Product recommendation method based on data pre-screening and introduction of intermediate variables

Abstract

The present invention discloses a product recommendation method based on data pre-screening and the introduction of intermediate variables. Based on the condition that each customer has similar common purchase characteristics for the product combination including the product to be recommended that has been purchased in the past, a customer list is screened in advance as the basis for determining whether the customer to be recommended is the target recommendation customer. The subsequent determination of whether the customer to be recommended is the target recommendation customer is based on the historical purchase data of each customer in the customer list. This greatly reduces the amount of data analysis and is beneficial for improving the analysis speed of product recommendations for a large customer group at the same time in scenarios where complex recommendation analysis and calculation are required to consider the mutual recommendation influence relationship between products. By introducing intermediate variables and constructing the mutual recommendation influence relationship between products, it is possible to accurately screen out target recommendation products that can meet the brand pursuit and consumption capacity of the customer to be recommended from a large number of products to be recommended, thereby greatly improving the accuracy of building material product recommendations.

Description

Technical Field

[0001] The present invention relates to the technical field of product recommendation, and in particular to a product recommendation method based on data pre-screening and introduction of intermediate variables. Background Art

[0002] The core issue that needs to be addressed in product recommendation is how to improve the accuracy of product recommendations. In addition, when the crawled customer base is large and it is necessary to quickly calculate the recommended products for each customer in this large group within a short period of time, how to improve the analysis speed is another important issue that needs to be addressed to ensure the user experience of the product recommendation analysis system.

[0003] Currently, existing technologies for improving the accuracy of product recommendations focus on improving recommendation algorithms, without considering the mutual recommendation influence between different products purchased by the same customer. For example, assuming that customer U has purchased product A, existing technologies use a set of algorithms to extract customer U's purchase characteristics when purchasing product A, and then match product B with product characteristics similar to product A, and finally use customer U as the candidate for recommendation of product B. When product C needs to be recommended, the similarity between product C and product A's product characteristics is similarly calculated, and product C with similarity is recommended to customers who have purchased product A. Under this principle of product recommendation algorithms, current improvements to the algorithms focus on how to improve the accuracy of analyzing customer purchase characteristics of product A, or improving the accuracy of analyzing the similarity between product B or product C and product A's product characteristics. However, when a customer has purchased product A and product B or product C at the same time in the past, the common purchase characteristics of the customer's purchases of product A, product B, and product C are not taken into account. Especially when product A, product B, and product C belong to the same category, accurately analyzing these common purchase characteristics can greatly improve the accuracy of product recommendations. However, how to accurately analyze these common purchase characteristics is the first technical problem that needs to be solved in this application.

[0004] As in the above example, the existing product recommendation algorithm focuses on the similarity of product features between the recommended product, such as product B, and the customer's historically purchased product, such as product A. The feature data required for the product feature similarity matching process is acquired and screened during the algorithm analysis process. When the acquisition or screening process is complicated, and the number of recommended products or customers to be recommended that need to be calculated for product feature similarity is large, this in-process data acquisition or screening process will seriously affect the calculation and analysis speed, which is not conducive to quickly calculating the recommended products for each customer in a large customer group in a short time. In particular, in scenarios where the mutual recommendation influence relationship between the above-mentioned products needs to be considered, in-process data acquisition or screening will be more time-consuming, affecting the user's experience of the system. Therefore, how to change in-process data screening to pre-process data screening, especially in scenarios where the mutual recommendation influence relationship between products needs to be considered, how to change data screening to pre-process screening becomes the second technical problem that needs to be solved in this application. Summary of the Invention

[0005] The present invention takes into account the mutual recommendation influence relationship between products to improve the accuracy of product recommendations, and in the scenario of considering the mutual recommendation influence relationship between products, changes the data screening required for recommendation to pre-screening, in order to improve the analysis speed of product recommendations for a large customer group at the same time. It provides a product recommendation method based on data pre-screening and the introduction of intermediate variables.

[0006] To achieve this object, the present invention adopts the following technical solutions:

[0007] A product recommendation method based on data pre-screening and introduction of intermediate variables is provided, including the following steps:

[0008] S1, using the customer's historical purchase history of the product combination including the product to be recommended as a screening condition, and screening out a list of reference customers as a basis for determining whether the customer to be recommended is a target recommendation customer;

[0009] S2, filtering out products that have been purchased by the customer to be recommended and by each customer in the customer list, to form a third recommendation basis list;

[0010] S3, calculate the first variable value of the intermediate variable between each product in the third recommendation basis list of the customer's historical purchase history, and extract the maximum first variable value, and then calculate the positive impact factor of the product to be recommended and each building material product in the third recommendation basis list in sequence based on the maximum first variable value and reverse impact factors , and further constructing a recommendation influence relationship between the proposed recommended product and each building material product in the third recommendation basis list;

[0011] S4, solving the fifth deviation between the mutual recommendation influence relationship between the customers to be recommended calculated for the same product combination and the mutual recommendation history influence relationship between the historical purchasing customers referred to in the third recommendation basis list, and taking the customers to be recommended who meet the condition that the fifth deviation is less than the preset fifth deviation threshold as the target recommended customers for recommending the products to be recommended.

[0012] Preferably, in step S1, the rule for determining that each customer has similar common purchasing characteristics for the product combination is: different customers purchase the same brand classification level of the first building material product in the product combination, and the same brand classification level of the second building material product purchased by different customers, and the deviation between the first average values ​​of the common characteristics of brand pursuit of the product combination is less than a preset first common deviation threshold, and the deviation between the second average values ​​of the common characteristics of consumption capacity of the product combination is less than a preset second common deviation threshold;

[0013] The intermediate variable includes the common characteristics pursued by the brands associated with the same product combination and / or the common characteristics of consumption capabilities; the variable value of the intermediate variable is the first average value or the second average value, or the weighted sum of the first average value and the second average value.

[0014] Preferably, the method for calculating the first average value representing the common characteristics pursued by the brands of the first building material product and the second building material product in the product combination comprises the steps of:

[0015] A1, classifying the first building material product and the second building material product constituting the product combination by brand, and assigning the same brand classification grade value to each brand in each category;

[0016] A2, calculating a first proportion of a first brand classification grade value of the first building material product of the corresponding brand that the customer has historically purchased relative to the brand classification grade values ​​corresponding to each brand of the first building material product, and calculating a second proportion of a second brand classification grade value of the second building material product of the corresponding brand that the same customer has historically purchased relative to the brand classification grade values ​​corresponding to each brand of the second building material product;

[0017] A3: Determine whether a first deviation between the first proportion and the second proportion is less than a preset first deviation threshold.

