A Customer Intelligent Recommendation System and Method Based on a Big Data Model

Abstract

The present invention discloses a customer intelligent recommendation system and method based on a big data model, which relates to the technical field of customer recommendation. The method includes the following steps: Step 1: Periodically obtain the shopping records and browsing records of customers; Step 2: Obtain the potential consumption values of customers for each potential consumption category, and recommend potential desire customers to the products of the potential consumption category; Step 3: Obtain the recommended effective evaluation value of customers, and judge whether to prohibit the marketing information of promoting the products of the potential consumption category to customers according to the comparison result of the recommended effective evaluation value and its threshold. The method of the present invention can intelligently analyze the potential consumption desire of customers for the products of the potential consumption category, and intelligently analyze the potential consumption power of each customer based on the big data model. On the basis of ensuring accurate recommendation, the consumption experience of customers is continuously optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of customer recommendation, and more specifically, to a customer intelligent recommendation system and method based on a big data model. Background Art

[0002] There are usually multiple departments within an enterprise, each of which is responsible for the marketing of the corresponding products, and each department's marketing is independent. In order to improve the marketing performance of each department in the enterprise, departments will share customer information with each other to facilitate departments to carry out targeted marketing promotions to customers. However, when the number of customers reaches a certain level, the screening of customer information is very troublesome, and it is difficult to accurately tap into customers' potential consumer goods. Some companies will choose the strategy of "casting a wide net", that is, randomly recommending products to customers, which will lead to a decline in customers' shopping experience and cause the company's customer stickiness to be low. Summary of the invention

[0003] In view of the deficiencies in the prior art, the object of the present invention is to provide a customer intelligent recommendation system and method based on a big data model.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A customer intelligent recommendation system based on a big data model, comprising a record acquisition module, a potential recommendation module, and a recommendation analysis module;

[0006] The record acquisition module is used to periodically acquire the customer's shopping records and browsing records;

[0007] The potential recommendation module is used to obtain the potential consumption value of the customer for each potential consumption category and recommend potential customers to products of the potential consumption category;

[0008] The recommendation analysis module is used to obtain the effective evaluation of the recommendation of the customer, and determine whether to prohibit the promotion of marketing information of potential consumer category products to the customer based on the comparison result of the effective evaluation of the recommendation and its threshold.

[0009] Furthermore, the customer's shopping records and browsing records are obtained periodically, specifically: based on the T cycle, at each cycle node, all the customer's shopping records and browsing records before the current system time are obtained.

[0010] Furthermore, shopping records include product categories and shopping times, and browsing records include product categories, browsing time, and browsing depth.

[0011] Further, potential consumer product categories are obtained in the following manner: Obtain the product categories in all the customer's shopping records and browsing records before the current time of the system, and mark them as reference categories. Obtain the product categories currently on sale in the enterprise, and mark them as on-sale categories. Remove all the reference categories from the on-sale categories, and mark the remaining on-sale categories as potential consumer product categories.

[0012] Further, obtain the potential consumption value of the customer for each potential consumer product category. Specifically: Obtain all the customer's shopping records and browsing records before the current time of the system. Obtain the consumption propensity values of each reference category in the shopping records and browsing records. Through knowledge graph association analysis, mark the reference categories associated with the potential consumer product category as analyzed categories, and mark the consumption propensity values of the analyzed categories as EDi, where i is the number of the analyzed category, i = 1, 2,..., n. Obtain the total number of analyzed categories associated with the potential consumer product category, and mark it as TBZ. Use the formula to obtain the potential consumption value HKB of the customer for the potential consumer product category, where b1 is the consumption propensity value coefficient and b2 is the total number coefficient of analyzed categories.

[0013] Further, the consumption propensity values of each product category in the shopping records and browsing records are obtained in the following manner: Mark the shopping records of the same product category as same-product shopping records, and mark the browsing records of the same product category as same-product browsing records. Obtain the average same-product shopping interval FGH, the average same-product browsing duration ASK, and the average same-product browsing depth SZM.

[0014] Obtain the total number of same-product shopping records, and mark it as TPZ. Obtain the total number of same-product browsing records, and mark it as WDR. Use the formula to obtain the consumption propensity value CPN of the product category, where a1 is the average same-product shopping interval coefficient, a2 is the same-product shopping record quantity coefficient, a3 is the average same-product browsing duration coefficient, a4 is the average same-product browsing depth coefficient, and a5 is the same-product browsing record quantity coefficient.

