Building materials product recommendation method based on big data

Abstract

The present invention discloses a building material product recommendation method based on big data. By introducing intermediate variables and calculating positive and reverse influencing factors, a mutual recommendation influence relationship is established between a first building material product and a second building material product, and the influence of differences in product characteristics between different products on the accuracy of product recommendation is considered, so that the common purchase characteristics between the proposed recommended customers and the historical purchasing customers can be found in the subsequent process, and the maximum first variable value of the intermediate variable of the proposed recommended customer is used as a basis, and the target recommended products that can meet the brand pursuit and consumption capacity of the proposed recommended customers can be accurately screened out from a large number of proposed recommended products, 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 method for recommending building materials products based on big data. Background Art

[0002] At present, the common method for recommending products between different categories or different models of products in the same category is: assuming that customer U has purchased product A, a set of algorithms is used to extract the customer purchase features of customer U when purchasing product A, and then match product B with product features similar to product A, and finally customer U is used as the candidate for recommendation of product B. When product C needs to be recommended, similarly, the similarity of product features between product C and product A or product B is calculated, and product C with similarity is recommended to customers who have purchased product A or product B.

[0003] The above product recommendation method has the following problems:

[0004] Only the product similarity between the proposed product and the reference product (such as purchased product A or product B) is considered, while the difference in product similarity between the reference products or between the proposed product and each reference product on the accuracy of recommending the proposed product is not considered, resulting in low effectiveness of product recommendation. For example, when customer U has purchased product A and product B, that is, the number of reference products for the proposed product is at least "2", which product should be used as the final reference, and how should the degree of influence of the product feature difference between the proposed product and each reference product on the accurate recommendation of the proposed product be characterized? Especially in the field of building materials product recommendation, each building materials product has fewer feature dimensions and smaller feature differences. In this case, it becomes difficult to judge whether the user should be recommended as a proposed object for building materials product B based on the user's historical purchase behavior of building materials product A, and existing methods are difficult to achieve accurate recommendations. Summary of the invention

[0005] The present invention aims to improve the accuracy of product recommendations under the premise of considering the impact of product feature differences between reference products on the recommendation accuracy of the proposed recommended products, and provides a building material product recommendation method based on big data.

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

[0007] A method for recommending building materials products based on big data is provided, including the following steps:

[0008] L1, introduces the intermediate variable and calculates the historical positive impact factor f of the customer's purchase of the first building material product with specified functional requirements but not of the same type or of the same type but different models and the second building material product 历史 and historical reverse impact factor f′ 历史, so as to build a mutual recommendation historical influence relationship between the two building materials products and add it to the relationship library;

[0009] L2, using the intermediate variable features as the matching basis, matches n historical purchasing customers with similar customer features to the recommended customers from the historical purchasing customer database;

[0010] L3, extracting the maximum first variable value from at least one first variable value associated with the proposed recommended customer, and then taking the proposed recommended product as the first building material product or the second building material product to establish the mutual recommendation history influence relationship as a screening condition, further screening customers from the historical purchasing customers matched in step L2, and forming the screening results into a first recommendation basis list according to the matching similarity of step L2 from high to low;

[0011] L4, screening out the first building material products or the second building material products in the first recommendation basis list that have been purchased by each of the historical purchasing customers and have established the mutual recommendation history influence relationship with the proposed recommended product, and sorting the screened building material products into a second recommendation basis list according to the same sorting order of each of the historical purchasing customers in the first recommendation basis list;

[0012] L5, filtering out the building material products in the second recommendation basis list that the customer to be recommended has purchased in the past, and sorting the filtered building material products into a third recommendation basis list according to the sorting order in the second recommendation basis list;

[0013] L6, for each product in the third recommendation basis list, based on the maximum first variable value of the proposed recommended customer, sequentially calculate the positive influence factor f and the reverse influence factor f′ of the proposed recommended product, and further calculate the mutual recommendation influence relationship between the proposed recommended product and each building material product in the third recommendation basis list;

[0014] L7, solve the fifth deviation between the mutual recommendation influence relationship associated with the intended recommended customers and the mutual recommendation historical influence relationship associated with the historical purchasing customers calculated for the same product combination, and use the intended recommended customers who meet the condition that the fifth deviation is less than the preset fifth deviation threshold as the target recommended customers for recommending the intended recommended products.

[0015] Preferably, in step L1, the method for establishing the mutual recommendation history influence relationship between the first building material product and the second building material product comprises the steps of:

[0016] L11, analyzing and extracting the intermediate variable associated with the product combination consisting of the first building material product and the second building material product according to the historical purchase feature data of each customer's historical purchase of the first building material product and the second building material product;

[0017] L12, constructing an influence relationship matrix related to the product combination including the intermediate variables and normalizing the element values, and then calculating the historical positive influence factor f of the first building material product 历史 and the historical reverse impact factor f′ of the second building material product 历史 ;

[0018] L13, calculate the historical positive impact factor f associated with the product portfolio 历史 and the historical reverse impact factor f′ 历史 The third deviation is then calculated, and the third average value of the third deviations solved for the same product combination by different customers with similar common purchasing characteristics for the product combination is used as the mutual recommendation history influence relationship constructed for the product combination.

