CN110533453A

CN110533453A - Based on the matched Products Show method, apparatus of user, computer equipment

Info

Publication number: CN110533453A
Application number: CN201910662549.XA
Authority: CN
Inventors: 刘嘉; 吴欢
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2019-07-22
Filing date: 2019-07-22
Publication date: 2019-12-03

Abstract

The invention discloses be based on the matched Products Show method, apparatus of user, computer equipment.Method includes: to be clustered to obtain customers' information to client included in client information table according to clustering rule, client's Matching Model is constructed according to customers' information and full connection hidden layer, client's Matching Model is trained according to training parameter, target customers corresponding with client to be recommended are obtained according to client's Matching Model after training, the product information in client information table is screened to obtain target product and be pushed according to target product screening rule and target customers.The present invention is based on prediction model technologies, it can be in the case where user buy our company's product, the user group to match with the user is obtained based on the matched mode of user, and it further obtains target product corresponding with the user group and recommends to the client, with more wide applicability, good technical effect is achieved in actual application.

Description

Based on the matched Products Show method, apparatus of user, computer equipment

Technical field

The present invention relates to field of computer technology, more particularly to it is a kind of based on the matched Products Show method, apparatus of user, Computer equipment and storage medium.

Background technique

Enterprise is to promote to itself product, the specifying information of product can be pushed to user, current use is more wide General technology are as follows: the client is calculated does not score product by the scored score data of product of score in predicting model and client Prediction scoring, and by prediction scoring never scoring product in selected section product push to user.However in the prior art Products Show method be only capable of recommending corresponding target product to user in the case where user has purchased portioned product, work as user Then without normal direction, it recommends target product in the case where purchase our company's product, therefore existing Products Show method is in reality There are limitations in use process, are unable to satisfy the actual demand of enterprise.Thus, existing technical method is produced to user There is use when recommending by limitation in product.

Summary of the invention

The embodiment of the invention provides one kind based on the matched Products Show method, apparatus of user, computer equipment and to deposit Storage media, it is intended to solve the problems, such as to have use in art methods when carrying out Products Show to user by limitation.

In a first aspect, the embodiment of the invention provides one kind to be based on the matched Products Show method of user comprising:

If receiving the client information table that administrator is inputted, according to preset clustering rule to institute in the client information table The client for including is clustered to obtain customers' information, wherein further includes what each client bought in the client information table Product information；

According to customers' information and preset full client of the connection hidden layer building comprising input node and output node With model；

Constructed client's Matching Model is trained according to preset training parameter and is matched with the client after being trained Model；

If receiving client to be recommended passes through the customer information to be recommended that user terminal is inputted, by the client to be recommended Client's Matching Model after information input training, and obtain in client's Matching Model the matching degree between the client to be recommended Highest customers are as target customers；

According to preset target product screening rule and the target customers to the product information in the client information table It is screened to obtain target product and push to the user terminal.

Second aspect, the embodiment of the invention provides one kind to be based on the matched Products Show device of user comprising:

Cluster cell, if the client information table inputted for receiving administrator, according to preset clustering rule to described Client included in client information table is clustered to obtain customers' information, wherein further includes in the client information table The product information of each client's purchase；

Client's Matching Model construction unit, for including defeated according to customers' information and preset full connection hidden layer building Client's Matching Model of ingress and output node；

Client's Matching Model training unit, for being instructed according to preset training parameter to constructed client's Matching Model Practice with client's Matching Model after being trained；

Target customers' matching unit, if passing through the visitor to be recommended that user terminal is inputted for receiving client to be recommended Family information by client's Matching Model after the customer information input training to be recommended, and obtains in client's Matching Model The highest customers of matching degree are as target customers between the client to be recommended；

Product information screening unit is used for according to preset target product screening rule and the target customers to the visitor Product information in the information table of family is screened to obtain target product and push to the user terminal.

The third aspect, the embodiment of the present invention provide a kind of computer equipment again comprising memory, processor and storage On the memory and the computer program that can run on the processor, the processor execute the computer program Based on the matched Products Show method of user described in the above-mentioned first aspect of Shi Shixian.

Fourth aspect, the embodiment of the invention also provides a kind of computer readable storage mediums, wherein the computer can It reads storage medium and is stored with computer program, it is above-mentioned that the computer program when being executed by a processor executes the processor Based on the matched Products Show method of user described in first aspect.

