CN112950320B - Method, device and electronic device for automatically logging goods on and off the line based on machine self-learning - Google Patents
Method, device and electronic device for automatically logging goods on and off the line based on machine self-learning Download PDFInfo
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Abstract
本发明提供一种基于机器自学习的商品自动上下线方法、装置、电子设备和记录介质，方法包括：建立商品信息数据库，用于存储商品基本信息、商品表现数据和商品评分，该商品评分是用于评价商品上线销售的适合度的指标；建立商品上下线推荐模型，模型基于机器自学习算法，能够根据历史商品基本信息和历史商品表现数据定期计算并更新商品数据库中商品的商品评分；根据预定的商品上下线规则在在线购物平台自动上线或下线商品，商品上下线规则与商品评分相关联。本发明能够更准确的评定购物平台上销售的商品是否受用户喜欢，将人气更高的商品自动优先上线供用户购买，提升用户体验和对购物平台的满意度，无需人工操作，提高效率，节省人力和成本。
The present invention provides a method, device, electronic device and recording medium for automatically listing and unlisting goods based on machine self-learning, the method comprising: establishing a product information database for storing basic product information, product performance data and product ratings, the product rating being an indicator for evaluating the suitability of products for online sales; establishing a product listing and unlisting recommendation model, the model being based on a machine self-learning algorithm, being able to regularly calculate and update the product ratings of the products in the product database based on historical basic product information and historical product performance data; automatically listing or unlisting goods on an online shopping platform according to predetermined product listing and unlisting rules, the product listing and unlisting rules being associated with the product ratings. The present invention can more accurately assess whether the goods sold on the shopping platform are liked by users, automatically prioritize the listing of more popular goods for users to purchase, improve user experience and satisfaction with the shopping platform, without the need for manual operation, improve efficiency, and save manpower and costs.
Description
Technical Field
The invention belongs to the technical field of network sales, and particularly relates to a method, a device and electronic equipment for automatically loading and unloading commodities based on machine self-learning.
Background
With the rise of shopping modes of pre-purchasing commodities by using a network or telephone platform, consumers can easily purchase the purchased commodities without going out, and with the expansion of the scale of the network shopping platform, the types and the quantity of the sold commodities are rapidly increased. The problem is that some products are sold faster and some are slower. To ensure the heat of the online commodity, the online commodity needs to be regularly or irregularly adjusted. At present, a statistics person searches according to the sales conditions of the commodities by utilizing a background system of the online shopping platform so as to obtain the sales condition ordering of the commodities, and then selects the on-line and off-line commodities according to the ordering.
The mode consumes a great deal of time and energy of statistics staff due to the adoption of a manual statistics mode. Thus, improvements in the art are needed.
Disclosure of Invention
First, the technical problem to be solved
The invention aims to solve the problem of low efficiency of on-line and off-line commodities on an on-line shopping platform in the prior art.
(II) technical scheme
In order to solve the technical problems, an aspect of the present invention provides a method for automatically loading and unloading commodities based on machine self-learning, for an online shopping platform, which is characterized in that the method comprises the following steps:
Establishing a commodity information database, wherein the database is used for storing commodity basic information, commodity expression data and commodity scores, and the commodity scores are indexes for evaluating the suitability of commodity online sales;
Establishing an online and offline commodity recommendation model, wherein the model is based on a machine self-learning algorithm, and can periodically calculate and update commodity scores of commodities in the commodity database according to historical commodity basic information and historical commodity expression data;
And automatically uploading or downloading commodities on the online shopping platform according to a preset commodity online-offline rule, wherein the commodity online-offline rule is associated with the commodity score.
According to a preferred embodiment of the present invention, the online shopping platform includes a merchant that hosts the platform;
the method further includes obtaining the merchandise basic information from the merchant.
According to a preferred embodiment of the present invention, the commodity performance data includes commodity heat data;
The method further includes tracking network data associated with the commodity and calculating commodity heat data of the commodity based on the network data.