[0018] If so, calculating a first average of the first brand classification grade value and the second brand classification grade value as the common characteristics pursued by the customer for the first building material product and the second building material product;

[0019] If not, it is determined that the customer does not have the common characteristics of brand pursuit for the first building material product and the second building material product.

[0020] Preferably, the method for calculating the second average value representing the common characteristics of the consumption capacity of the first building material product and the second building material product in the product combination comprises the steps of:

[0021] B1, classifying the selling price ranges of each brand in each brand grade classification of the first building material product and the second building material product constituting the product combination, and assigning corresponding price range classification grade values ​​to each brand in the same brand grade classification;

[0022] B2, calculating a first ratio of a first price range classification level value corresponding to the selling price range within which the purchase price of the first building material product of the corresponding brand purchased by the customer falls, to the price range classification level values ​​corresponding to each brand at the same brand classification level of the first building material product, and obtaining a second ratio of a second price range classification level value corresponding to the selling price range within which the purchase price of the second building material product of the corresponding brand purchased by the same customer falls, to the price range classification level values ​​corresponding to each brand at the same brand classification level of the second building material product;

[0023] B3, determining whether a second deviation between the first ratio and the second ratio is less than a preset second deviation threshold,

[0024] If so, calculating a second average of the first price range classification grade value and the second price range classification grade value as the common characteristic of the customer's consumption ability for the first building material product and the second building material product;

[0025] If not, it is determined that the customer does not have the common consumption capacity characteristics for the first building material product and the second building material product.

[0026] Preferably, in step B1, the method for assigning the price range classification level values ​​corresponding to the brands under the same brand classification level comprises the steps of:

[0027] B11, determine whether there is any overlapping price range between brands in the same brand classification level.

[0028] If so, the impact of lowering and / or raising prices on the price range classification value is calculated for each brand with overlapping product price ranges, and then the process goes to step B12;

[0029] If not, assign the price range classification level values ​​from high to low to the selling price ranges corresponding to the brands under the same brand classification level according to the highest price in the product price range;

[0030] B12, assigning the same price intersection interval classification level value to the price intersection intervals in the selling price ranges of the two brands for solving the influence amount, and then adding the influence amount to the price intersection interval classification level value to form the price interval classification level value corresponding to the brand with the price reduction and / or price increase.

[0031] Preferably, the influence amount in step B11 is calculated by the following method steps:

[0032] B111, segmenting the first selling price range and the second selling price range corresponding to the first brand and the second brand at the same brand classification level participating in the influence calculation, respectively, with a fixed step size, to obtain a plurality of first price sub-ranges associated with the first selling price range and a plurality of second price sub-ranges associated with the second selling price range;

[0033] B112: Count the number of segments with the same price sub-interval as a reference number for solving the influence amount, and count the number of first segments in which the price in each of the first price sub-intervals and / or each of the second price sub-intervals is less than the minimum price in each of the same price sub-intervals, and / or count the number of second segments in which the price is greater than the maximum price in each of the same price sub-intervals;

[0034] B113, calculating a first ratio and a second ratio of the first segment number and / or the second segment number to the reference number respectively;

[0035] B114, calculate the first product of the first ratio and the price intersection interval classification level value as a negative downward influence amount, and / or calculate the second product of the second ratio and the price intersection interval classification level value as a positive upward influence amount.

[0036] Preferably, in step S3, the mutual recommendation influence relationship is constructed by the following method steps:

[0037] S31, constructing an influence relationship matrix of a product combination consisting of the proposed product and a building material product in the third recommendation list, including the maximum first variable value, and performing element value normalization processing, and then calculating the positive influence factor of the proposed product and the building material product in the product combination and reverse impact factors ;

[0038] S32, calculate the positive impact factor of product portfolio association and reverse impact factors The sixth deviation serves as the mutual recommendation influence relationship for constructing the product portfolio.

[0039] Preferably, in step S31, the constructed influence relationship matrix including the maximum first variable value is expressed by the following expression (1):

[0040]

[0041] In expression (1), represents the influence relationship matrix;

[0042] Indicates that the product to be recommended is Class characteristics eigenvalues; Indicates that a building material product in the third recommendation basis list that forms a product combination with the proposed recommended product is in the first Class characteristics eigenvalues, , Indicates the Class or The number of eigenvalues ​​under the class characteristics; , Indicates the number of characteristic types of the proposed product or the building material product; 、 represents a first average value and a second average value respectively representing the common characteristics of brand pursuit and the common characteristics of consumption capacity used to solve the maximum first variable value;

[0043] Positive impact factor Calculated by the following steps:

[0044] S311, calculate the pair matrix calculating a first influence value of each element data pair generated from the proposed product in the normalized matrix after normalization processing on generating the first average value, and calculating a second influence value of each element data pair generated from the building material product that constitutes a product combination with the proposed product in the normalized matrix on generating the second average value;

[0045] S312: Calculate the weighted average of the first influence value and the second influence value as the positive influence factor of the proposed product ;

[0046] In step S311, the calculation method of the first influence value or the second influence value is expressed by the following formula (2):

[0047]

[0048] In formula (2), , represents the first impact value, represents the second impact value;

[0049] ;

[0050] express The normalized value of

[0051] Representation matrix The Rank the eigenvalues ​​of the elements of the column;

[0052] express In calculation The weight of time;

[0053] and The values ​​are the same or different.

[0054] The present invention has the following beneficial effects:

[0055] 1. Based on the screening criteria of whether each customer has similar common purchasing characteristics in the product combination including the proposed recommended product in the past, a customer list is screened in advance as the basis for determining whether the proposed recommended customer is the target recommended customer. Subsequently, whether the proposed recommended customer is the target recommended customer is determined based on the historical purchase data of each customer in the customer list. This greatly reduces the amount of data analysis and is conducive to improving the analysis speed of simultaneous product recommendations for a large customer group in scenarios where complex recommendation analysis and calculations are required to consider the mutual recommendation influence relationship between products.

[0056] 2. By introducing intermediate variables and calculating positive and reverse influencing factors, a mutual recommendation influence relationship is established between the first building material product and the second building material product, and the impact of differences in product characteristics between different products on the accuracy of product recommendations is taken into account. In the future, by finding the common purchasing characteristics of the intended recommended customers and historical purchasing customers, and based on the maximum first variable value of the intermediate variable of the intended recommended customer, the target recommended products that can meet the brand pursuit and consumption capacity of the intended recommended customers can be accurately screened from a large number of intended recommended products, thereby greatly improving the accuracy of building material product recommendations.

[0057] 3. By identifying and extracting the common purchasing features of the same customer for the first building material product and the second building material product, and introducing the common purchasing features as intermediate variables, it becomes possible to establish a mutual recommendation history influence relationship between the first building material product and the second building material product.