[0019] Preferably, the intermediate variable is the common purchasing characteristics of the customer's historical purchases of the first building material product and the second building material product that constitute the product portfolio, and the common purchasing characteristics include the customer's common brand pursuit characteristics of the first building material product and the second building material product, and / or common consumption capacity characteristics.

[0020] Preferably, the common characteristics pursued by the brands are analyzed and extracted through the following method steps:

[0021] A1, classifying the first building material product and the second building material product by brand, and then assigning the same brand classification grade value to each brand under each category;

[0022] A2, calculating a first proportion of the first brand classification grade value of the first building material product of the corresponding brand that the customer has purchased in the past among the brand classification grade values ​​corresponding to each brand of the first building material product, and calculating a second proportion of the second brand classification grade value of the second building material product of the corresponding brand that the same customer has purchased in the past among the brand classification grade values ​​corresponding to each brand of the second building material product;

[0023] A3, determining whether a first deviation between the first proportion and the second proportion is less than a preset first deviation threshold,

[0024] If yes, then calculating a first average value of the first brand classification grade value and the second brand classification grade value as the common characteristics of the brand pursuit of the first building material product and the second building material product by the customer;

[0025] 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.

[0026] Preferably, the common characteristics of consumption capacity are analyzed and extracted by the following method steps:

[0027] B1, classifying the selling price ranges of each brand under each brand grade classification of the first building material product and the second building material product, and assigning corresponding price range classification grade values ​​to each brand under the same brand grade classification;

[0028] B2, calculating a first ratio of a first price range classification level value corresponding to the selling price range into which the purchase price of the first building material product of the corresponding brand purchased by the customer in history falls, to the price range classification level values ​​respectively corresponding to each brand under 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 into which the purchase price of the second building material product of the corresponding brand purchased by the same customer in history falls, to the price range classification level values ​​respectively corresponding to each brand under the same brand classification level of the second building material product;

[0029] B3, determining whether a second deviation between the first ratio and the second ratio is less than a preset second deviation threshold,

[0030] If so, calculating a second average value of the first price range classification grade value and the second price range classification grade value as the common characteristics of the consumption capacity of the customer for the first building material product and the second building material product;

[0031] 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.

[0032] 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:

[0033] B11, determine whether there is any overlapping price range between brands in the same brand classification level.

[0034] If so, the influence of lowering and / or raising prices on the price range classification level value is calculated for each brand with overlapping product price ranges, and then the process goes to step B12;

[0035] If not, then 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 from high to low;

[0036] 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.

[0037] Preferably, the influence amount in step B11 is calculated by the following method steps:

[0038] 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 calculation of the influence amount respectively with a fixed step length 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;

[0039] B112, counting the number of segments with the same price sub-interval as a reference number for solving the influence amount, and counting 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 counting the number of second segments in which the price is greater than the maximum price in each of the same price sub-intervals;

[0040] 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;

[0041] 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.

[0042] Preferably, in step L12, the constructed influence relationship matrix including the intermediate variables is expressed by the following expression (1):

[0043]

[0044] In expression (1), M represents the influence relationship matrix;

[0045] represents the jth characteristic value of the first building material product under the i-th type of characteristics; represents the jth characteristic value of the second building material product under the i′th type of characteristics, j=1,2,…,n, n represents the number of characteristic values ​​under the i′th type of characteristics; i or i′=1,2,…,I, I represents the number of characteristic types of the first building material product or the second building material product; v1 and v2 represent the first average value and the second average value respectively representing the common characteristics of the brand pursuit and the common characteristics of the consumption ability;

[0046] f 历史 Calculated by the following steps:

[0047] L121, calculating a first influence value of each element data pair generated from the first building material product in a normalized matrix after normalizing the matrix M to generate the first average value, and calculating a second influence value of each element data pair generated from the second building material product in the normalized matrix to generate the second average value;

[0048] L122, calculating the weighted average of the first impact value and the second impact value as the historical positive impact factor f of the first building material product 历史 ;

[0049] In step L121, the calculation method of the first impact value or the second impact value is expressed by the following formula (2):

[0050]

[0051] In formula (2), x=1, 2, c1 represents the first influence value, and c2 represents the second influence value;

[0052] y=1,2;

[0053] v y ′ Indicates v y The normalized value of

[0054] b re Represents the eigenvalue of the element in the rth row and eth column of the matrix M;

[0055] w re Indicates b re In calculating c x The weight of time;

[0056] The values ​​of x and y may be the same or different.

[0057] Preferably, in step L2, the method of matching the first n historical purchase customers having customer feature similarity with the customer to be recommended by taking the intermediate variable feature as the matching basis is:

[0058] Obtain the first variable value of the intermediate variable between each pair of building material products that the customer to be recommended has purchased in the past, and then match the historical purchasing customers corresponding to the second variable value of the intermediate variable whose fourth deviation from the first variable value is less than the fourth deviation threshold from the historical purchasing customer database, and sort the matched historical purchasing customers from small to large according to the fourth deviation, and then extract the top n historical purchasing customers in the sorting.

[0059] Preferably, when different customers purchase the first building material product in the product combination at the same brand classification level, and purchase the second building material product at the same brand classification level, and the deviations between the first average values ​​serving as the common characteristics of brand pursuit for the product combination are less than a preset first common deviation threshold, and the deviations between the second average values ​​serving as the common characteristics of consumption capacity for the product combination are less than a preset second common deviation threshold, it is determined that the customers have similar common purchasing characteristics for the product combination.