According to a preferred embodiment of the present invention, the network data related to the commodity includes at least one of search data, dotting data, message data, and advertisement data.
According to a preferred embodiment of the invention, the method further comprises:
Generating a heat commodity recommendation table according to the commodity heat data;
And automatically feeding back a commodity list which is contained in the hot commodity recommendation table but not in the commodity information database to the merchant which is resident in the platform, and requesting the merchant to provide attribute information of the commodity.
According to a preferred embodiment of the invention, the method further comprises:
And establishing a commodity pricing model based on machine self-learning, and generating commodity online prices according to at least one of commodity basic information, commodity performance data and commodity scores by the commodity pricing model when commodities are automatically online on the online shopping platform.
According to a preferred embodiment of the present invention, the automatic on-line or off-line commodity on the on-line shopping platform according to a predetermined commodity on-line and off-line rule further comprises:
And the online shopping platform is used for online commodity according to the commodity grading sequence from high to low.
The second aspect of the present invention provides a device for automatically loading and unloading commodities based on machine self-learning, comprising:
The information storage module is used for establishing a commodity information database which is used for storing commodity basic information, commodity expression data and commodity scores, wherein the commodity scores are indexes for evaluating the suitability of commodity online sales;
The scoring calculation module is used for establishing an online and offline commodity recommendation model, and the model is based on a machine self-learning algorithm and can calculate and update commodity scores of commodities in the commodity database regularly according to the historical commodity basic information and the historical commodity expression data;
and the online and offline control module is used for automatically uploading or downloading the commodity on the online shopping platform according to a preset commodity online and offline rule, and the commodity online and offline rule is associated with the commodity score.
A third aspect of the invention proposes an electronic device comprising a processor and a memory for storing a computer executable program, which processor performs the method when the computer program is executed by the processor.
The fourth aspect of the present invention also proposes a computer readable medium storing a computer executable program, which when executed, implements the method.
(III) beneficial effects
According to the commodity information and commodity online-offline recommendation method, a commodity information database and a commodity online-offline recommendation model are established, commodity scores are automatically calculated according to commodity information and commodity heat stored in the database by the model, and online and offline of commodities on a shopping platform are automatically adjusted according to the scores and preset rules. Whether the commodities sold on the shopping platform are liked by the user or not can be accurately assessed, the commodities with higher popularity are automatically and preferentially put on line for the user to purchase, the unsatisfied commodities of the user are put off line, user experience and satisfaction degree of the shopping platform are improved, manual operation is not needed, efficiency is improved, and manpower and cost are saved.
Drawings
FIG. 1 is a schematic diagram of an automatic commodity online and offline application scenario based on machine self-learning;
FIG. 2 is a flow chart of a method for automatically placing and removing items on-line based on machine self-learning in accordance with one embodiment of the present invention;
FIG. 3 is a schematic diagram of an automatic on-line and off-line device for a commodity based on machine self-learning according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the structure of an electronic device of one embodiment of the invention;
Fig. 5 is a schematic diagram of a computer-readable recording medium of an embodiment of the present invention.
Detailed Description
The present invention will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent.
In describing particular embodiments, specific details of construction, performance, effects, or other features are set forth in order to provide a thorough understanding of the embodiments by those skilled in the art. It is not excluded that one skilled in the art may implement the present invention in a particular case in a solution that does not include the structures, properties, effects, or other characteristics described above.
The flow diagrams in the figures are merely exemplary flow illustrations and do not represent that all of the elements, operations, and steps in the flow diagrams must be included in the aspects of the invention, nor that the steps must be performed in the order shown in the figures. For example, some operations/steps in the flowcharts may be decomposed, some operations/steps may be combined or partially combined, etc., and the order of execution shown in the flowcharts may be changed according to actual situations without departing from the gist of the present invention.
The block diagrams in the figures generally represent functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and/or processing unit means and/or microcontroller means.
