numerical value and a preference degree of the product. The gender value characterizes a gender attribute of a product that the user may purchase at the next time. The preference degree represents the possibility of the user purchasing the product with the gender attribute predicted by the offline gender prediction model, for example, the gender value of the product output by the offline gender prediction model is 0.3 (representing male products), the preference degree is 0.88, and the probability representing that the user is predicted to prefer the male products at the next moment is 0.88.
The training process for the offline gender prediction model is briefly described as follows:
a basic table of user merchant detailed page browsing (clicking) behaviors in units of days or hours or weeks is made, and a time series of user history (for example, the previous day) browsing products is extracted as training data and test data. Specifically, by using a sliding window method, the attribute data of the product browsed most recently is used as test data, and the attribute data of the product browsed before is used as training data by sliding windows with different sizes (behavior data for 3 times, 5 times and 10 times). Calculating the sex ratio in each sequence to perform feature extraction, and if 5 men, 3 women and 2 neutrality exist in ten times, the feature extraction is male 5/10, female 3/10 and neutrality 2/10. And (3) carrying out a multi-class logistic regression (multiclass logistic regression) training model according to the feature extraction result to obtain an offline gender prediction model.
And 103, judging whether the gender numerical value output by the off-line gender prediction model falls into a preset range.
If the judgment result shows that the gender preference of the user predicted by the offline gender prediction model to the product is not obvious, the prediction result is not ideal, and further calculation is needed, step 104 is executed, and the preference degree output by the real-time gender prediction model is replaced by the preference degree output by the offline gender prediction model. If not, the prediction result is ideal, and no further calculation is needed, then step 105 is executed.
In this embodiment, the preset range is obtained according to multiple random simulation experiments, and different preset ranges can be set in different use scenarios, for example, the preset range in this embodiment is [0.4,0.6 ].
And 104, inputting the time sequence of the browsed products into a real-time gender prediction model.
Wherein, the output parameters of the real-time gender prediction model comprise: gender number, preference, confidence, etc. of the product.
The calculation formula of the preference degree is as follows:
Figure BDA0001970696880000051
Figure BDA0001970696880000052
wherein RV represents a preference; range (1,50) represents the number of gender attribute arrays; i is more than or equal to 1 and less than or equal to 50; GenderValueiCharacterizing the gender attribute of the ith product, and respectively representing a male product, a neutral product (both male and female can use) and a female product by 0, 0.5 and 1; t is tiRepresenting a decay function, α representing a decay parameter, α∈ (0.5,1), α the closer to 1 the greater the magnitude of the decay, since the time to browse the product is more current, the gender of the product may be preferred by the userThe stronger the performance, the weaker the longer the time. Therefore, the decay function gives higher weight to the product at the latest current moment of the browsing time, and the weight of the product in the future is gradually decreased.
And 105, predicting the gender attribute of the product according to the gender numerical value and outputting a prediction result.
Wherein, the prediction result comprises: gender attribute, preference, confidence coefficient and the like, wherein the preference is the preference output by the real-time gender prediction model.
The formula for calculating the gender attribute of the product is as follows:
Figure BDA0001970696880000061
wherein f (sender) characterizes a gender attribute function; p is a radical ofmale(confidence of male product) represents the proportion of products with gender attribute male in the time series of browsing products; buildermaleIndicating a gender attribute as male; p is a radical offemale(confidence of feminine product) represents the proportion of products in the time series of viewed products for which the gender attribute is female; builderfemaleIndicating a gender attribute as female; p is a radical ofmiddle(confidence of neutral products) represents the proportion of products with neutral gender attributes in the time series of viewed products; buildermiddleIndicating that the gender attribute is neutral.
It should be noted that, it is assumed that a user clicks and browses 10 products respectively, wherein the products include 4 male products, 4 female products, and 2 neutral products; then p ismale＝0.4，pfemale＝0.4，pmiddleAnd (3) since the number of clicks of the men and women of the user is leveled, which indicates that the user has no special gender preference, the gender attribute of the user is predicted to be neutral.
And step 106, storing the prediction result in Redis.
In this embodiment, the prediction model is arranged in a Storm cluster (a server cluster of a master-slave structure). During prediction, Kafka sends behavior data of a target user to a Storm cluster for data processing and model prediction, and finally prediction results are stored in Redis (a storage system).
In the embodiment, the gender preference of the user to the product is predicted by using the stable offline gender prediction model and the high-flexibility real-time gender prediction model, so that the gender preference of the user to the product is stable, the current preference of the user can be accurately predicted when the user changes the preference tendency, the model is not required to be retrained, the current application scene can be fitted, and the use is convenient.