[0060] The present invention has the following beneficial effects:

[0061] 1. 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. This makes it possible to subsequently find the common purchasing characteristics of the proposed recommended customers and historical purchasing customers, and based on the maximum first variable value of the intermediate variable of the proposed recommended customer, accurately screen out the target recommended products that can meet the brand pursuit and consumption capacity of the proposed recommended customers from a large number of proposed recommended products, thereby greatly improving the accuracy of building material product recommendations.

[0062] 2. By identifying and extracting the common purchase features of the same customer for the first building material product and the second building material product, and introducing the common purchase 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.

[0063] 3. When calculating the impact value c as the basis for calculating the historical positive impact factor or the historical negative impact factor x When the normalized element value b re and the normalized common purchase feature v′ y 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 through b re The corresponding weight w re 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 materials product or the second building materials product.

[0064] 4. 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) is 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) is used as the basis for calculating the second impact value. The historical positive impact factor is obtained by weighted summing the first impact value and the second impact value, which characterizes 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 is used as the basis for calculating the first impact value, and the common characteristics of consumption capacity of the first building material product is used as the basis for calculating the second impact value. The historical reverse impact factor is obtained by weighted summing the first impact value and the second impact value, which characterizes 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 accurately recommend the proposed products to the proposed customers in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order 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, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0066] Figure 1 This is a diagram of implementation steps of a method for recommending building materials products based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0068] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0069] 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", etc. appear to indicate the orientation or position relationship, it is based on the orientation or position relationship shown in the drawings, which is 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 orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as a limitation on the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0070] In the description of the present invention, unless otherwise clearly specified and limited, if the term "connection" or the like appears to indicate the connection relationship between components, the 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 the internal connection of two components or the interaction relationship between two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0071] The method for recommending building materials products based on big data provided by the embodiment of the present invention is as follows: Figure 1 As shown, the steps include:

[0072] L1, introduces the intermediate variable and calculates the historical positive impact factor f of the customer's purchase of the first building material product with specified functional requirements but not of the same type or of the same type but different models and the second building material product 历史 and historical reverse impact factor f′ 历史 , so as to build a mutual recommendation historical influence relationship between the two building materials products and add it to the relationship library;

[0073] Here, the first building material product and the second building material product with specified functional requirements but different types or the same type but different models are explained. For example, the first building material product is floor heating floor, and the second building material product is central air conditioning. The customer has purchased floor heating floor in the past, which means that the customer's specified functional requirements for the floor include the floor having a heating function, that is, the customer has a specified functional requirement for the floor to have heating. Suppose that the customer has also purchased central air conditioning in the past. Central air conditioning usually has the function of at least one outdoor unit to drive two indoor units, which means that the customer's specified functional requirements for the air conditioning include the specified functional requirement of "at least one outdoor unit to drive two indoor units". Floor heating floor and central air conditioning are two different building material categories, and therefore they are the first building material product and the second building material product with specified functional requirements but different types. Similarly, the definition method for products with specified functional requirements but different types is the same as the definition method for products with specified functional requirements but different types, and will not be repeated.

[0074] The method for establishing a mutual recommendation history influence relationship between a first building material product and a second building material product comprises the following steps:

[0075] L11, analyzing and extracting intermediate variables related to the product combination consisting of the first building material product and the second building material product according to the historical purchase feature data of each customer's historical purchase of the first building material product and the second building material product;

[0076] For example, the first building material product and the second building material product are the above-mentioned floor heating floor and central air conditioner, respectively, and the product combination is "floor heating floor - central air conditioner". In step L11, the historical purchase feature data of each customer obtained is: the historical purchase feature data of each customer's historical purchase of floor heating floor and central air conditioner in the product combination.

[0077] The intermediate variables associated with the product portfolio are: the common purchasing characteristics of the first building material product and the second building material product that constitute the product portfolio that the customer has purchased in history. The common purchasing characteristics include the common characteristics of the customer's pursuit of the brand of the first building material product, such as floor heating, and the second building material product, such as central air conditioning, and / or the common characteristics of consumption capacity.

[0078] The common characteristics of brands that are common purchasing characteristics are analyzed and extracted through the following method steps:

[0079] A1, classify the first building material product and the second building material product by brand, and then assign the same brand classification grade value to each brand under each category;

[0080] For example, the brands of floor heating flooring as the 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, and brands BP3 and BP4 are assigned another same brand classification level value. Brand classification level values ​​at different classification levels are assigned accordingly from high to low according to the classification level, that is, 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.

[0081] A2, calculating a first proportion of the first brand classification grade value of the first building material product of the corresponding brand purchased by the customer in the brand classification grade values ​​corresponding to each brand of the first building material product, and calculating a second proportion of the second brand classification grade value of the second building material product of the corresponding brand purchased by the same customer in the brand classification grade values ​​corresponding to each brand of the second building material product;

[0082] For example, the brand of floor heating floor that the customer has purchased in the past is BP1. 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". 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.