The same reference numerals in the drawings denote the same or similar elements, components or portions, and thus repeated descriptions of the same or similar elements, components or portions may be omitted hereinafter. It will be further understood that, although the terms first, second, third, etc. may be used herein to describe various devices, elements, components or portions, these devices, elements, components or portions should not be limited by these terms. That is, these phrases are merely intended to distinguish one from the other. For example, a first device may also be referred to as a second device without departing from the spirit of the invention. Furthermore, the term "and/or," "and/or" is meant to include all combinations of any one or more of the items listed.
In order to solve the technical problems, the invention provides a commodity automatic online and offline method based on machine self-learning, which is mainly executed by an online and offline system, wherein the online and offline system comprises software and hardware installed in a server. Including but not limited to a single server, a server cluster, a distributed server, a cloud architecture based server cluster, and the like.
Fig. 1 is a schematic diagram of an automatic online and offline application scenario of a commodity based on machine self-learning, as shown in fig. 1, a merchant enters an online shopping platform through a merchant configuration module and provides basic information of the commodity to be put on the shelf, a commodity information database is used for storing basic information of the commodity provided by each merchant and transmitting the basic information to an online and offline system, the online and offline system can only calculate commodity scores according to commodity information and commodity heat, and determines which commodities can be put on line according to online and offline rules and scores, and feeds the commodity information of the commodity to a commodity information database in a list form through an online and offline recommendation module, the online shopping platform automatically carries out online or offline operation on the commodity on the list, ensures that the commodity on line in the shopping platform is a commodity with high quality and high heat, enables the commodity with higher popularity to be put on line for users to purchase, and improves user experience and satisfaction to the shopping platform.
FIG. 2 is a flow chart of a method for automatically placing and removing items on-line based on machine self-learning according to an embodiment of the present invention. As shown in fig. 2, the method includes:
S101, establishing a commodity information database, wherein the database is used for storing commodity basic information, commodity expression data and commodity scores, and the commodity scores are indexes for evaluating the suitability of commodity online sales.
Specifically, the merchant can voluntarily enter the online shopping platform, if the merchant wants to place an online commodity on the online shopping platform after entering, commodity basic information can be submitted to the online shopping platform, the online shopping platform is provided with a commodity information database, commodity basic information submitted by the merchant is stored, the performance data and commodity scores of the commodity are obtained on the online shopping platform and other online shopping platforms according to the commodity basic information, the commodity basic information comprises information such as commodity names, commodity types, commodity appearance pictures, commodity trademarks, commodity production places, commodity prices provided by the merchant and the like, the commodity performance data comprises heat data of the commodity, commodity heat performance forms are commodity sales quantity, commodity sales amount, commodity click quantity, commodity message or evaluation quantity and the like, the commodity can be completely matched in the online shopping platform and other online shopping platforms on the network, or commodity heat information corresponding to the commodity with the coincidence degree being larger than a preset coincidence degree threshold is stored, the commodity scores are obtained by referencing the commodity evaluation rate and the public praise of the commodity on each online shopping platform on the basis of the commodity performance data, and whether the commodity is placed on line can be evaluated according to the commodity scores.
S102, establishing a commodity online and offline recommendation model, wherein the model is based on a machine self-learning algorithm, and commodity scores of commodities in the commodity database can be calculated and updated regularly according to historical commodity basic information and historical commodity expression data.
Specifically, the online shopping platform establishes an online and offline commodity recommendation model, the model is based on a machine learning model, basic information of historical online commodities and performance data of the commodities are input into the machine learning model, the historical online commodities comprise the commodities of the online shopping platform and also comprise the commodities of other shopping platforms, each attribute in the basic information of the commodities is used as one-dimensional characteristics, each item of data in the performance data can also be used as one-dimensional characteristics, a corresponding algorithm is set, commodity scores of the commodities are output, and parameters of the machine learning model are adjusted according to actual scores of the historical online commodities and the output score difference values, so that the online and offline commodity recommendation model is obtained.