Example 2
Fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, which shows a block diagram of an exemplary electronic device 90 suitable for implementing an embodiment of the present invention. The electronic device 90 shown in fig. 2 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 2, the electronic device 90 may take the form of a general purpose computing device, which may be a server device, for example. The components of the electronic device 90 may include, but are not limited to: the at least one processor 91, the at least one memory 92, and a bus 93 that connects the various system components (including the memory 92 and the processor 91).
The bus 93 includes a data bus, an address bus, and a control bus.
Memory 92 may include volatile memory, such as Random Access Memory (RAM)921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.
Memory 92 may also include a program tool 925 (or utility) having a set (at least one) of program modules 924, such program modules 924 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The processor 91 executes various functional applications and data processing, such as a method for predicting gender preference of a product provided in embodiment 1 of the present invention, by executing a computer program stored in the memory 92.
The electronic device 90 may also communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 95. Also, the model-generated electronic device 90 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via a network adapter 96. As shown, the network adapter 96 communicates with the other modules of the model-generated electronic device 90 via a bus 93. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating electronic device 90, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 3
The present embodiment provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor, implements the steps of the method for predicting gender preferences of a product provided in embodiment 1.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps of the method for predicting product preference as described in embodiment 1, when the program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may be executed entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
Example 4
The present embodiment provides a system for predicting gender preference of a user on a product, as shown in fig. 3, the system includes: the device comprises a data acquisition module 1, a calculation module 2, a judgment module 3, a prediction module 4 and a storage module 5.
The data acquisition module 1 is used for acquiring user behavior data. Wherein the user behavior data comprises: user ID, SKU (stock keeping unit), time series of products viewed (clicked) by the user, etc. The time sequence of browsing the products by the user comprises a gender attribute array of k (but not limited to 50) products with browsing time closest to the current time; for each product, a logo is provided, which is used to characterize the gender attribute of the product, for example, 0, 0.5,1 for male, neutral (both male and female) and female products, respectively.
In this embodiment, the data obtaining module 1 is specifically configured to obtain user behavior data based on Kafka, that is, extracting fields required for predicting user preferences, such as user ID, SKU, time series of products browsed by a user, and the like, from Kafka logs by using streaming data collection.
The calculation module 2 is used for inputting the user behavior data into the offline gender prediction model. The output parameters of the off-line gender prediction model comprise a gender numerical value and a preference degree of the product. The gender value characterizes a gender attribute of a product that the user may purchase at the next time. The preference degree represents the possibility of the user purchasing the product with the gender attribute predicted by the offline gender prediction model, for example, the gender value of the product output by the offline gender prediction model is 0.3 (representing male products), the preference degree is 0.88, and the probability representing that the user is predicted to prefer the male products at the next moment is 0.88. The training process of the offline gender prediction model is similar to the model training process in the prediction method shown in embodiment 1, and is not described here again.
The judgment module 3 is used for judging whether the gender numerical value output by the off-line gender prediction model is in a preset range; if the judgment result is negative, the prediction result is ideal, the prediction module 4 is called to predict the gender attribute of the product according to the gender numerical value output by the off-line gender prediction model and output the prediction result; if the judgment result is yes, the calculation module 2 is called, and the judgment result shows that the gender preference of the user predicted by the offline gender prediction model to the product is not obvious, the prediction result is not ideal, and further calculation is needed.
In this embodiment, the preset range is obtained according to multiple random simulation experiments, and different preset ranges can be set in different use scenarios, for example, the preset range in this embodiment is [0.4,0.6 ].
The calculation module 2 is further configured to input the time sequence of the browsing product into the real-time gender prediction model, and call the prediction module 4 to predict the gender attribute of the product according to the gender numerical value output by the real-time gender prediction model and output a prediction result. Wherein, the output parameters of the real-time gender prediction model comprise: the gender numerical value, the preference degree, the confidence coefficient and the like of the product, wherein the preference degree is the preference degree output by the real-time gender prediction model.
The calculation formula of the preference degree is as follows:
Figure BDA0001970696880000101
Figure BDA0001970696880000102
wherein RV represents a preference; range (1,50) represents the number of category attribute arrays in time series; i is more than or equal to 1 and less than or equal to 50; GenderValueiCharacterizing the gender attribute of the ith product, and respectively representing a male product, a neutral product (both male and female can use) and a female product by 0, 0.5 and 1; t is tiRepresenting the decay function, α representing the decay parameter, α∈ (0.5,1), α, the closer to 1, the greater the decay magnitude, because of the parallelThe more current the time to visit the product, the more likely it is that the user prefers the gender of the product, and the more recent the time, the less likely it is. Therefore, the decay function gives higher weight to the product at the latest current moment of the browsing time, and the weight of the product in the future is gradually decreased.
In this embodiment, the prediction result output by the prediction module 4 includes: gender attribute, preference, confidence, etc.