[0058] 4. When calculating the impact value used as the basis for calculating the historical positive impact factor or the historical negative impact factor When the normalized element value Common purchase characteristics after normalization The ratio of quantifies the influence of each characteristic value under each type of characteristics of the first building material product and the second building material product on the common purchasing characteristics of customers, and Corresponding weight To correct this degree of influence, the solved historical positive influence factor or historical negative influence factor can reflect the common purchasing characteristics of customers who purchase the first building material product or the second building material product.

[0059] 5. When calculating the historical positive impact factor, the common characteristics of brand pursuit of the first building material product (i.e., the first average value) are used as the basis for calculating the first impact value, and the common characteristics of consumption capacity of the second building material product (i.e., the second average value) are used as the basis for calculating the second impact value. The historical positive impact factor is obtained by taking the weighted sum of the first impact value and the second impact value, which represents the influence of the same customer's historical consumption capacity for the second building material product on the brand pursuit when purchasing the first building material product. Similarly, when calculating the historical reverse impact factor, the common characteristics of brand pursuit of the second building material product are used as the basis for calculating the first impact value, and the common characteristics of consumption capacity of the first building material product are used as the basis for calculating the second impact value. The historical reverse impact factor is obtained by taking the weighted sum of the first impact value and the second impact value, which represents the influence of the same customer's historical consumption capacity for the first building material product on the brand pursuit when purchasing the second building material product, making it possible to subsequently accurately recommend the proposed products to the proposed customers. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0061] Figure 1 This is a diagram of the implementation steps of a product recommendation method based on data pre-screening and introduction of intermediate variables provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0062] The technical solution of the present invention will be further described below with reference to the accompanying drawings and through specific implementation methods.

[0063] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0064] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inside", "outside" and the like indicate directions or positional relationships, they are based on the directions or positional relationships shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0065] In the description of the present invention, unless otherwise expressly specified or limited, when the term "connection" or the like appears to indicate a connection relationship between components, such term should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be internal communication between two components or an interaction between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood in specific circumstances.

[0066] In this embodiment, taking the recommendation of building materials products as an example, the provided product recommendation method based on data pre-screening and introduction of intermediate variables is explained.

[0067] The embodiment of the present invention provides a product recommendation method based on data pre-screening and introduction of intermediate variables, such as Figure 1 As shown, the steps include:

[0068] S1, using the customer's historical purchases of the product portfolio including the product to be recommended as a screening condition, and screening out a list of historical customers as a basis for determining whether the customer to be recommended is a target customer;

[0069] Assume that a product combination consists of a first building material product and a second building material product. The first building material product is assumed to be floor heating flooring, and the second building material product is assumed to be central air conditioning. Furthermore, assume that the product to be recommended is building material a, and that there are 100 historical purchasing customers, cuu1-cuu100. Of these 100 customers, assume that each of 30 has previously purchased product a, and each has also purchased at least one other type of building material product, and that at least one of these other products forms a product combination with building material a. After calculating the historical recommendation impact relationship between the two building material products in the product combination, these 30 historical purchasing customers are determined to be customers whose historical product combinations include the product to be recommended.

[0070] Then, these 30 historical purchasers are screened based on similar common purchasing characteristics for product combinations, creating a list of reference customers to determine whether a prospective recommendation customer is a target customer. For example, let's take customers cuu1, cuu2, and cuu3 among the 30 customers. Assume that customer cuu1's historical purchases, and the product combination for which a historical recommendation relationship has been established, include building materials a and b; customer cuu2's historical purchases, and the product combination for which a historical recommendation relationship has been established, include building materials a and c; and customer cuu3's historical purchases, and the product combination for which a historical recommendation relationship has been established, include building materials a and d. The rule for determining which of these three customers share similar common purchasing characteristics for product combinations is: the brand rating of the first building material product purchased by different customers is the same, the brand classification level of the second building material product purchased by different customers is the same, the deviation between the first average values ​​of the common brand pursuit characteristic of the product combination is less than a preset first common deviation threshold, and the deviation between the second average values ​​of the common consumption capacity characteristic of the product combination is less than a preset second common deviation threshold. For example, assuming that the product to be recommended is building material product a and is also the first building material product in the product portfolio, then building material product b purchased by customer cuu1 is the second building material product in the first product portfolio constructed by building material products a and building material products b; similarly, building material product c purchased by customer cuu2 is the second building material product in the second product portfolio constructed by building material products a and building material products c; building material product d purchased by customer cuu3 is the second building material product in the third product portfolio constructed by building material products a and building material products d. Assume that the brand classification level of the brand of building material product a purchased by customer cuu1 is the first level, and the brand classification level of the brand of building material product b is the first level; the brand classification level of the brand of building material product c purchased by customer cuu2 is also the first level; the brand classification level of the brand of building material product d purchased by customer cuu3 is the second level. Since the brand classification level of the brand of the first building material product (building material product a) purchased by customer cuu1 and customer cuu2 is the same, and the brand classification level of the brand of the first building material product (the brand classification level of the building material product b purchased by customer cuu1 is the first level, and the brand classification level of the brand of the building material product c purchased by customer cuu2 is also the first level) is achieved, the first condition for determining that customer cuu1 and customer cuu2 have similar common purchasing characteristics is met, that is: the brand classification level of the brand of the first building material product in the product portfolio purchased by different customers is the same, and the brand classification level of the brand of the second building material product purchased is the same. The brand classification level of the second building material product purchased by customer cuu3, namely building material product d, is the second level, which is different from the brand classification level of the second building material product purchased by cuu1 and cuu2. Therefore, cuu3 is filtered out from the 30 customers.

[0071] Furthermore, it is necessary to determine whether the second condition of similar common purchasing characteristics is met for customers cuu1 and cuu2, namely, whether the deviation between the first average values ​​of the common characteristics of brand pursuit as a product combination is less than the preset first common deviation threshold, and whether the second condition of the common characteristics of consumption capacity as a product combination is less than the preset second common deviation threshold is met. For example, the first average value of the common characteristics of brand pursuit of building materials products a and building materials products b that customer cuu1 has purchased in the past is assumed to be , the second average value representing the common characteristics of consumption capacity is ; cuu2 The first average value of the common characteristics of the brand pursuit of building materials products a and c that constitute the product portfolio that customers have purchased in the past is assumed to be , the second average value representing the common characteristics of consumption capacity is , then when and The absolute value of the difference (deviation) is less than the preset first common deviation threshold, and and When the absolute value of the difference (deviation) is less than the preset second commonality deviation threshold, the second condition with similar common purchase characteristics is determined to be achieved, and customers cuu1 and cuu2 are included in the customer list; otherwise, customers cuu1 or cuu2 or both cuu1 and cuu2 are filtered out from the 30 customers (when there is no other historical purchase that meets the above-mentioned second achievement condition with cuu1, cuu1 is filtered out from the 30 customers, and the filtering condition for cuu2 is the same. When neither cuu1 nor cuu2 has other historical purchasing customers that meet the second achievement condition, cuu1 and cuu2 are filtered out together).