After the commodity online and offline recommendation model is obtained, commodity information provided by the resident merchant and the acquired commodity expression data are input into the commodity online and offline recommendation model, so that commodity grading of the commodity can be obtained.
In this embodiment, a time period may be preset, for example, a week, and each time period is used to replace the historical commodity as a sample periodically, or the recent online commodity performance data may be used as a sample to update the parameters of the online and offline commodity recommendation model, so that the output result of the online and offline commodity recommendation model is more accurate.
And S103, automatically uploading or downloading commodities on the online shopping platform according to a preset commodity online and offline rule, wherein the commodity online and offline rule is associated with the commodity score.
Specifically, after the commodity online-offline recommendation model is utilized to obtain commodity scores, an online-offline rule may be preset, for example, whether the commodity is online or offline is determined according to a score threshold of the scores, the commodity with the score higher than the score threshold may be online, and the commodity with the score lower than the score threshold may not be online.
The online and offline period can be set, for example, one day or one week, the online commodity is kept online for 24 hours on the shopping platform, the online commodity is automatically offline after 24 hours, the performance data of the commodity in 24 hours are added into the previous performance data, the basic information and the performance data of the commodity are input into the commodity online and offline recommendation model again for scoring, and whether the commodity is online or offline is determined according to the score in the next period. Under the condition, the operation of an online shopping platform can be maintained only by automatically loading and unloading commodities according to rules every day, and the whole process is automatically completed by an online and offline system without manual operation.
In addition, the online and offline system can also generate a hot commodity recommendation table according to the hot data of each commodity in one period, and if the fact that the commodity in the hot commodity recommendation table is not complete in information stored in the commodity information database is detected, the system automatically feeds back a commodity list which is contained in the hot commodity recommendation table but not in the commodity information database to the merchant which is in the platform, requests the merchant to provide attribute information of the commodity, and stores the attribute information in a corresponding position in the commodity information database.
Preferably, the online and offline system further establishes a commodity pricing model based on machine self-learning, takes at least one of basic information of commodities, commodity expression data and commodity scores as input of the pricing model, can set different weights for the three types of characteristics according to the types of the commodities, inputs the three types of characteristics into the pricing model together after setting the weights, outputs the three types of characteristics as online prices of the commodities, can calculate the relationship between the online prices and the commodity heat through historical online commodity data in advance, calculates the most reasonable price from the relationship, uses the historical commodity as a sample to train the machine learning model, adjusts parameters of the model through the difference between the output online prices and the calculated prices to obtain the pricing model, and is used for determining the price of the new online commodity. Pricing rules may also be added to refine the price of the commodity, e.g., to price higher than the price of the commodity offered by the merchant, but not by a certain proportion.
After pricing the online commodities, the online and offline system sorts the online commodities from high to low according to the scores, and the commodities of each class are online according to the sorted order, so that the commodity with the highest score in each class is preferentially online.
According to the method, a commodity information database and a commodity online-offline recommendation model are established, commodity scores are automatically calculated according to commodity information and commodity heat stored in the database by the model, and online and offline of commodities on a shopping platform are automatically adjusted according to the scores and preset rules. Whether the commodities sold on the shopping platform are liked by the user or not can be accurately assessed, the commodities with higher popularity are automatically and preferentially put on line for the user to purchase, the unsatisfied commodities of the user are put off line, user experience and satisfaction degree of the shopping platform are improved, manual operation is not needed, efficiency is improved, and manpower and cost are saved.
Those skilled in the art will appreciate that all or part of the steps implementing the above-described embodiments are implemented as a program (computer program) executed by a computer data processing apparatus. The above-described method provided by the present invention can be implemented when the computer program is executed. Moreover, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, for example, a magnetic disk or a tape storage array. The storage medium is not limited to a centralized storage, but may be a distributed storage, such as cloud storage based on cloud computing.