[0015] Further, the average same-product shopping interval FGH, the average same-product browsing duration ASK, and the average same-product browsing depth SZM are obtained in the following manner: Sort all the same-product shopping records in the order of shopping time. Calculate the time difference between two adjacent shopping times after sorting to obtain the same-product shopping interval. Sum up all the same-product shopping intervals and take the average to obtain the average same-product shopping interval, and mark it as FGH. Sum up the browsing durations of all the same-product browsing records and take the average to obtain the average same-product browsing duration, and mark it as ASK. Sum up the browsing depths of all the same-product browsing records and take the average to obtain the average same-product browsing depth, and mark it as SZM.

[0016] Furthermore, potential desire customers are recommended for products in potential consumer categories, specifically: set a potential consumption threshold. When the potential consumption value of a potential consumer category is greater than or equal to the potential consumption threshold, mark this customer as a potential desire customer for this potential consumer category. Sort all potential desire customers for this potential consumer category in descending order according to the potential consumption value. Promote the product marketing information of this potential consumer category to potential desire customers in the sorted order. When promoting the product marketing information of this potential consumer category to potential desire customers, mark this moment as the promotion moment.

[0017] Furthermore, obtain the recommendation effective evaluation value of the customer. According to the comparison result between the recommendation effective evaluation value and its threshold, determine whether to prohibit promoting the product marketing information of the potential consumer category to the customer, specifically:

[0018] When a potential desire customer first purchases a product in a potential consumer category, mark this moment as the potential conversion moment, and synchronously generate a potential conversion record. The potential conversion record includes the potential conversion moment and the consumption amount. Calculate the time difference between the potential conversion moment and the promotion moment to obtain the potential conversion duration, and mark it as HKN. Increase the potential conversion times of this customer by one;

[0019] When there is no potential conversion moment for a potential desire customer in this potential consumer category, calculate the time difference between the current system time and the promotion moment to obtain the promotion waiting - to - convert duration. Set the promotion waiting - to - convert threshold duration. When the promotion waiting - to - convert duration is greater than or equal to the promotion waiting - to - convert threshold duration, increase the conversion failure times of this customer by one;

[0020] Obtain all potential conversion records of the customer before the current system time, obtain the number of potential conversion records, sort all potential conversion records in the order of the potential conversion moment, calculate the time difference between two adjacent potential conversion moments after sorting to obtain the potential conversion interval, sum up all potential conversion intervals and take the average to obtain the average potential conversion interval, sum up the consumption amounts of all potential conversion records and take the average to obtain the average conversion consumption amount. Use the number of potential conversion records, the average potential conversion interval, and the average conversion consumption amount as the input data of the customer potential conversion analysis model to obtain the customer's potential conversion analysis value, and mark it as BCX;

[0021] Sum up all potential conversion times of the customer before the current system time to obtain the total potential conversion times, and mark it as BZS. Sum up all conversion failure times of the customer before the current system time to obtain the total conversion failure times, and mark it as YTR. Sum up all potential conversion durations of the customer before the current system time and take the average to obtain the average potential conversion duration, and mark it as PYL. Use the formula Obtain the effective evaluation value of customer recommendation for ERP, where c1 is the potential conversion difference coefficient, c2 is the average potential conversion duration coefficient, and c3 is the potential conversion analysis value coefficient;

[0022] Set the recommended effective threshold evaluation value. When the effective evaluation value of customer recommendation is less than the recommended effective threshold evaluation value, within the subsequent m cycles, prohibit promoting marketing information of potential consumer category products to this customer.