[0072] The following specifically explains how to calculate the first average value of the common characteristics of brand pursuit for the first and second building materials products in the product portfolio, as well as the second average value of the common characteristics of consumption capacity:

[0073] The first average value representing the common characteristics pursued by brands is calculated using the following method steps:

[0074] A1: Classify the first building material product and the second building material product that constitute the product portfolio into brand categories, and then assign the same brand classification grade value to each brand in each category;

[0075] For example, assume that the brands of floor heating flooring, a first building material product, include BP1, BP2, BP3, and BP4. Assuming that the brand level of brands BP1 and BP2 is the first level, and the brand level of brands BP3 and BP4 is the second level, brands BP1 and BP2 are assigned the same brand classification level value, while brands BP3 and BP4 are assigned another identical brand classification level value. Brand classification level values ​​at different classification levels are assigned in descending order according to the classification level, i.e., the brand classification level value of each brand at the first level is greater than the brand classification level value of each brand at the second level.

[0076] A2 calculates a first proportion of the first brand classification grade value of a first building material product of the corresponding brand purchased by the customer in the total brand classification grade values ​​corresponding to each brand of the first building material product, and calculates a second proportion of the second brand classification grade value of a second building material product of the corresponding brand purchased by the same customer in the total brand classification grade values ​​corresponding to each brand of the second building material product;

[0077] For example, if the brand of floor heating floor that a customer has purchased is BP1, and the first brand classification level value corresponding to brand BP1 is assumed to be "1", the brand classification level value of brand BP2 of floor heating floor is also "1", and the brand classification level values ​​corresponding to brands BP3 and BP4 are both 0.8, then the first proportion is The calculation method of the second proportion is the same as the first proportion and will not be repeated here.

[0078] A3, determining whether a first deviation between the first proportion and the second proportion is less than a preset first deviation threshold,

[0079] If so, calculating a first average of the first brand classification grade value and the second brand classification grade value as the common characteristics of the customer's pursuit of brands for the first building material product and the second building material product;

[0080] If not, it is determined that the customer does not have common brand pursuit characteristics for the first building material product and the second building material product.

[0081] For example, assuming the second proportion is The first deviation of the second proportion for the first proportion is the absolute value of the difference between the two, which is 0.035. Assuming that the first deviation threshold is 0.05, the "if" condition of step A3 is met, and the average value of the first brand classification grade value and the second brand classification grade value (defined as the first average value) is calculated as the common characteristics of customers' pursuit of the brands of the first building material product and the second building material product that constitute the product portfolio.

[0082] Two aspects need to be explained here. The first aspect is that the setting of the first deviation threshold is related to the number of brands of the first building material product and the second building material product, and the number of brand grade classifications. The first deviation threshold can be adjusted to capture the size difference in the common characteristics pursued by the brands of the first building material product and the second building material product. Since how to set the first deviation threshold is not within the scope of the rights claimed in this application, it will not be explained in detail. The second aspect is that the brand classification grade values ​​of each brand of the first building material product and the second building material product at the same classification level are preferably the same.

[0083] The second average value representing the common characteristics of consumption capacity is obtained through the following method steps:

[0084] B1. Classify the selling price ranges of each brand in each brand grade category of the first building material product and the second building material product that constitute the product combination, and assign corresponding price range classification grade values ​​to each brand in the same brand grade category;

[0085] For example, for the above-mentioned floor heating floor as the first building material product, the selling price ranges of brands BP1 and BP2 with the first brand classification level are assumed to be RMB 200-400 and RMB 250-450 respectively. Then, the brand BP1 under the first level is assigned a price range classification level value corresponding to the selling price range of RMB 200-400, and the brand BP2 is assigned a price range classification level value corresponding to the selling price range of RMB 250-450.

[0086] The method for assigning the price range classification level value corresponding to each brand under the same brand classification level includes the following steps:

[0087] B11, determine whether there is any overlapping price range between brands in the same brand classification level.

[0088] If so, the impact of lowering and / or raising prices on the price range classification level is calculated for each brand with overlapping product price ranges, and then the process proceeds to step B12;

[0089] If not, then assign price range classification values ​​from high to low to the corresponding selling price ranges of each brand under the same brand classification level according to the highest price in the product price range;

[0090] For example, the selling price range of brand BP1 is 200-400, and the selling price range of brand BP2 is 250-450. It is determined that there is an overlapping area of ​​brand product prices between brands BP1 and BP2 with the same brand classification level, and the overlapping area is 250-400. Then the "if yes" condition of step B11 is met.

[0091] When the "if" condition of step B11 is met, the influence amount in step B11 is calculated by the following method steps:

[0092] B111, segmenting the first selling price range and the second selling price range corresponding to the first brand and the second brand at the same brand classification level participating in the influence calculation with a fixed step size to obtain a plurality of first price sub-ranges associated with the first selling price range and a plurality of second price sub-ranges associated with the second selling price range;

[0093] For example, for the above-mentioned brand BP1 (first brand) and brand BP2 (second brand), the first selling price range (200-400) and the second selling price range (250-450) corresponding to each other are segmented with a fixed step size of "50", and several first price sub-ranges associated with the first selling price range are obtained, namely: 200-250, 250-300, 300-350, 350-400, and several second price sub-ranges associated with the second selling price range are obtained, namely: 250-300, 300-350, 350-400, and 400-450, and then the process goes to step B112.

[0094] B112: Count the number of segments with the same price sub-interval as a reference number for solving the influence quantity, and count the number of first segments in which the price in each first price sub-interval and / or second price sub-interval is less than the minimum price in each same sub-interval, and / or count the number of second segments in which the price is greater than the maximum price in each same price sub-interval.

[0095] Continuing with the above example, the same price subranges for 200-250, 250-300, 300-350, 350-400 and 250-300, 300-350, 350-400, and 400-450 are 250-300, 300-350, and 350-400. Therefore, the number of segments with the same price subrange is "3," which serves as a reference for determining the impact. The minimum price in these three identical subranges is 250, and the maximum price is 400. Therefore, the first price subrange of 200-250 satisfies the condition that the price is less than the minimum price in each identical subrange. Therefore, the first segment number is "1." Using the same statistical method, the second segment number is also "1," and the process then proceeds to step B113.

[0096] B113, calculating a first ratio and a second ratio of the first segment number and / or the second segment number to the reference number respectively;

[0097] Continuing with the above example, the first ratio and the second ratio are both one-third.

[0098] B114, calculate the product of the first ratio and the price intersection interval classification level value (defined as the first product) as a negative downward influence amount, and / or calculate the product of the second ratio and the price intersection interval classification level value (defined as the second product) as a positive upward influence amount.