The embodiment of the invention provides one kind based on the matched Products Show method, apparatus of user, computer equipment and to deposit Storage media.Client included in client information table is clustered according to clustering rule to obtain customers' information, according to client Group's information and full connection hidden layer construct client's Matching Model, are trained according to training parameter to client's Matching Model, according to instruction Client's Matching Model after white silk obtains target customers corresponding with client to be recommended, according to target product screening rule and target Customers are screened to obtain target product and be pushed to the product information in client information table.It, can by the above method In the case where user does not buy our company's product, the user group to match with the user is obtained based on the matched mode of user, And further obtain target product corresponding with the user group and recommend to the client, there is more wide applicability, in reality Good technical effect is achieved in application process.

Detailed description of the invention

Technical solution in order to illustrate the embodiments of the present invention more clearly, below will be to needed in embodiment description Attached drawing is briefly described, it should be apparent that, drawings in the following description are some embodiments of the invention, general for this field For logical technical staff, without creative efforts, it is also possible to obtain other drawings based on these drawings.

Fig. 1 is the flow diagram provided in an embodiment of the present invention based on the matched Products Show method of user；

Fig. 2 is the application scenarios schematic diagram provided in an embodiment of the present invention based on the matched Products Show method of user；

Fig. 3 is the sub-process schematic diagram provided in an embodiment of the present invention based on the matched Products Show method of user；

Fig. 4 is another sub-process schematic diagram provided in an embodiment of the present invention based on the matched Products Show method of user；

Fig. 5 is another sub-process schematic diagram provided in an embodiment of the present invention based on the matched Products Show method of user；

Fig. 6 is another sub-process schematic diagram provided in an embodiment of the present invention based on the matched Products Show method of user；

Fig. 7 is another flow diagram provided in an embodiment of the present invention based on the matched Products Show method of user；

Fig. 8 is the schematic block diagram provided in an embodiment of the present invention based on the matched Products Show device of user；

Fig. 9 is the schematic block diagram of computer equipment provided in an embodiment of the present invention.

Specific embodiment

Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that described embodiments are some of the embodiments of the present invention, instead of all the embodiments.Based on this hair Embodiment in bright, every other implementation obtained by those of ordinary skill in the art without making creative efforts Example, shall fall within the protection scope of the present invention.

It should be appreciated that ought use in this specification and in the appended claims, term " includes " and "comprising" instruction Described feature, entirety, step, operation, the presence of element and/or component, but one or more of the other feature, whole is not precluded Body, step, operation, the presence or addition of element, component and/or its set.

It is also understood that mesh of the term used in this description of the invention merely for the sake of description specific embodiment And be not intended to limit the present invention.As description of the invention and it is used in the attached claims, unless on Other situations are hereafter clearly indicated, otherwise " one " of singular, "one" and "the" are intended to include plural form.

It will be further appreciated that the term "and/or" used in description of the invention and the appended claims is Refer to any combination and all possible combinations of one or more of associated item listed, and including these combinations.

Referring to Fig. 1, Fig. 1 is the process signal provided in an embodiment of the present invention based on the matched Products Show method of user Figure；Fig. 2 is the application scenarios schematic diagram provided in an embodiment of the present invention based on the matched Products Show method of user.It should be based on use The matched Products Show method in family is applied in management server 10, and this method passes through the application that is installed in management server 10 Software is executed, and user terminal 20 is by establishing the transmission being connected to the network to realize data information with management server 10.Pipe Reason server 10 is for executing based on the matched Products Show method of user so that target product is pushed to client to be recommended Enterprise terminal, user terminal 20 is the terminal device for sending data information to management server 10, such as desktop Brain, laptop, tablet computer or mobile phone etc..Only illustrated in Fig. 2 management server 10 and a user terminal 20 into Row information transmission, in practical applications, the management server 10 can also carry out information transmission with more user terminals 20.

As shown in Figure 1, the method comprising the steps of S110~S150.

If S110, the client information table that administrator is inputted is received, according to preset clustering rule to the customer information Client included in table is clustered to obtain customers' information.

If receiving the client information table that administrator is inputted, according to preset clustering rule to institute in the client information table The client for including is clustered to obtain customers' information, wherein in client information table comprising multiple clients customer information with And the product information of each client's purchase.Administrator is the user of management server, and clustering rule is for all Client carries out the Rule Information of clustering, after being clustered based on customer information to client can will similar client segmentation to one Customers' information comprising multiple customers can be obtained in customers after clustering to all clients.

For example, certain a client information table is as shown in table 1.

Customer name	Age	Occupation	Hobby	Product information
					Client 1	28	Lawyer	Running	Product 1, product 3
Client 2	36	Civil servant	Tourism	Product 2
					Client 3	22	Student	Swimming	Product 1
Client 4	31	Self-employed worker	Running	Product 3, product 4

Table 1

In one embodiment, as shown in figure 3, step S110 includes sub-step S111 and S112.