[0023] Furthermore, a customer intelligent recommendation method based on a big data model includes the following steps:

[0024] Step 1: Periodically obtain the shopping records and browsing records of customers;

[0025] Step 2: Obtain the potential consumption value of each potential consumer category for customers, and recommend potential desire customers to the products of the potential consumer category;

[0026] Step 3: Obtain the effective evaluation value of customer recommendation, and judge whether to prohibit promoting marketing information of potential consumer category products to customers according to the comparison result between the effective evaluation value of customer recommendation and its threshold value.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. The method of the present invention can intelligently analyze the potential consumption desire of customers for potential consumer category products, and intelligently analyze the potential consumption power of each customer based on the big data model. On the basis of ensuring accurate recommendation, continuously optimize the consumption experience of customers;

[0029] 2. Set up a record acquisition module and a potential recommendation module. Through the analysis of the shopping records and browsing records of customers, it can intelligently determine the potential consumer category of each customer, further analyze the potential consumption desire of customers for potential consumer category products, and accurately recommend potential desire customers to the products of the potential consumer category. Set up a recommendation analysis module, which can intelligently analyze the potential consumption power of each customer based on the big data model, and then determine whether it is necessary to prohibit promoting marketing information of potential consumer category products to customers. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flowchart of a customer intelligent recommendation method based on a big data model;

[0031] Figure 2 It is a flowchart of recommending potential desire customers to the products of the potential consumer category;

[0032] Figure 3 It is a flowchart of judging whether to prohibit promoting marketing information of potential consumer category products to customers. DETAILED DESCRIPTION OF THE INVENTION Example 1

[0033] Reference Figure 1 , a customer intelligent recommendation method based on big data model, comprising the following steps:

[0034] Step 1: Periodically obtain the customer's shopping and browsing history.

[0035] Step 2: Obtain the customer's potential consumption value for each potential consumer category, and recommend products of the potential consumer category to potential customers.

[0036] Step 3: Obtain the effective rating of the customer's recommendation, and determine whether to prohibit the promotion of marketing information of potential consumer products to the customer based on the comparison result of the effective rating of the recommendation and its threshold.

[0037] The above method can intelligently analyze customers' potential consumption desire for potential consumer product categories, and intelligently analyze each customer's potential consumption power based on big data models, while continuously optimizing customers' consumption experience while ensuring accurate recommendations. Example 2

[0038] Reference Figures 2 to 3 ,A customer intelligent recommendation system based on a big data model, includes a record acquisition module, a potential recommendation module, and a recommendation analysis module.

[0039] Record acquisition module: Based on the T cycle, at each cycle node, all shopping records and browsing records of customers before the current system time are obtained. Shopping records include product categories and shopping times (shopping time is the Beijing time when the customer makes a purchase). Browsing records include product categories, browsing time, and browsing depth (browsing depth is the number of times the user clicks on page information when browsing a marketing page).

[0040] There are usually different branch departments within an enterprise, and each department is responsible for the marketing of different product categories. The branch departments within the enterprise will share each other's customer information to tap into potential customers in each department. The enterprise will display the marketing pages of each product category on the client. When customers purchase products on the marketing page, a shopping record will be generated, and when customers browse the marketing page, a browsing record will be generated.

[0041] For example, an electrical appliance company usually has multiple branch departments such as televisions, refrigerators, and air conditioners. The television branch department is responsible for the marketing of the television category, the refrigerator branch department is responsible for the marketing of the refrigerator category, and the air conditioner branch department is responsible for the marketing of the air conditioner category. The TV branch department, the refrigerator branch department, and the air conditioner branch department will share each other's customer information.

[0042] Potential Recommendation Module: Obtain all the shopping records and browsing records of this customer before the current time in the system. Obtain the consumption tendency values of each reference product category in the shopping records and browsing records. Through correlation analysis using the knowledge graph, mark the reference product categories associated with the potential consumption product categories as analyzed product categories, and mark the consumption tendency values of the analyzed product categories as EDi, where i is the number of the analyzed product category, i = 1, 2, …, n. Obtain the total number of analyzed product categories associated with the potential consumption product category and mark it as TBZ. Use the formula to obtain the potential consumption value HKB of the customer for this potential consumption product category. Among them, b1 is the consumption tendency value coefficient, b2 is the total number coefficient of analyzed product categories. The value of b1 is 0.58, and the value of b2 is 0.97.

[0043] The potential consumption product category is obtained through the following method: Obtain the product categories in all the shopping records and browsing records of this customer before the current time in the system and mark them as reference product categories. Obtain the product categories on sale in the enterprise and mark them as on-sale product categories. Remove all the reference product categories from the on-sale product categories, and mark the remaining on-sale product categories as potential consumption product categories.