[0083] A3, determining whether a first deviation between the first proportion and the second proportion is less than a preset first deviation threshold,

[0084] If so, a first average value of the first brand classification grade value and the second brand classification grade value is calculated as the common characteristics of the brand pursuit of the first building material product and the second building material product by the customer;

[0085] If not, it is determined that the customer does not have common characteristics in brand pursuit for the first building material product and the second building material product.

[0086] 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 level value and the second brand classification level value (defined as the first average value) is calculated as the common characteristics of customers' brand pursuit of the first building material product and the second building material product.

[0087] 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 of brand pursuit between 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 protection required by 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.

[0088] The common characteristics of consumption ability as common purchasing characteristics are analyzed and extracted through the following method steps:

[0089] B1, classify the selling price ranges of each brand under each brand level classification of the first building material product and the second building material product, and assign corresponding price range classification level values ​​to each brand under the same brand level classification;

[0090] 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 of 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.

[0091] The method for assigning the price range classification level values ​​corresponding to each brand under the same brand classification level comprises the following steps:

[0092] B11, determine whether there is any overlapping price range between brands in the same brand classification level.

[0093] If yes, then the influence of lowering the price and / or raising the price on the price range classification level value is solved for each brand with overlapping product price ranges, and then the process goes to step B12;

[0094] If not, then assign the price range classification level 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;

[0095] 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.

[0096] When the "if" condition of step B11 is met, the influence amount in step B11 is calculated by the following method steps:

[0097] B111, segmenting the first selling price interval and the second selling price interval corresponding to the first brand and the second brand at the same brand classification level participating in the calculation of the influence amount respectively with a fixed step length, and obtaining a plurality of first price sub-intervals associated with the first selling price interval and a plurality of second price sub-intervals associated with the second selling price;

[0098] For example, the first selling price range (200-400) and the second selling price range (250-450) corresponding to the above-mentioned brand BP1 (first brand) and brand BP2 (second brand) are segmented with a fixed step size of "50" to obtain several first price sub-ranges associated with the first selling price range, which are: 200-250, 250-300, 300-350, 350-400, and several second price sub-ranges associated with the second selling price range, which are: 250-300, 300-350, 350-400, 400-450, and then proceed to step B112.

[0099] B112, counting the number of segments with the same price sub-interval as the reference number for solving the influence amount, and counting 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 counting the number of second segments in which the price is greater than the maximum price in each same price sub-interval;

[0100] Continuing with the above example, the same price sub-intervals of 200-250, 250-300, 300-350, 350-400 and 250-300, 300-350, 350-400, 400-450 are 250-300, 300-350, 350-400, and the number of segments with the same price sub-interval is "3", which is used as the reference number for solving the influence quantity. The minimum price in these three same sub-intervals is 250 and the maximum price is 400. The first price sub-interval of 200-250 satisfies the condition that the price is less than the minimum price in each same sub-interval, and the first segment number is "1". According to the same statistical method, the second segment number is also "1", and then the process goes to step B113.

[0101] 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;

[0102] Continuing with the above example, the first ratio and the second ratio are both one-third.

[0103] 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.

[0104] 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.

[0105] The method of assigning price intersection interval classification level values ​​is not within the scope of the rights protection claimed in this application, and has no direct impact on the technical problem solved by the present invention, so it will not be explained in detail.

[0106] When the "if no" condition in step B11 is met, the method of assigning the price range classification level value is given as follows:

[0107] 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 highest price "600" has a larger price range classification level value than "400".

[0108] After the determination in step B11 is completed, the method of assigning the price range classification level values ​​corresponding to each brand under the same brand classification level is transferred to step:

[0109] B12, assigning the same price intersection interval classification level value to the price intersection interval in the selling price interval of the two brands whose influence amount is solved in step B11, 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 lowering price and / or raising price. It should be emphasized here that the influence amount corresponding to lowering price is negative, and the influence amount corresponding to raising price is positive.

[0110] After completing the assignment of the price range classification level values ​​corresponding to each brand under the same brand classification level through steps B11-B12, the common characteristics of consumption capacity are analyzed and extracted and transferred to the following steps:

[0111] B2, calculating a first ratio of a first price range classification level value corresponding to a selling price range in which a purchase price of a first building material product of a corresponding brand purchased by a customer falls among 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 a selling price range in which a purchase price of a second building material product of a corresponding brand purchased by the same customer falls among price range classification level values ​​corresponding to each brand at the same brand classification level of the second building material product;

[0112] For example, the first building material product purchased by customer U in the past was floor heating floor of brand BP1, and the purchase price was 230 yuan / square meter. The floor heating floor 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.

[0113] Assume that brands with the same brand classification level as BP1 include BP2 and BP3. Under BP1, BP2 and BP3, there are 8 price range classification level values, namely v 11 -v 18 , Assume that the price range of 150-250 corresponds to the price range classification level value v 13 , 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.

[0114] 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,

[0115] If so, then the average value of the first price range classification grade value and the second price range classification grade value (defined as the second average value) is calculated as the common characteristic of the customer's consumption capacity for the first building material product and the second building material product;

[0116] 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.