The following describes apparatus embodiments of the invention that may be used to perform method embodiments of the invention. The details described in the embodiments of the device according to the invention are to be regarded as additions to the method embodiments described above, and the details not disclosed in the embodiments of the device according to the invention can be realized with reference to the method embodiments described above.
Fig. 3 is a schematic diagram of an automatic commodity feeding/discharging device based on machine self-learning according to an embodiment of the present invention.
As shown in fig. 3, the apparatus 200 includes:
An information storage module 201 for creating a commodity information database for storing commodity basic information, commodity expression data, and commodity scores, which are indexes for evaluating suitability of commodity on-line sales;
The score calculating module 202 is configured to establish an online and offline commodity recommendation model, where the model is based on a machine self-learning algorithm, and is capable of periodically calculating and updating commodity scores of commodities in the commodity database according to historical commodity basic information and historical commodity performance data;
And the online and offline control module 203 is configured to automatically online or offline the commodity on the online shopping platform according to a predetermined commodity online and offline rule, where the commodity online and offline rule is associated with the commodity score.
According to a preferred embodiment of the present invention, the online shopping platform includes a merchant that hosts the platform;
the information storage module 201 further includes a commodity information acquisition unit for acquiring the commodity basic information from the merchant.
According to a preferred embodiment of the present invention, the commodity performance data includes commodity heat data;
the information storage module 201 further includes a commodity heat acquiring unit for tracking network data related to the commodity and calculating commodity heat data of the commodity according to the network data.
According to a preferred embodiment of the present invention, the network data related to the commodity includes at least one of search data, dotting data, message data, and advertisement data.
According to a preferred embodiment of the invention, the device 200 further comprises:
the commodity recommendation module is used for generating a hot commodity recommendation table according to the commodity hot data;
And the commodity detection module is used for automatically feeding back a commodity list which is contained in the hot commodity recommendation table but not in the commodity information database to the merchant which is resident in the platform, and requesting the merchant to provide attribute information of the commodity.
In accordance with a preferred embodiment of the present invention the apparatus 200 further includes a pricing module for establishing a machine self-learning based commodity pricing model for generating commodity online prices from at least one of commodity basic information, commodity performance data and commodity scores when commodity online automatically at the online shopping platform.
According to a preferred embodiment of the present invention, the on-line and off-line control module 203 includes a commodity sorting unit for sequentially ordering commodities on line according to a commodity score of the commodity from high to low.
It will be appreciated by those skilled in the art that the modules in the embodiments of the apparatus described above may be distributed in an apparatus as described, or may be distributed in one or more apparatuses different from the embodiments described above with corresponding changes. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
The following describes an embodiment of an electronic device according to the present invention, which may be regarded as a specific physical implementation of the above-described embodiment of the method and apparatus according to the present invention. The details described in the embodiments of the electronic device according to the invention should be regarded as additions to the embodiments of the method or the apparatus described above, and the details not disclosed in the embodiments of the electronic device according to the invention may be realized by referring to the embodiments of the method or the apparatus described above.
Fig. 4 is a schematic structural view of an electronic device according to an embodiment of the present invention, the electronic device including a processor and a memory for storing a computer-executable program, the processor executing a machine self-learning-based commodity automatic on-line and off-line method when the computer program is executed by the processor.
As shown in fig. 4, the electronic device is in the form of a general purpose computing device. The processor may be one or a plurality of processors and work cooperatively. The invention does not exclude that the distributed processing is performed, i.e. the processor may be distributed among different physical devices. The electronic device of the present invention is not limited to a single entity, but may be a sum of a plurality of entity devices.
The memory stores a computer executable program, typically machine readable code. The computer readable program may be executable by the processor to enable an electronic device to perform the method, or at least some of the steps of the method, of the present invention.
The memory includes volatile memory, such as Random Access Memory (RAM) and/or cache memory, and may be non-volatile memory, such as Read Only Memory (ROM).