[0044] The product categories within the enterprise are represented in the form of a knowledge graph. For example: The relationships in the knowledge graph represent the associations between product categories, such as the audience relationship, ensuring that the knowledge graph can accurately reflect the relationships existing between product categories.

[0045] The consumption tendency value of each product category in the shopping records and browsing records is obtained through the following method: Mark the shopping records of the same product category as same-product shopping records, mark the browsing records of the same product category as same-product browsing records. Sort all the same-product shopping records in the order of shopping time. Calculate the time difference between two adjacent shopping times after sorting to obtain the same-product shopping interval. Sum up all the same-product shopping intervals and take the average to obtain the average same-product shopping interval and mark it as FGH. Obtain the total number of same-product shopping records and mark it as TPZ. Sum up the browsing durations of all the same-product browsing records and take the average to obtain the average same-product browsing duration and mark it as ASK. Sum up the browsing depths of all the same-product browsing records and take the average to obtain the average same-product browsing depth and mark it as SZM. Obtain the total number of same-product browsing records and mark it as WDR. Use the formula to obtain the consumption tendency value CPN of this product category. Among them, a1 is the average same-product shopping interval coefficient, a2 is the same-product shopping record quantity coefficient, a3 is the average same-product browsing duration coefficient, a4 is the average same-product browsing depth coefficient, a5 is the same-product browsing record quantity coefficient. The value of a1 is 0.76, the value of a2 is 0.28, the value of a3 is 0.19, the value of a4 is 0.25, and the value of a5 is 0.38.

[0046] Set a potential consumption threshold, which is a system - preset threshold and can be modified according to actual needs.

[0047] When the potential consumption value of a potential consumption category is greater than or equal to the potential consumption threshold, mark this customer as a potential desire customer for this potential consumption category. Sort all potential desire customers for this potential consumption category in descending order according to the potential consumption value. Promote the product marketing information of this potential consumption category to potential desire customers in the sorted order. When promoting the product marketing information of this potential consumption category to potential desire customers, mark this moment as the promotion moment.

[0048] When the potential consumption value of a potential consumption category is less than the potential consumption threshold, no corresponding processing is performed.

[0049] Set a record acquisition module and a potential recommendation module. By analyzing the shopping records and browsing records of customers, it can be intelligently determined the potential consumption categories of each customer, and further analyze the potential consumption desire of customers for products of potential consumption categories, and accurately recommend potential desire customers to products of potential consumption categories.

[0050] Recommendation analysis module: When a potential desire customer first purchases a product of a potential consumption category, mark this moment as the potential conversion moment, and synchronously generate a potential conversion record. The potential conversion record includes the potential conversion moment and the consumption amount. Calculate the time difference between the potential conversion moment and the promotion moment to obtain the potential conversion duration, and mark it as HKN, and increase the potential conversion times of this customer by one.

[0051] When there is no potential conversion moment for a potential desire customer for this potential consumption category, calculate the time difference between the current system time and the promotion moment to obtain the promotion waiting - to - convert duration. Set the promotion waiting - to - convert threshold duration, which is a system - preset threshold and can be modified according to actual needs.

[0052] When the promotion waiting - to - convert duration is greater than or equal to the promotion waiting - to - convert threshold duration, increase the conversion failure times of this customer by one. When the promotion waiting - to - convert duration is less than the promotion waiting - to - convert threshold duration, no corresponding processing is performed.

[0053] Retrieve all potential conversion records of the customer before the current system time, obtain the quantity of potential conversion records, sort all potential conversion records in ascending order of the potential conversion time, calculate the time difference between two adjacent potential conversion times after sorting to obtain the potential conversion interval, sum up all potential conversion intervals and take the average to get the average potential conversion interval, sum up the consumption amounts of all potential conversion records and take the average to get the average conversion consumption amount, use the quantity of potential conversion records, the average potential conversion interval, and the average conversion consumption amount as the input data of the customer potential conversion analysis model to obtain the customer's potential conversion analysis value, and label it as BCX.