[0117] It should be noted here that, similar to the setting principle of the first deviation threshold, the specific setting of the second deviation threshold is related to the types of the first building material product and the second building material product, the number of brands under the same brand classification level, the number of selling price ranges of each brand, the width of the selling price range, whether there are overlapping ranges between the selling price ranges, etc. By considering these factors and adjusting the second deviation threshold, the degree of tolerance of the difference in the consumption capacity characteristics of the customer when purchasing the first building material product and the second building material product is characterized as "commonality". The setting of the second deviation threshold and the first deviation threshold is a complicated process. Since the specific setting is not within the scope of the rights protection required by this application, no specific explanation is given.

[0118] After the intermediate variables associated with the product combination consisting of the first building material product and the second building material product are analyzed and extracted in step L11 by the above method, the method of constructing a mutual recommendation history influence relationship between the first building material product and the second building material product proceeds to step:

[0119] L12, construct the influence relationship matrix of the product combination consisting of the first building material product and the second building material product, including the correlation of the intermediate variables, and perform element value normalization, and then calculate the historical positive impact factor f of the first building material product 历史 and the historical reverse impact factor f′ of the second building material product 历史 ;

[0120] The constructed influence relationship matrix containing intermediate variables is expressed by the following expression:

[0121]

[0122] In expression (1), M represents the influence relationship matrix;

[0123] represents the jth characteristic value of the first building material product under the i-th type of characteristics; represents the j-th characteristic value of the second building material product under the i′-th type of characteristics, j=1,2,…,n, n represents the number of characteristic values ​​under the i-th or i′-th type of characteristics; i or i′=1,2,…,I, I represents the number of characteristic types of the first building material product or the second building material product; v1 and v2 represent the first average value and the second average value respectively representing the common characteristics of brand pursuit and the common characteristics of consumption ability.

[0124] For example, the characteristics of the floor heating floor as the first building material product include comfort characteristics, environmental protection characteristics, energy-saving characteristics, price characteristics, etc., among which the comfort characteristics include thermal conductivity characteristics, heat resistance characteristics, constant temperature radiation characteristics, thermal stability characteristics, noise reduction and sound insulation characteristics, floor thickness characteristics, etc. The characteristics of the second building material product and the specific characteristics under each characteristic category will not be given as examples.

[0125] The historical positive impact factor f of the first building materials product 历史 Calculated by the following steps:

[0126] L121, calculate each element data generated from the first building material product in the normalized matrix after normalizing the matrix M (i.e. ) on generating a first average value (brand pursuit of common characteristics), and calculating a second influence value of each element data generated from the second building material product in the normalized matrix on generating a second average value;

[0127] The method of normalizing the matrix M is expressed by the following expression (3):

[0128]

[0129] I′ re It represents the normalized value after normalizing the element value in the rth row and the eth column in the matrix M;

[0130] I re Represents the element value of the rth row and eth column in the matrix M;

[0131] N represents the number of columns in the matrix M;

[0132] P represents the number of rows in the matrix M.

[0133] The calculation method of the first influence value or the second influence value is expressed by the following formula (2):

[0134]

[0135] In formula (2), x = 1, 2, c1 represents the first influence value, and c2 represents the second influence value;

[0136] y=1,2;

[0137] v y ′ Indicates v y The normalized value of

[0138] b re Represents the eigenvalue of the element in the rth row and eth column of the matrix M;

[0139] w re Indicates b re In calculating c x The weight of time.

[0140] The values ​​of x and y are the same or different, preferably the same, in order to simplify the calculation process of the positive or negative impact factor. reIt is related to the importance of the feature type and each feature under the feature type in calculating the common purchase feature, and it is related to the product similarity between the first building material product and the second building material product. re The method of granting is not within the scope of the rights protected by this application, so it will not be explained in detail.

[0141] L122, calculate the weighted average of the first impact value and the second impact value (i.e., the weighted average of c1 and c2) as the historical positive impact factor f of the first building material product 历史 ;

[0142] It should be noted here that f′ 历史 The calculation method of f 历史 The calculation principle of is similar, the difference is that the calculation of f′ 历史 When the weighted average value 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 ​​f′ 历史 In addition, when calculating the reverse impact factor, it is optional that x≠y.

[0143] Complete the historical positive impact factor f on the first building materials products 历史 and the historical reverse impact factor f′ of the second building material product 历史 After the calculation of , in step L1, the method for establishing a mutual recommendation history influence relationship between the first building material product and the second building material product proceeds to step:

[0144] L13, calculate the historical positive impact factor f associated with the product portfolio consisting of the first building material product and the second building material product 历史 and historical reverse impact factor f′ 历史 The third deviation (f 历史 and f′ 历史 The absolute value of the difference is then calculated, and then the average value of each third deviation solved for the product combination by different customers with similar common purchasing characteristics for the product combination (defined as the third average value) is calculated as the mutual recommendation historical influence relationship constructed for the product combination. It should 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 purchased first building material product is the same, the brand classification level of the purchased second building material product 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 the 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 the preset second common deviation threshold.