[0099] Continuing with the above example, 250-400 is the price intersection range of the first selling price range and the second selling price range. This price intersection range has the same price intersection range classification level value in the first selling price and the second selling price. Assuming it is defined as V1, the product of the first ratio and V1 is calculated as the downward influence amount, and the value of the downward influence amount is negative; the product of the second ratio and V1 is calculated as the upward influence amount, and the value of the upward influence amount is positive.

[0100] The method of assigning the classification grade value of the price intersection interval is not within the scope of the rights protected by this application, and has no direct impact on the solution of the technical problem of the present invention, so it will not be explained in detail.

[0101] When the "If No" condition in step B11 is met, the following example shows how to assign a value to the price range classification level:

[0102] Assume that the first selling price range is 200-400, and the second selling price range is 450-600, and the first selling price range and the second selling price range do not have a price overlapping range, and the highest prices of the first selling price range and the second selling price range are 400 and 600 respectively, then the price range classification level value assigned to the highest price "600" is larger than that assigned to "400".

[0103] After the determination in step B11 is completed, the method for assigning the price range classification level values ​​corresponding to each brand in the same brand classification level proceeds to step:

[0104] B12 assigns the same price intersection classification rank value to the price intersection intervals within the selling price ranges of the two brands whose influence values ​​were calculated in step B11. The influence value is then added to the price intersection classification rank value to form the price interval classification rank value corresponding to the brand that has the effect of lowering and / or raising prices. It should be noted that the influence value corresponding to lowering prices is negative, while the influence value corresponding to raising prices is positive.

[0105] After assigning the corresponding price range classification level values ​​to each brand in the same brand classification level through steps B11-B12, analyze and extract the common characteristics of consumption capacity and proceed to the following steps:

[0106] B2, calculating a first ratio of a first price range classification grade value corresponding to the selling price range within which the purchase price of a first building material product of the corresponding brand purchased by the customer falls, to the price range classification grade values ​​corresponding to each brand at the same brand classification grade of the first building material product, and obtaining a second ratio of a second price range classification grade value corresponding to the selling price range within which the purchase price of a second building material product of the corresponding brand purchased by the same customer falls, to the price range classification grade values ​​corresponding to each brand at the same brand classification grade of the second building material product;

[0107] For example, the first building material product purchased by customer U in the past was floor heating flooring of brand BP1, with a purchase price of 230 yuan / square meter. The floor heating flooring of brand BP1 has three models, and the selling price ranges are 150-250 yuan, 250-350, and 350-500 respectively. The price of 230 yuan / square meter falls into the selling price range of 150-250, and the first price range classification level value is the price range classification level value corresponding to the selling price range of 150-250.

[0108] Assume that brands with the same brand classification level as BP1 include BP2 and BP3. There are 8 price range classification level values ​​under BP1, BP2 and BP3, which are , Assume that the price range of 150-250 corresponds to the price range classification level value , then the first ratio is The calculation principle of the second ratio is the same as that of the first ratio and will not be repeated here.

[0109] B3, determining whether a second deviation between the first ratio and the second ratio (the absolute value of the difference between the first ratio and the second ratio) is less than a preset second deviation threshold,

[0110] If so, then calculate the average of the first price range classification grade value and the second price range classification grade value (defined as the second average value) as the common characteristic of the customer's consumption capacity for the first building material product and the second building material product that constitute the product portfolio;

[0111] If not, it is determined that the customer does not have common consumption capacity characteristics for the first building material product and the second building material product.

[0112] It should be noted here that, similar to the principle of setting the first deviation threshold, the specific setting of the second deviation threshold is related to factors such as the type of the first building material product and the second building material product, the number of brands within the same brand classification level, the number of selling price ranges for each brand, the width of the selling price ranges, and whether there are overlapping price ranges between the selling price ranges. By considering these factors and adjusting the second deviation threshold, the degree of tolerance for differences in the determination of "commonality" in the consumer spending power characteristics when purchasing the first building material product and the second building material product is characterized. Setting the second deviation threshold, as well as the first deviation threshold, is a complex process. Since the specific setting is not within the scope of the rights claimed in this application, it will not be explained in detail.

[0113] After the customer list is screened out in step S1, the product recommendation method based on data pre-screening and introduction of intermediate variables provided in this embodiment proceeds to step:

[0114] S2, filtering out products that have been purchased by each customer in the customer list based on the customer history of the recommended customer, to form a third recommendation basis list;

[0115] For example, assuming that the customer list includes customer cuu1, customer cuu2 and customer cuu3, cuu1 is assumed to have purchased building materials products a and building materials products b in the past, cuu2 has purchased building materials products a and building materials products c in the past, and cuu3 has purchased building materials products a and building materials products d in the past, then building materials products a, building materials products b, building materials products c and building materials products d will be formed into the third recommendation list.

[0116] It should be noted here that when the product to be recommended is building material product a, the customer to be recommended has never purchased building material product a. In this application, whether building material product a, which is the product to be recommended, will actually be recommended as the target recommended product to this customer to be recommended is mainly determined by two factors: first, the similarity between the customer characteristics of the customer to be recommended and the historical purchasing customers referred to in the third recommendation list. Second, when the customer to be recommended purchases the product to be recommended based on the purchase characteristics of the customer to be recommended who has purchased at least one building material product in the third recommendation list, whether the mutual recommendation influence relationship between the product to be recommended and the building material products in the third recommendation list meets the recommendation requirements.

[0117] The first determining factor mentioned above has screened out a list of customers with similar customer characteristics to the customer to be recommended through step S1 using "similar common purchasing characteristics" as the screening condition.

[0118] In the second determining factor described above, whether the mutual recommendation influence relationship between the intended recommendation and the building material products in the third recommendation list meets the recommendation requirements when the intended recommendation customer purchases the intended product based on the purchase characteristics of the intended recommendation customer who has purchased at least one building material product in the third recommendation list is determined by the method described in step S3 below:

[0119] S3, calculate the first variable value of the intermediate variable between each product in the third recommendation list of the customer's historical purchase history, and extract the maximum first variable value. Then, based on the maximum first variable value, calculate the positive impact factor of the product to be recommended and each product in the third recommendation basis list in sequence. and reverse impact factors , and further calculate the mutual recommendation influence relationship between the proposed recommended product and each building material product in the third recommendation basis list;

[0120] The method for calculating the variable value of the intermediate variable between two building material products is described in detail in steps A1-A3 and B1-B3 above and will not be repeated here. If there are multiple variable values ​​(defined as first variable values) for the intermediate variable between each product in the third recommendation list based on the customer's historical purchase history, that is, if the customer has purchased more than two products in the third recommendation list, the maximum value of each first variable value is extracted, i.e., the maximum first variable value. This maximum first variable value is then used to calculate the mutual recommendation influence relationship between the product to be recommended and each building material product in the third recommendation basis list.