[0054] The customer potential conversion analysis model is a big data model and is obtained through the following method:

[0055] Obtain multiple groups of customer potential conversion analysis data. Each group of customer potential conversion analysis data includes the quantity of potential conversion records, the average potential conversion interval, and the average conversion consumption amount. The customer potential conversion analysis data can be preset by the system or real data. Assign different labels to each group of customer potential conversion analysis data, and the label is the subsequent potential conversion analysis value. Divide the customer potential conversion analysis data into a training set and a validation set, and obtain multiple groups of customer potential conversion analysis data through iterative training. The larger the value of the potential conversion analysis value, the stronger the customer's potential consumption ability.

[0056] Sum up all the potential conversion times of the customer before the current system time to obtain the total potential conversion times, and label it as BZS. Sum up all the conversion failure times of the customer before the current system time to obtain the total conversion failure times, and label it as YTR. Sum up all the potential conversion durations of the customer before the current system time and take the average to get the average potential conversion duration, and label it as PYL. Use the formula to obtain the customer's recommended effective evaluation value ERP, where c1 is the potential conversion difference coefficient, c2 is the average potential conversion duration coefficient, c3 is the potential conversion analysis value coefficient, c1 is 1.38, c2 is 0.12, and c3 is 0.25.

[0057] Set the recommended effective threshold evaluation value. When the customer's recommended effective evaluation value is greater than or equal to the recommended effective threshold evaluation value, no corresponding processing is performed. When the customer's recommended effective evaluation value is less than the recommended effective threshold evaluation value, within the subsequent m cycles, prohibit promoting marketing information of potential consumption category products to this customer.

[0058] Set up a recommended analysis module, which can intelligently analyze the potential consumption power of each customer based on the big data model, and then determine whether it is necessary to prohibit promoting marketing information of potential consumption category products to the customer.

[0059] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0060] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0061] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0062] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0063] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0064] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical, or other forms.

[0065] If the described functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0066] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claimed rights.

Claims

1. A customer intelligent recommendation system based on big data model, characterized in that: It includes record acquisition module, potential recommendation module and recommendation analysis module; The record acquisition module is used to periodically acquire the customer's shopping records and browsing records; The potential recommendation module is used to obtain the potential consumption value of the customer for each potential consumption category and recommend potential customers to products of the potential consumption category; Potential consumer categories are obtained in the following ways: obtaining product categories in all shopping records and browsing records of customers before the current time of the system, and marking them as reference categories; obtaining product categories on sale in the enterprise, and marking them as on sale categories; removing all reference categories in the on sale categories, and marking the remaining on sale categories as potential consumer categories; Obtain all shopping and browsing records of the customer before the current time of the system, obtain the consumption propensity value of each reference category in the shopping and browsing records, mark the reference category associated with the potential consumption category as the analysis category through knowledge graph association analysis, and mark the consumption propensity value of the analysis category as EDi, where i is the number of the analysis category, i=1, 2, ..., n, obtain the total number of analysis categories associated with the potential consumption category and mark it as TBZ, and use the formula Get the customer's potential consumption value HKB for the potential consumption category, where b1 is the consumption propensity value coefficient and b2 is the total quantity coefficient of the analyzed category; Mark the shopping records of the same product category as same-product shopping records, mark the browsing records of the same product category as same-product browsing records, and obtain the average same-product shopping interval FGH, average same-product browsing time ASK, and average same-product browsing depth SZM; Get the total number of purchase records for the same product and mark it as TPZ, get the total number of browsing records for the same product and mark it as WDR, and use the formula Get the consumption propensity value CPN of the product category, where a1 is the average same-product shopping interval coefficient, a2 is the same-product shopping record quantity coefficient, a3 is the average same-product browsing time coefficient, a4 is the average same-product browsing depth coefficient, and a5 is the same-product browsing record quantity coefficient; The recommendation analysis module obtains the effective evaluation of the customer's recommendation based on the big data model, and determines whether to prohibit the promotion of marketing information of potential consumer category products to the customer based on the comparison result of the effective evaluation of the recommendation and its threshold.

2. According to claim 1, a customer intelligent recommendation system based on a big data model is characterized in that: The customer's shopping and browsing records are obtained periodically. Specifically, based on the T cycle, at each cycle node, all the customer's shopping and browsing records before the current system time are obtained.

3. According to claim 2, a customer intelligent recommendation system based on a big data model is characterized in that: Shopping records include product categories and shopping times, and browsing records include product categories, browsing time, and browsing depth.