[0145] After completing the construction of the mutual recommendation history influence relationship of the product combination consisting of the first building material product and the second building material product through step L1, as shown in FIG. Figure 1 As shown, the building material product recommendation method provided in this embodiment enters the steps of:

[0146] L2, using the intermediate variable features as the matching basis, matches n historical purchase customers with similar customer features to the recommended customers from the historical purchase customer database. The matching method is:

[0147] Get the first variable value of the intermediate variable between the building materials products that the recommended customer has purchased in the past. For example, the crawled recommended customers include cus1, cus2, cus3, cus4, and cus5. Assuming that cus1 has purchased floor heating floors and range hoods in the past, calculate the first variable value of the intermediate variable of its historical purchase of floor heating floors and range hoods. The calculation method is to use the floor heating floor as the first building material product and the range hood as the second building material product through the above steps A1-A3 and steps B1-B3 to calculate the first variable value of the intermediate variable of the recommended customer cus1 (including the first average value representing the common characteristics of the brand pursuit of floor heating floors and range hoods by customer cus1 and the second average value of the common characteristics of consumption ability). It should be noted here that in order to facilitate the matching process of customer feature similarity in step L2, the first variable value of the intermediate variable is preferably a weighted average of the first average value and the second average value.

[0148] Then, historical purchasing customers corresponding to the second variable value of the intermediate variable whose deviation from the first variable value (defined as the fourth deviation) is less than the fourth deviation threshold are matched from the historical purchasing customer database, and the matched historical purchasing customers are sorted from small to large according to the fourth deviation, and then the top n historical purchasing customers are extracted.

[0149] For example, the customer cus1 to be recommended has only purchased floor heating floors and range hoods in the past, and the first variable value of the intermediate variable calculated for it is assumed to be c1. It is also assumed that there are 100 historical purchasing customers in the historical purchasing customer database, which generate a total of 200 intermediate variables, that is, the 100 historical purchasing customers in the database generate a total of 200 second variable values ​​of the intermediate variables. The fourth deviation is calculated between c1 and these 200 second variable values, and the historical purchasing customers corresponding to the second variable values ​​that are less than the fourth deviation threshold are sorted from small to large according to the fourth deviation. Finally, the top n historical purchasing customers are extracted. The fourth deviation is the absolute value of the difference between the first variable value and the second variable value.

[0150] After step L2, several historical purchase customers with similar common purchase characteristics to the customer to be recommended are found. However, at this time, it is still unclear what product is most suitable to recommend to the customer to be recommended. This application solves this problem through the following steps L3-L7.

[0151] L3, extracting the maximum first variable value from at least one first variable value associated with the proposed recommended customer, and then using the proposed recommended product as the first building material product or the second building material product to establish a recommendation history influence relationship between them as a screening condition, further screening customers from the historical purchasing customers matched in step L2, and forming the screening results into a first recommendation basis list according to the matching similarity of step L2 from high to low;

[0152] For example, assuming that the first variable values ​​of the intermediate variables of the proposed recommended customer cus1 in the above example include c1 and c2, and c1>c2, then c1 is taken as the maximum first variable value. Assuming that the proposed recommended product is product a, the first building material products and second building material products that have been purchased by each historical purchasing customer in step L2 include product a, product b, product c, product d, product e, product f, and product g. Among them, a mutual recommendation history influence relationship has been established for the products a and product b purchased by the historical purchasing customer cuu1, and a mutual recommendation history influence relationship has been established for the customer cuu2. If the customer cuu2 is not satisfied with the screening condition that the proposed recommended product a has established a mutual recommendation history influence relationship as the first building material product or the second building material product, the customer cuu2 is filtered out, and the customer cuu1 is retained.

[0153] L4, screening out the first building material products or second building material products that have been purchased by each historical purchasing customer in the first recommendation basis list and have established a recommendation history influence relationship with the proposed recommended product, and sorting the screened building material products into a second recommendation basis list according to the same sorting order of each historical purchasing customer in the first recommendation basis list;

[0154] For example, it is assumed that the first recommendation basis list includes three historical purchasing customers, cuu1, cuu3, and cuu4, among which customer cuu1 has historically purchased products a and product b, and a historical recommendation influence relationship has been established between products a and b; cuu3 has historically purchased products a and product c, and a historical recommendation influence relationship has been established between products a and c; customer cuu4 has historically purchased products a and product d, and a historical recommendation influence relationship has been established between products a and d, then in step L4, products b, c, and d are constructed into the second recommendation basis list, and the sorting order is still sorted from small to large according to the fourth deviation.

[0155] L5, filter out the building materials products in the second recommendation basis list that the customer to be recommended has purchased in the past, and sort the filtered building materials products into a third recommendation basis list according to the sorting order in the second recommendation basis list;

[0156] For example, assuming that the customer cus1 to be recommended has purchased product b and product c in the second recommendation basis list, then product b and product c are filtered out and the sorting order of the two products in the second recommendation basis list is retained, and then a third recommendation basis list is formed;

[0157] L6, for each product in the third recommendation basis list, according to the maximum first variable value of the proposed recommended customer, sequentially calculate the positive influence factor f and the reverse influence factor f′ of the proposed recommended product, and further calculate the mutual recommendation influence relationship between the proposed recommended product and each building material product in the third recommendation basis list;

[0158] From steps L11-L13, it can be seen that the historical positive impact factor f calculated in step L12 is 历史 and historical reverse impact factor f′ 历史 The value of is related to the value of the intermediate variable analyzed in step L11. In step L6, the maximum first variable value is the maximum variable value of the intermediate variable of the historical purchase of building materials products by the customer to be recommended, which includes the first average value expressing the common characteristics of brand pursuit and the second average value expressing the common characteristics of consumption capacity. In step L6, the first average value and the second average value associated with the maximum first variable value are used to construct an influence relationship matrix between the proposed product and the specified product in the third recommendation basis list, and then the positive influence factor f and the reverse influence factor f′ between the proposed product and the specified product are solved, and then the mutual recommendation influence relationship between the two products is calculated. The calculation method is the same as the mutual recommendation historical influence relationship, which will not be repeated.