[0121] The mutual recommendation influence relationship in step S3 is constructed by the following method steps:

[0122] S31, constructing an influence relationship matrix of a product combination consisting of the proposed product and a building material product in the third recommendation list, including the largest first variable value, and performing element value normalization processing, and then calculating the positive influence factor between the proposed product in the product combination and the building material product and reverse impact factors ;

[0123] The constructed influence relationship matrix containing the maximum first variable value is expressed by the following expression (1):

[0124]

[0125] In expression (1), Represents the influence relationship matrix;

[0126] Indicates the product to be recommended in Class characteristics eigenvalues; Indicates that a building material product in the third recommendation basis list that forms a product combination with the proposed recommended product is in the first Class characteristics eigenvalues, , Indicates the Class or The number of eigenvalues ​​under the class characteristics; , Indicates the number of characteristic types of the product or building material product to be recommended; 、 Represents the first average value and the second average value respectively representing the common characteristics of brand pursuit and the common characteristics of consumption ability used to solve the value of the first variable.

[0127] For example, the characteristics of floor heating floors, a proposed product, include comfort, environmental protection, energy saving, and price. Comfort characteristics include thermal conductivity, heat resistance, constant temperature radiation, thermal stability, noise reduction, and floor thickness. The characteristics of building materials products such as central air conditioners in the third recommendation basis list that form a product combination with the proposed product, and the specific features within each characteristic category, are not listed separately.

[0128] It should be noted here that when constructing an influence relationship matrix for two building materials products (such as the first building material product and the second building material product) purchased by a customer in the past, the 、 It represents the first average value and the second average value of the common characteristics of brand pursuit and the common characteristics of consumption capacity, which respectively represent the variable values ​​used to solve the intermediate variables of the first building material product and the second building material product.

[0129] Positive impact factor Calculated by the following steps:

[0130] S311, calculate the pair matrix Calculating a first influence value for each element data pair generated from the proposed product in the normalized matrix after normalization processing to generate a first average value, and calculating a second influence value for each element data pair generated from the building material product that constitutes a product combination with the proposed product in the normalized matrix to generate a second average value;

[0131] Pair Matrix The normalization method is expressed as follows:

[0132]

[0133] Represents the matrix The Rank The normalized value of the column element value after normalization;

[0134] Representation matrix The Rank The element value of the column;

[0135] Representation matrix The number of columns in

[0136] Representation matrix The number of rows in .

[0137] In step S311, the calculation method of the first influence value or the second influence value is expressed by the following formula (2):

[0138]

[0139] In formula (2), , represents the first impact value, represents the second impact value;

[0140] ;

[0141] express The normalized value of

[0142] Representation matrix The Rank the eigenvalues ​​of the elements of the column;

[0143] express In calculation The weight of time;

[0144] and The values ​​of are the same or different. They are preferably the same to simplify the calculation process of positive or negative impact factors. It is related to the importance of the feature type and each feature under the feature type in calculating the common purchase feature, and is related to the product similarity between the first building material product (such as the product to be recommended) and the second building material product (such as the building material product in the third recommendation basis list). The method of granting is not within the scope of the rights protected by this application, so it will not be explained in detail.

[0145] S312, calculating the weighted average of the first impact value and the second impact value (ie 、 The weighted average of the ... ;

[0146] It should be noted here that the reverse impact factor The calculation method and The calculation principle of the method is similar, the difference is that the calculation When , the weighted average of the first influence value of each element data pair generated from the second building material product to generate the first average value and the second influence value of each element data pair generated from the first building material product to generate the second average value in the normalized matrix is In addition, when calculating the reverse impact factor, it is optional to .

[0147] Complete the positive impact factors on the products to be recommended and reverse impact factors After the calculation of , the method for constructing the mutual recommendation influence relationship in step S3 proceeds to the steps:

[0148] S32, calculate the positive impact factor of product portfolio association and reverse impact factors Deviation (defined as the sixth deviation, and The absolute value of the difference between and is used as the mutual recommendation influence relationship for the construction of the product portfolio.

[0149] What needs to be explained here is that the mutual recommendation influence relationship constructed for two building materials products purchased by customers in the past, such as the first building materials product and the second building materials product, is defined as the mutual recommendation historical influence relationship. The mutual recommendation historical influence relationship is the same as the construction method of the above-mentioned mutual recommendation influence relationship in principle. The difference is that when constructing the mutual recommendation historical influence relationship, the mutual recommendation historical influence relationship is the average value (defined as the third average value) of the third deviations solved for the same product combination by different customers with similar common purchase characteristics. The third deviation is the historical positive influence factor between the first building materials product and the second building materials product that constitute the product combination. and historical reverse impact factor The absolute value of the difference. and , and The calculation principle is the same as , so it will not be repeated here. It should also be noted here that having similar common purchasing characteristics for the same product combination means that when different customers purchase the first building material product and the second building material product in the product combination, the brand classification level of the first building material product purchased is the same, the brand classification level of the second building material product purchased is the same, and the deviation between the first average values ​​of the common characteristic of brand pursuit of the product combination is less than a preset first common deviation threshold, and the deviation between the second average values ​​of the common characteristic of consumption capacity of the product combination is less than a preset second common deviation threshold.

[0150] After calculating the mutual recommendation influence relationship between the proposed product and each building material product in the third recommendation basis list in step S3, the product recommendation method based on data pre-screening and introduction of intermediate variables provided in this embodiment proceeds to step:

[0151] S4, solve the fifth deviation (the absolute value of the difference between the mutual recommendation influence relationship and the mutual recommendation historical influence relationship) between the mutual recommendation influence relationship of the associated proposed recommended customers calculated for the same product combination and the mutual recommendation historical influence relationship of each historical purchasing customer referred to in the associated third recommendation basis list, and use the proposed recommended customers whose fifth deviation is less than the preset fifth deviation threshold as the target recommended customers for recommending the proposed recommended products.

[0152] For example, after step S3, for the same prospective customer cus1, the first mutual recommendation influence relationship between products a and b, and the second mutual recommendation influence relationship between products a and c are calculated. Assume that historical purchasing customer cuu2 has previously purchased both products a and b, and the first mutual recommendation historical influence relationship has been calculated for products a and b; and historical purchasing customer cuu3 has previously purchased both products a and c, and the second mutual recommendation historical influence relationship has been calculated for products a and c. The fifth deviation between the first mutual recommendation influence relationship and the first mutual recommendation historical influence relationship is calculated; and the fifth deviation between the second mutual recommendation influence relationship and the second mutual recommendation historical influence relationship is calculated. As long as at least one of these fifth deviations is less than the preset fifth deviation threshold, prospective customer cus1 will be selected as the target customer for product a.