4. According to claim 1, a customer intelligent recommendation system based on a big data model is characterized in that: The average same-product shopping interval FGH, the average same-product browsing time ASK, and the average same-product browsing depth SZM are obtained in the following way: sort all same-product shopping records in the order of shopping time, calculate the time difference between two adjacent shopping times after sorting, and obtain the same-product shopping interval, sum up all the same-product shopping intervals and take the average to obtain the average same-product shopping interval, and mark it as FGH, sum up the browsing time of all the same-product browsing records and take the average to obtain the average same-product browsing time, and mark it as ASK, sum up the browsing depths of all the same-product browsing records and take the average to obtain the average same-product browsing depth, and mark it as SZM.

5. According to claim 1, a customer intelligent recommendation system based on a big data model is characterized in that: Recommending potential desire customers to products in a potential consumer category, specifically: setting a potential consumption threshold, when the potential consumption value of the potential consumer category is greater than or equal to the potential consumption threshold, marking the customer as a potential desire customer of the potential consumer category, sorting all potential desire customers of the potential consumer category in descending order according to their potential consumption values, promoting the product marketing information of the potential consumer category to the potential desire customers in the sorted order, and when promoting the marketing information of the products of the potential consumer category to the potential desire customers, marking the moment as a promotion moment.

6. The customer intelligent recommendation system based on big data model according to claim 1 is characterized in that: Based on the big data model, the effective evaluation of the customer's recommendation is obtained. According to the comparison result between the effective evaluation of the recommendation and its threshold, it is determined whether to prohibit the promotion of marketing information of potential consumer products to the customer. Specifically: When a potential customer purchases a product of a potential consumption category for the first time, the moment is marked as a potential conversion moment, and a potential conversion record is generated simultaneously. The potential conversion record includes the potential conversion moment and the consumption amount. The time difference between the potential conversion moment and the promotion moment is calculated to obtain the potential conversion duration, which is marked as HKN, and the number of potential conversions of the customer is increased by one; When a potential customer does not have a potential conversion moment for this potential consumer category, the time difference between the current system time and the promotion time is calculated to obtain the promotion waiting time, and the promotion waiting threshold time is set. When the promotion waiting time is greater than or equal to the promotion waiting threshold time, the number of conversion failures for this customer is increased by one; Obtain all potential conversion records of the customer before the current time of the system, obtain the number of potential conversion records, sort all potential conversion records in the order of potential conversion moments, calculate the time difference between two adjacent potential conversion moments after sorting, obtain the potential conversion interval, sum up all potential conversion intervals and take the average value to obtain the average potential conversion interval, sum up the consumption amounts of all potential conversion records and take the average value to obtain the average conversion consumption amount, use the number of potential conversion records, the average potential conversion interval, and the average conversion consumption amount as input data of the customer's potential conversion analysis model, obtain the customer's potential conversion analysis value, and mark it as BCX; Sum up all the potential conversion times of the customer before the current system time to get the total number of potential conversions, which is marked as BZS. Sum up all the conversion failure times of the customer before the current system time to get the total number of conversion failures, which is marked as YTR. Sum up all the potential conversion durations of the customer before the current system time and take the average to get the average potential conversion duration, which is marked as PYL. Use the formula Get the customer's recommended effective evaluation ERP, where c1 is the potential conversion difference coefficient, c2 is the average potential conversion time coefficient, and c3 is the potential conversion analysis value coefficient; Set a recommendation effective threshold rating value. When a customer's recommendation effective rating value is less than the recommendation effective threshold rating value, it is prohibited to promote marketing information of potential consumer category products to the customer in the subsequent m cycles.

7. A customer intelligent recommendation method based on a big data model, applied to a customer intelligent recommendation system based on a big data model as claimed in claim 1, characterized in that: The steps include: Step 1: Periodically obtain the customer's shopping and browsing history; Step 2: Obtain the potential consumption value of each potential consumption category and recommend potential customers to products in the potential consumption category; Step 3: Obtain the effective rating of the customer's recommendation, and determine whether to prohibit the promotion of marketing information of potential consumer products to the customer based on the comparison result of the effective rating of the recommendation and its threshold.

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