Optionally, in this embodiment, the electronic device further includes an I/O interface, which is used for exchanging data between the electronic device and an external device. The I/O interface may be a bus representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
It should be understood that the electronic device shown in fig. 4 is only one example of the present invention, and the electronic device of the present invention may further include elements or components not shown in the above examples. For example, some electronic devices further include a display unit such as a display screen, and some electronic devices further include a man-machine interaction element such as a button, a keyboard, and the like. The electronic device may be considered as covered by the invention as long as the electronic device is capable of executing a computer readable program in a memory for carrying out the method or at least part of the steps of the method.
From the above description of embodiments, those skilled in the art will readily appreciate that the exemplary embodiments described herein may be implemented in software, or may be implemented in software in combination with necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a computer readable storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-mentioned method according to the present invention. The computer program, when executed by a data processing device, enables the computer readable medium to implement the above method of the present invention by creating a commodity information database for storing commodity basic information, commodity performance data, and commodity scores, which are indexes for evaluating suitability of commodity online sales, creating a commodity online-offline recommendation model capable of periodically calculating and updating commodity scores of commodities in the commodity database based on a machine self-learning algorithm according to historical commodity basic information and historical commodity performance data, and automatically online or offline commodities in the online shopping platform according to a predetermined commodity online-offline rule, the commodity online-offline rule being associated with the commodity scores.
Fig. 5 is a schematic diagram of a computer-readable recording medium of an embodiment of the present invention. As shown in fig. 5, a computer-readable recording medium stores a computer-executable program that, when executed, implements the machine-self-learning commodity automatic on-line and off-line method of the present invention described above. The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium 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 readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
From the above description of embodiments, those skilled in the art will readily appreciate that the present invention may be implemented by hardware capable of executing a specific computer program, such as the system of the present invention, as well as electronic processing units, servers, clients, handsets, control units, processors, etc. included in the system. The invention may also be implemented by computer software for performing the method of the invention. It should be noted, however, that the computer software for performing the method of the present invention is not limited to being executed by one or a specific hardware entity, but may also be implemented in a distributed fashion by unspecified specific hardware, e.g., some of the method steps executed by the computer program may be executed on a mobile client, and another part may be executed on a smart meter, smart pen, etc. For computer software, the software product may be stored on a computer readable storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.), or may be stored distributed over a network, as long as it enables the electronic device to perform the method according to the invention.
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in accordance with embodiments of the present invention may be implemented in practice using a general purpose data processing device such as a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
The above-described specific embodiments further describe the objects, technical solutions and advantageous effects of the present invention in detail, and it should be understood that the present invention is not inherently related to any particular computer, virtual device or electronic apparatus, and various general-purpose devices may also implement the present invention. The foregoing description of the embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (9)
1. An automatic commodity online and offline method based on machine self-learning is used for an online shopping platform and is characterized by comprising the following steps:
Establishing a commodity information database, wherein the database is used for storing commodity basic information, commodity expression data and commodity scores, and the commodity scores are indexes for evaluating the suitability of commodity online sales;
The method comprises the steps of establishing an online and offline commodity recommendation model, wherein the model is based on a machine self-learning algorithm, and can periodically calculate and update commodity scores of commodities in a commodity database according to historical commodity basic information and historical commodity expression data;
Setting an online-offline period, automatically offline after the online-offline period is maintained by the shopping platform, adding the performance data of the commodity in the online-offline period into the previous performance data, inputting the basic information and the performance data of the commodity into a commodity online-offline recommendation model again to score, and determining whether the commodity is online or offline in the next period according to the score;
And generating a hot commodity recommendation table according to the hot data of each commodity in one period, and if the fact that the commodity in the hot commodity recommendation table is not complete in information stored in a commodity information database is detected, automatically feeding back a commodity list which is contained in the hot commodity recommendation table but not in the commodity information database to a merchant which resides in the platform, and requesting the merchant to provide the attribute of the commodity.