[0159] L7, 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 associated proposed recommended customers calculated for the same product combination and the mutual recommendation historical influence relationship between the associated historical purchasing customers, and use the proposed recommended customers whose fifth deviation is less than the fifth deviation threshold as the target recommended customers for recommending the proposed recommended product.

[0160] For example, after going through step L6, for the same proposed recommended customer cus1, the first mutual recommendation influence relationship between product a and product b is calculated, and the second mutual recommendation influence relationship between product a and product c is calculated. Assume that historical purchasing customer cuu2 has purchased product a and product b, and the first mutual recommendation historical influence relationship is calculated for product a and product b purchased by customer cuu2; historical purchasing customer cuu3 has purchased product a and product c, and the second mutual recommendation historical influence relationship is calculated for product a and product c purchased by customer cuu3. Then calculate the fifth deviation between the first mutual recommendation influence relationship and the first mutual recommendation historical influence relationship; calculate the fifth deviation between the second mutual recommendation influence relationship and the second mutual recommendation historical influence relationship. As long as at least one of the two fifth deviations is less than the preset fifth deviation threshold, the proposed recommended customer cus1 will be used as the target recommended customer for recommending the proposed recommended product a.

[0161] Finally, it should be noted that the building materials product in this embodiment is only an explanatory example product. As long as the common purchasing features of customers between two products can be extracted, the product recommendation method provided in this application can be used to achieve accurate and efficient recommendations for products.

[0162] In summary, the present invention introduces intermediate variables and constructs a mutual recommendation influence relationship between the first building material product and the second building material product by calculating the positive and reverse influence factors, and takes into account the influence of product feature differences between different products on the accuracy of product recommendations, so that the common purchase features between the intended recommended customers and the historical purchasing customers can be found in the subsequent process, and the maximum first variable value of the intermediate variable of the intended recommended customer is used as the basis, so as to accurately screen out the target recommended products that can meet the brand pursuit and consumption capacity of the intended recommended customers from a large number of intended recommended products, thereby greatly improving the accuracy of building material product recommendations.

[0163] It should be noted that the above specific implementations are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art should understand that various modifications, equivalent substitutions, changes, etc. can be made to the present invention. However, as long as these changes do not deviate from the spirit of the present invention, they should be within the scope of protection of the present invention. In addition, some terms used in the specification and claims of this application are not restrictive, but are only for the convenience of description.

Claims

1. A building materials product recommendation method based on big data, characterized in that: Includes steps: L1, introduces the intermediate variable and calculates the historical positive impact factor of the customer's purchase of the first building material product with specified functional requirements but not of the same type or of the same type but different models and the second building material product and historical reverse impact factor , so as to build a mutual recommendation historical influence relationship between the two building materials products and add it to the relationship library; L2, based on the intermediate variable characteristics, matches the customers with similar customer characteristics to the recommended customers from the historical purchase customer database. Historical purchasing customers; L3, extracting the maximum first variable value from at least one first variable value associated with the proposed recommended customer, and then taking the proposed recommended product as the first building material product or the second building material product to establish the mutual recommendation history influence relationship as a screening condition, further screening customers from the historical purchasing customers matched in step L2, and forming the screening results into a first recommendation basis list according to the matching similarity of step L2 from high to low; L4, screening out the first building material products or the second building material products in the first recommendation basis list that have been purchased by each of the historical purchasing customers and have established the mutual recommendation history influence relationship with the proposed recommended product, and sorting the screened building material products into a second recommendation basis list according to the same sorting order of each of the historical purchasing customers in the first recommendation basis list; L5, filtering out the building material products in the second recommendation basis list that the customer to be recommended has purchased in the past, and sorting the filtered building material products into a third recommendation basis list according to the sorting order in the second recommendation basis list; L6, for each product in the third recommendation basis list, based on the maximum first variable value of the proposed recommended customer, sequentially calculate the positive impact factor of the proposed recommended product and reverse impact factor , and further calculate the mutual recommendation influence relationship between the proposed recommended product and each building material product in the third recommendation basis list; L7, solving the fifth deviation between the mutual recommendation influence relationship associated with the proposed recommended customers and the mutual recommendation historical influence relationship associated with the historical purchasing customers calculated for the same product combination, and taking the proposed recommended customers who meet the condition that the fifth deviation is less than the preset fifth deviation threshold as the target recommended customers for recommending the proposed recommended products; In step L1, the method for establishing the mutual recommendation history influence relationship between the first building material product and the second building material product comprises the following steps: L11, analyzing and extracting the intermediate variable associated with the product combination consisting of the first building material product and the second building material product according to the historical purchase feature data of each customer's historical purchase of the first building material product and the second building material product; L12, constructing an influence relationship matrix related to the product combination including the intermediate variables and normalizing the element values, and then calculating the historical positive influence factor of the first building material product and the historical reverse impact factor of the second building material product ; L13, calculate the historical positive impact factor associated with the product portfolio and the historical reverse impact factor The third deviation is then calculated, and the third average value of the third deviations solved for the same product combination by different customers with similar common purchasing characteristics for the product combination is used as the mutual recommendation history influence relationship constructed for the product combination.