[0153] Finally, it should be emphasized that the building materials product in this embodiment is only an illustrative example product. As long as the common purchasing characteristics of customers between the two products can be extracted, the product recommendation method provided in this application can be used to achieve accurate and efficient product recommendations.

[0154] In summary, the present invention uses the similar common purchase characteristics of the product combinations that each customer has purchased in the past, including the product to be recommended, as a screening condition, and pre-screens a list of customers as a basis for determining whether the customer to be recommended is the target recommended customer. Subsequently, whether the customer to be recommended is the target recommended customer is determined based on the historical purchase data of each customer in the customer list, which greatly reduces the amount of data analysis and is beneficial for improving the analysis speed of product recommendations for a large customer group at the same time in scenarios where complex recommendation analysis and calculation are required to consider the mutual recommendation influence relationship between products. By introducing intermediate variables and calculating positive and negative influence factors, a mutual recommendation influence relationship is constructed between the first building material product and the second building material product, and the influence of the differences in product characteristics between different products on the accuracy of product recommendations is taken into account, so that the common purchase characteristics of the customer to be recommended and the historical purchasing customers can be found in the future, and based on the maximum first variable value of the intermediate variable of the customer to be recommended, the target recommended product that can meet the brand pursuit and consumption capacity of the customer to be recommended can be accurately screened from a large number of products to be recommended, thereby greatly improving the accuracy of building material product recommendations.

[0155] It should be noted that the above-described specific embodiments are merely preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that various modifications, equivalent substitutions, and variations may be made to the present invention. However, as long as these modifications do not depart from the spirit of the present invention, they are intended to be within the scope of protection of the present invention. Furthermore, certain terms used in the specification and claims of this application are not intended to be limiting and are provided solely for ease of description.

Claims

1. A product recommendation method based on data pre-screening and introduction of intermediate variables, characterized in that: Including steps: S1, using the customer's historical purchase history of the product combination including the product to be recommended as a screening condition, and screening out a list of reference customers as a basis for determining whether the customer to be recommended is a target recommendation customer; S2, filtering out products that have been purchased by the customer to be recommended and each customer in the customer list to form a third recommendation basis list; S3, calculate the first variable value of the intermediate variable between each product in the third recommendation basis list of the customer's historical purchase history, and extract the maximum first variable value, and then calculate the positive impact factor of the product to be recommended and each building material product in the third recommendation basis list in sequence based on the maximum first variable value and reverse impact factors , and further constructing a mutual recommendation influence relationship between the proposed recommended product and each building material product in the third recommendation basis list; S4, calculating the fifth deviation between the mutual recommendation influence relationship between the proposed recommended customers calculated for the same product combination and the mutual recommendation historical influence relationship between the historical purchasing customers indicated in the third recommendation basis list, and selecting the proposed recommended customers who meet the condition that the fifth deviation is less than a preset fifth deviation threshold as target recommended customers for recommending the proposed recommended product; In step S1, the rule for determining that each customer has similar common purchasing characteristics for the product combination is: the brand classification level of the first building material product purchased by different customers in the product combination is the same, the brand classification level of the second building material product purchased by different customers is the same, and the deviation between the first average values ​​of the common characteristics of brand pursuit of the product combination is less than a preset first common deviation threshold, and the deviation between the second average values ​​of the common characteristics of consumption capacity of the product combination is less than a preset second common deviation threshold; The intermediate variables include the common characteristics of brand pursuit and the common characteristics of consumption capacity associated with the same product combination; The variable value of the intermediate variable is the first average value or the second average value, or a weighted sum of the first average value and the second average value; The method for calculating the second average value representing the common characteristics of the consumption capacity of the first building material product and the second building material product in the product combination comprises the steps of: B1, classifying the selling price ranges of each brand in each brand grade classification of the first building material product and the second building material product constituting the product combination, and assigning corresponding price range classification grade values ​​to each brand in the same brand grade classification; B2, calculating a first ratio of a first price range classification level value corresponding to the selling price range within which the purchase price of the first building material product of the corresponding brand purchased by the customer falls, to the price range classification level values ​​corresponding to each brand at the same brand classification level of the first building material product, and obtaining a second ratio of a second price range classification level value corresponding to the selling price range within which the purchase price of the second building material product of the corresponding brand purchased by the same customer falls, to the price range classification level values ​​corresponding to each brand at the same brand classification level of the second building material product; B3, determining whether a second deviation between the first ratio and the second ratio is less than a preset second deviation threshold, If so, calculating a second average of the first price range classification grade value and the second price range classification grade value as the common characteristic of the customer's consumption ability for the first building material product and the second building material product; If not, it is determined that the customer does not have the common consumption capacity characteristics for the first building material product and the second building material product; In step B1, the method for assigning the price range classification level value corresponding to each brand under the same brand classification level includes the following steps: B11, determine whether there is any overlapping price range between brands in the same brand classification level. If so, the impact of lowering and / or raising prices on the price range classification value is calculated for each brand with overlapping product price ranges, and then the process goes to step B12; If not, assign the price range classification level values ​​from high to low to the selling price ranges corresponding to the brands under the same brand classification level according to the highest price in the product price range; B12, assigning the same price intersection interval classification level value to the price intersection intervals in the selling price ranges of the two brands for solving the influence amount, and then adding the influence amount to the price intersection interval classification level value to form the price interval classification level value corresponding to the brand with the price reduction and / or price increase.

2. A product recommendation method based on data pre-screening and introduction of intermediate variables, characterized in that: Including steps: S1, using the customer's historical purchase history of the product combination including the product to be recommended as a screening condition, and screening out a list of reference customers as a basis for determining whether the customer to be recommended is a target recommendation customer; S2, filtering out products that have been purchased by the customer to be recommended and each customer in the customer list to form a third recommendation basis list; S3, calculate the first variable value of the intermediate variable between each product in the third recommendation basis list of the customer's historical purchase history, and extract the maximum first variable value, and then calculate the positive impact factor of the product to be recommended and each building material product in the third recommendation basis list in sequence based on the maximum first variable value and reverse impact factors , and further constructing a mutual recommendation influence relationship between the proposed recommended product and each building material product in the third recommendation basis list; S4, calculating the fifth deviation between the mutual recommendation influence relationship between the proposed recommended customers calculated for the same product combination and the mutual recommendation historical influence relationship between the historical purchasing customers indicated in the third recommendation basis list, and selecting the proposed recommended customers who meet the condition that the fifth deviation is less than a preset fifth deviation threshold as target recommended customers for recommending the proposed recommended product; In step S3, the mutual recommendation influence relationship is constructed through the following method steps: S31, constructing an influence relationship matrix of a product combination consisting of the proposed product and a building material product in the third recommendation list, including the maximum first variable value, and performing element value normalization processing, and then calculating the positive influence factor of the proposed product and the building material product in the product combination and reverse impact factors ; S32, calculate the positive impact factor of product portfolio association and reverse impact factors The sixth deviation serves as the mutual recommendation influence relationship for constructing the product portfolio.