2. The automatic machine self-learning based commodity online and offline method according to claim 1, wherein:
the online shopping platform comprises a merchant which resides in the platform;
the method further includes obtaining the merchandise basic information from the merchant.
3. The automatic machine self-learning based commodity online and offline method according to claim 2, wherein:
the commodity expression data comprises commodity heat data;
The method further includes tracking network data associated with the commodity and calculating commodity heat data of the commodity based on the network data.
4. The automatic machine self-learning based commodity online and offline method according to claim 3, wherein:
The network data related to the commodity comprises at least one of search data, dotting data, message data and advertisement data.
5. The machine self-learning based automatic online and offline method of merchandise according to any one of claims 1 to 4, further comprising:
And establishing a commodity pricing model based on machine self-learning, and generating commodity online prices according to at least one of commodity basic information, commodity performance data and commodity scores by the commodity pricing model when commodities are automatically online on the online shopping platform.
6. The automatic online and offline method for commodities based on machine self-learning according to claim 1, wherein said automatic online or offline commodity on said online shopping platform according to a predetermined commodity online and offline rule further comprises:
And the online shopping platform is used for online commodity according to the commodity grading sequence from high to low.
7. Automatic commodity online and offline device based on machine self-learning, which is characterized by comprising:
The information storage module is used for establishing a commodity information database which is used for storing commodity basic information, commodity expression data and commodity scores, wherein the commodity scores are indexes for evaluating the suitability of commodity online sales;
The system comprises a commodity database, a commodity grading calculation module, a commodity grading recommendation module, a commodity grading analysis module and a commodity grading analysis module, wherein the commodity grading calculation module is used for establishing a commodity online-offline recommendation model, the model is based on a machine self-learning algorithm, and commodity grading of commodities in the commodity database can be regularly calculated and updated according to historical commodity basic information and historical commodity expression data;
The system comprises an online-offline control module, a commodity information database, a commodity information recommendation module and a commodity information recommendation module, wherein the online-offline control module is used for setting an online-offline period, automatically offline after the online-offline commodity is maintained in the online-offline period by the shopping platform, inputting the performance data of the commodity in the online-offline period into the performance data before the performance data of the commodity are added, inputting the basic information and the performance data of the commodity into the commodity online-offline recommendation model again to score, determining whether the commodity is online or offline in the next period according to the score, generating a heat commodity recommendation table according to the heat data of each commodity in one period, and automatically feeding back the commodity list which is contained in the heat commodity recommendation table but not contained in the commodity information database to a merchant which resides in the platform if the fact that the information of the commodity in the heat commodity recommendation table is not complete is detected in the commodity information database, and requesting the merchant to provide the attribute of the commodity.
8. An electronic device comprising a processor and a memory for storing a computer executable program, characterized in that:
The computer program, when executed by the processor, performs the method of any of claims 1-6.
9. A computer readable medium storing a computer executable program, wherein the computer executable program when executed implements the method of any one of claims 1-6.
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| CN114202389A (en) * | 2021-10-27 | 2022-03-18 | 杭州拼便宜网络科技有限公司 | User evaluation control method and device, electronic equipment and storage medium |
| CN114549117A (en) * | 2022-01-14 | 2022-05-27 | 百芯智能制造科技(深圳)有限公司 | Electronic component heat calculation method and electronic equipment |
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| KR101872220B1 (en) * | 2016-10-14 | 2018-06-28 | 주식회사 셀팅 | Apparatus for providing sales forecasting information based on network |
| CN110009400A (en) * | 2019-03-18 | 2019-07-12 | 康美药业股份有限公司 | Merchandise valuation method, terminal and computer readable storage medium |
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| CN110648182A (en) * | 2019-09-29 | 2020-01-03 | 阿里巴巴（中国）有限公司 | Method, system, medium and computing device for automatic pricing of commodities |
| CN111582973A (en) * | 2020-04-09 | 2020-08-25 | 苏宁云计算有限公司 | Commodity recommendation data generation method, device and system |
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