The formula for calculating the gender attribute of the product is as follows:
Figure BDA0001970696880000103
wherein f (sender) characterizes a gender attribute function; p is a radical ofmale(confidence of male product) represents the proportion of products with gender attribute male in the time series of browsing products; buildermaleIndicating a gender attribute as male; p is a radical offemale(confidence of feminine product) represents the proportion of products in the time series of viewed products for which the gender attribute is female; builderfemaleIndicating a gender attribute as female; p is a radical ofmiddle(confidence of neutral products) represents the proportion of products with neutral gender attributes in the time series of viewed products; buildermiddleIndicating that the gender attribute is neutral.
The storage module 5 is configured to store the prediction result in Redis.
In this embodiment, the prediction model is arranged in a Storm cluster (a server cluster of a master-slave structure). During prediction, Kafka sends behavior data of a target user to a Storm cluster for data processing and model prediction, and finally prediction results are stored in Redis (a storage system).
In the embodiment, the gender preference of the user to the product is predicted by using the stable offline gender prediction model and the high-flexibility real-time gender prediction model, so that the gender preference of the user to the product is stable, the current preference of the user can be accurately predicted when the user changes the preference tendency, the model is not required to be retrained, the current application scene can be fitted, and the use is convenient.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.

Claims (10)

1. A method for predicting product gender preference, which is characterized in that the method for predicting gender preference comprises the following steps:
acquiring user behavior data; the user behavior data includes: browsing a time series of products; the product is provided with an identifier representing gender attribute;
inputting the user behavior data into an offline gender prediction model; the output parameters of the off-line gender prediction model comprise: a gender number of the product; the gender value characterizes a gender attribute of the product;
judging whether the gender numerical value output by the off-line gender prediction model is in a preset range or not;
if yes, inputting the time sequence into a real-time gender prediction model; the output parameters of the real-time gender prediction model comprise: a gender number of the product;
and predicting the gender attribute of the product according to the gender numerical value output by the real-time gender prediction model.
2. The method of claim 1, wherein if the determination is negative, the gender attribute of the product is predicted according to the gender value outputted from the offline gender prediction model.
3. The method of predicting product gender preferences of claim 1 wherein the output parameters of said real-time gender prediction model further comprise: a preference degree;
the calculation formula of the preference degree is as follows:
Figure FDA0001970696870000011
Figure FDA0001970696870000012
wherein RV represents a preference; i is more than or equal to 1 and less than or equal to k; k represents the number of products in the time series; GenderValueiCharacterizing a gender attribute of the ith product; t is tiRepresenting the decay function, α representing the decay parameters.
4. The method for predicting the product property preferences of any one of claims 1-3, wherein the step of obtaining the user behavior data specifically comprises:
acquiring the user behavior data based on Kafka;
and/or, after the step of predicting the gender attribute of the product, further comprising:
the prediction result is stored in Redis.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of predicting productivity preferences of any of claims 1-4 when executing the computer program.
6. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of predicting product preference according to any one of claims 1 to 4.
7. A system for predicting productivity preferences, the system comprising:
the data acquisition module is used for acquiring user behavior data; the user behavior data includes: browsing a time series of products; the product is provided with an identifier representing gender attribute;
a calculation module for inputting the user behavior data into an offline gender prediction model; the output parameters of the off-line gender prediction model comprise: a gender number of the product; the gender value characterizes a gender attribute of the product;
the judgment module is used for judging whether the gender numerical value output by the off-line gender prediction model is in a preset range or not, and calling the calculation module when the gender numerical value output by the off-line gender prediction model is judged to be in the preset range;
the computing module is further configured to input the time series into a real-time gender prediction model; the output parameters of the real-time gender prediction model comprise: a gender number of the product;
and the prediction module is used for predicting the gender attribute of the product according to the gender numerical value output by the real-time gender prediction model.
8. The system of claim 7, wherein the prediction module is further configured to predict gender attributes of the product according to the gender values outputted from the offline gender prediction model when the determination module determines no.
9. The system for predicting product gender preferences of claim 7 wherein the output parameters of said real-time gender prediction model further comprises: a preference degree;
the calculation formula of the preference degree is as follows:
Figure FDA0001970696870000021
Figure FDA0001970696870000022
wherein RV represents a preference; i is more than or equal to 1 and less than or equal to k; k represents the number of products in the time series; GenderValueiCharacterizing a gender attribute of the ith product; t is tiRepresenting the decay function, α representing the decay parameters.
10. The system for predicting product suitability preferences according to any one of claims 7-9 wherein the data acquisition module is specifically configured to acquire the user behavior data based on Kafka;
and/or, the prediction system further comprises: a storage module;
the storage module is used for storing the prediction result in Redis.
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