3. The product recommendation method based on data pre-screening and introduction of intermediate variables according to claim 2 is characterized in that: In step S1, the rule for determining that each customer has similar common purchasing characteristics for the product combination is: the brand classification level of the first building material product purchased by different customers in the product combination is the same, the brand classification level of the second building material product purchased by different customers is the same, and the deviation between the first average values ​​of the common characteristics of brand pursuit of the product combination is less than a preset first common deviation threshold, and the deviation between the second average values ​​of the common characteristics of consumption capacity of the product combination is less than a preset second common deviation threshold; The intermediate variables include the common characteristics of the brand pursuit and the common characteristics of the consumption capacity associated with the same product combination; The variable value of the intermediate variable is the first average value or the second average value, or a weighted sum of the first average value and the second average value.

4. The product recommendation method based on data pre-screening and introduction of intermediate variables according to claim 1 or 3, characterized in that: The method for calculating the first average value representing the common characteristics pursued by the brands of the first building material product and the second building material product in the product portfolio comprises the steps of: A1, classifying the first building material product and the second building material product constituting the product combination by brand, and assigning the same brand classification grade value to each brand in each category; A2, calculating a first proportion of a first brand classification grade value of the first building material product of the corresponding brand that the customer has historically purchased relative to the brand classification grade values ​​corresponding to each brand of the first building material product, and calculating a second proportion of a second brand classification grade value of the second building material product of the corresponding brand that the same customer has historically purchased relative to the brand classification grade values ​​corresponding to each brand of the second building material product; A3: Determine whether a first deviation between the first proportion and the second proportion is less than a preset first deviation threshold. If so, calculating a first average of the first brand classification grade value and the second brand classification grade value as the common characteristics pursued by the customer for the first building material product and the second building material product; If not, it is determined that the customer does not have the common characteristics of brand pursuit for the first building material product and the second building material product.

5. The product recommendation method based on data pre-screening and introduction of intermediate variables according to claim 3 is characterized in that: The method for calculating the second average value representing the common characteristics of the consumption capacity of the first building material product and the second building material product in the product combination comprises the steps of: B1, classifying the selling price ranges of each brand in each brand grade classification of the first building material product and the second building material product constituting the product combination, and assigning corresponding price range classification grade values ​​to each brand in the same brand grade classification; B2, calculating a first ratio of a first price range classification level value corresponding to the selling price range within which the purchase price of the first building material product of the corresponding brand purchased by the customer falls, to the price range classification level values ​​corresponding to each brand at the same brand classification level of the first building material product, and obtaining a second ratio of a second price range classification level value corresponding to the selling price range within which the purchase price of the second building material product of the corresponding brand purchased by the same customer falls, to the price range classification level values ​​corresponding to each brand at the same brand classification level of the second building material product; B3, determining whether a second deviation between the first ratio and the second ratio is less than a preset second deviation threshold, If so, calculating a second average of the first price range classification grade value and the second price range classification grade value as the common characteristic of the customer's consumption ability for the first building material product and the second building material product; If not, it is determined that the customer does not have the common consumption capacity characteristics for the first building material product and the second building material product.

6. The product recommendation method based on data pre-screening and introduction of intermediate variables according to claim 5 is characterized in that: In step B1, the method for assigning the price range classification level value corresponding to each brand under the same brand classification level includes the following steps: B11, determine whether there is any overlapping price range between brands in the same brand classification level. If so, the impact of lowering and / or raising prices on the price range classification value is calculated for each brand with overlapping product price ranges, and then the process goes to step B12; If not, assign the price range classification level values ​​from high to low to the selling price ranges corresponding to the brands under the same brand classification level according to the highest price in the product price range; B12, assigning the same price intersection interval classification level value to the price intersection intervals in the selling price ranges of the two brands for solving the influence amount, and then adding the influence amount to the price intersection interval classification level value to form the price interval classification level value corresponding to the brand with the price reduction and / or price increase.

7. The product recommendation method based on data pre-screening and introduction of intermediate variables according to claim 1 or 6, characterized in that: The influence amount in step B11 is calculated by the following method steps: B111, segmenting the first selling price range and the second selling price range corresponding to the first brand and the second brand at the same brand classification level participating in the influence calculation, respectively, with a fixed step size, to obtain a plurality of first price sub-ranges associated with the first selling price range and a plurality of second price sub-ranges associated with the second selling price range; B112: Count the number of segments with the same price sub-interval as a reference number for solving the influence amount, and count the number of first segments in which the price in each of the first price sub-intervals and / or each of the second price sub-intervals is less than the minimum price in each of the same price sub-intervals, and / or count the number of second segments in which the price is greater than the maximum price in each of the same price sub-intervals; B113, calculating a first ratio and a second ratio of the first segment number and / or the second segment number to the reference number respectively; B114, calculate the first product of the first ratio and the price intersection interval classification level value as a negative downward influence amount, and / or calculate the second product of the second ratio and the price intersection interval classification level value as a positive upward influence amount.

8. The product recommendation method based on data pre-screening and introduction of intermediate variables according to claim 2 is characterized in that: In step S31, the constructed influence relationship matrix including the maximum first variable value is expressed by the following expression (1): In expression (1), represents the influence relationship matrix; Indicates that the product to be recommended is Class characteristics eigenvalues; Indicates that a building material product in the third recommendation basis list that forms a product combination with the proposed recommended product is in the first Class characteristics eigenvalues, , Indicates the Class or The number of eigenvalues ​​under the class characteristics; , Indicates the number of characteristic types of the proposed recommended product or the building material product; 、 represents a first average value and a second average value respectively representing the common characteristics of brand pursuit and the common characteristics of consumption capacity used to solve the maximum first variable value; Positive impact factor Calculated by the following steps: S311, calculate the pair matrix calculating a first influence value of each element data pair generated from the proposed product in the normalized matrix after normalization processing on generating the first average value, and calculating a second influence value of each element data pair generated from the building material product that constitutes a product combination with the proposed product in the normalized matrix on generating the second average value; S312: Calculate the weighted average of the first influence value and the second influence value as the positive influence factor of the proposed product ; In step S311, the calculation method of the first influence value or the second influence value is expressed by the following formula (2): In formula (2), , represents the first impact value, represents the second impact value; ； express The normalized value of Representation matrix The Rank the eigenvalues ​​of the elements of the column; express In calculation The weight of time.

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