2. The method for recommending building materials products based on big data according to claim 1, characterized in that: The intermediate variable is the common purchasing characteristics of the customer's historical purchases of the first building material product and the second building material product that constitute the product portfolio, and the common purchasing characteristics include the customer's common brand pursuit characteristics of the first building material product and the second building material product, and / or common consumption capacity characteristics.

3. The method for recommending building materials products based on big data according to claim 2, characterized in that: The common characteristics pursued by the brands are analyzed and extracted through the following method steps: A1, classifying the first building material product and the second building material product by brand, and then assigning the same brand classification grade value to each brand under each category; A2, calculating a first proportion of the first brand classification grade value of the first building material product of the corresponding brand that the customer has purchased in the past among the brand classification grade values ​​corresponding to each brand of the first building material product, and calculating a second proportion of the second brand classification grade value of the second building material product of the corresponding brand that the same customer has purchased in the past among the brand classification grade values ​​corresponding to each brand of the second building material product; A3, determining whether a first deviation between the first proportion and the second proportion is less than a preset first deviation threshold, If yes, then calculating a first average value of the first brand classification grade value and the second brand classification grade value as the common characteristics of the brand pursuit of the first building material product and the second building material product by the customer; 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.

4. The method for recommending building materials products based on big data according to claim 2, characterized in that: The common characteristics of the consumption capacity are analyzed and extracted through the following method steps: B1, classifying the selling price ranges of each brand under each brand grade classification of the first building material product and the second building material product, and assigning corresponding price range classification grade values ​​to each brand under the same brand grade classification; B2, calculating a first ratio of a first price range classification level value corresponding to the selling price range into which the purchase price of the first building material product of the corresponding brand purchased by the customer in history falls, to the price range classification level values ​​respectively corresponding to each brand under 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 into which the purchase price of the second building material product of the corresponding brand purchased by the same customer in history falls, to the price range classification level values ​​respectively corresponding to each brand under 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 value of the first price range classification grade value and the second price range classification grade value as the common characteristics of the consumption capacity of 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 consumption capacity characteristics for the first building material product and the second building material product.

5. The method for recommending building materials products based on big data according to claim 4, characterized in that: 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: B11, determine whether there is any overlapping price range between brands in the same brand classification level. If so, the influence of lowering and / or raising prices on the price range classification level value is calculated for each brand with overlapping product price ranges, and then the process goes to step B12; If not, then 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 from high to low; 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.

6. The method for recommending building materials products based on big data according to claim 5, 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 calculation of the influence amount respectively with a fixed step length 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, counting the number of segments with the same price sub-interval as a reference number for solving the influence amount, and counting 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 counting 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.

7. The method for recommending building materials products based on big data according to any one of claims 2 to 6, characterized in that: In step L12, the constructed influence relationship matrix including the intermediate variables is expressed by the following expression (1): In expression (1), represents the influence relationship matrix; Indicates that the first building material product is Class characteristics eigenvalues; Indicates that the second building material product is Class characteristics eigenvalues, , Indicates Class or The number of eigenvalues ​​under the class characteristics; , Indicates the number of characteristic types of the first building material product or the second building material product; , represents a first average value and a second average value respectively representing the common characteristics of the brand pursuit and the common characteristics of the consumption ability; Calculated by the following steps: L121, calculate the pair matrix calculating a first influence value of each element data pair generated from the first building material product in the normalized matrix after normalization processing to generate the first average value, and calculating a second influence value of each element data pair generated from the second building material product in the normalized matrix to generate the second average value; L122, calculating the weighted average of the first impact value and the second impact value as the historical positive impact factor of the first building material product ; In step L121, the calculation method of the first impact value or the second impact 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 Line the eigenvalues ​​of the elements of the column; express In calculation The weight of time; and The values ​​are the same or different.

8. The method for recommending building materials products based on big data according to claim 1, characterized in that: In step L2, the intermediate variable features are used as the matching basis to match the predecessors with customer features similar to the customer to be recommended. The method of purchasing the historical customers is: Obtain the first variable value of the intermediate variable between the building material products that the recommended customer has purchased in the past, then match the historical purchasing customers corresponding to the second variable value of the intermediate variable whose fourth deviation from the first variable value is less than the fourth deviation threshold from the historical purchasing customer database, and sort the matched historical purchasing customers from small to large according to the fourth deviation, and then extract the historical purchasing customers before sorting. The historical purchasing customers.

9. The method for recommending building materials products based on big data according to claim 1, characterized in that: When different customers purchase the first building material product in the product combination at the same brand classification level, and purchase the second building material product at the same brand classification level, and the deviations between the first average values, which are the common characteristics of brand pursuit for the product combination, are less than a preset first common deviation threshold, and the deviations between the second average values, which are the common characteristics of consumption capacity for the product combination, are less than a preset second common deviation threshold, it is determined that the customers have similar common purchasing characteristics for the product combination.

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