S111, according to the characteristic variable transformation rule in the clustering rule by visitor included in the client information table Family information is converted to characteristic variable.

Client included in the client information table is believed according to the characteristic variable transformation rule in the clustering rule Breath is converted to characteristic variable.Specifically, including the information such as customer name, age, occupation, hobby, feature in customer information Variable transformation rule is the Rule Information for the customer information of each client to be converted to characteristic variable, every in customer information One information can be converted to corresponding vector value by characteristic variable transformation rule and is indicated, then can be by each customer information Corresponding conversion is the feature vector of a multidimensional, that is to say characteristic variable.Each client correspondence possesses phase in client information table The customer information answered can then obtain the customer information of all clients in client information table, pass through characteristic variable transformation rule It obtains the customer information of all clients and is converted to corresponding characteristic variable, characteristic variable transformation rule is for believing client Breath is converted to the rule of characteristic variable, in customer information each single item information with a vector value in characteristic variable transformation rule It is corresponding.

For example, several information for including in the customer information of first place client in table 1 are respectively: name " client 1 ", age " 28 ", professional " lawyer ", hobby " running ", the vector value of this information of age is this in characteristic variable transformation rule The numerical value of information, it is 5 that this professional information, which be " lawyer " corresponding vector value, hobby this be " running " correspondence Vector value be " 12 ", then be converted to corresponding characteristic variable be D={ 28,5,12 }.

S112, according in the clustering rule clustering algorithm and the characteristic variable client is clustered to be wrapped Customers' information containing multiple customers.

According in the clustering rule clustering algorithm and the characteristic variable client is clustered to obtain comprising more Customers' information of a customers, the characteristic variable in customers' information comprising multiple customers and each customers' mass center Value presets final required monoid specifically, clustering algorithm is K-means clustering algorithm in K-means clustering algorithm Quantity k, specifically, the corresponding characteristic variable of each customer information is indicated using a point coordinate based on three-dimensional system of coordinate, All three-dimensional coordinate points are clustered according to the numerical value of k, obtain the three-dimensional coordinate of k customers and each customers' mass center Value, the D coordinates value of mass center that is to say the characteristic variable value of mass center.

For example, K=15 is arranged in K-means clustering algorithm, then 15 customers and each customers are finally obtained The characteristic variable value of mass center, mass center are the central point of the customers, and the characteristic variable value of mass center is to own in the customers The mean value of characteristic variable.

S120, the visitor comprising input node and output node is constructed according to customers' information and preset full connection hidden layer Family Matching Model.

According to customers' information and preset full client of the connection hidden layer building comprising input node and output node With model.Customers' information is by the obtained information comprising multiple customers of clustering algorithm, each customers Zhong Bao Customer information and the customers containing multiple clients correspond to the characteristic information of mass center.It include multiple inputs in client's Matching Model Node and multiple output nodes, wherein the corresponding input node of each of client characteristics variable dimension, each client The corresponding output node of group, each output node one output node value of corresponding output, output node value can indicate a certain Matching degree between customers corresponding to client and the output node.Due to passing through the spy of the obtained each client of transformation rule Number of dimensions included in sign variable is all the same, therefore can pass through the corresponding input for generating identical quantity of the dimension of characteristic variable Node passes through the corresponding output node for generating identical quantity of the quantity of customers in customers' information.Full connection hidden layer is use In the middle layer contacted input node and output node, complete connect includes several feature units in hidden layer, each Feature unit is associated with all input nodes and all output nodes, and feature unit can be used for reflecting every in customer information The quantity of one relationship between information and customers, feature unit can be set according to customers' information, preferred feature The quantity of unit may be set to the 1/3-1/2 of customers' quantity.

For example, the quantity of customers is 15 in customers' information, then the quantity of feature unit can be set as 7.

In one embodiment, as shown in figure 4, step S120 includes sub-step S121, S122, S123 and S124.

S121, the input node that client's Matching Model is constructed according to the dimension of client characteristics variable in customers' information.

According to the input node of dimension building client's Matching Model of client characteristics variable in customers' information, specifically, Each of client characteristics variable dimension corresponds to an input node, therefore can generate phase by the way that the dimension of characteristic variable is corresponding With the input node of quantity, the dimension and input node that characteristic variable is included are corresponded.

S122, the output node that client's Matching Model is constructed according to the quantity of customers in customers' information.

According to the output node of customers' information architecture client's Matching Model, specifically, each customers is one corresponding Output node, therefore the corresponding output node for generating identical quantity of quantity of customers in customers' information, customers can be passed through Customers and output node in information correspond.

S123, according to it is preset it is full connection hidden layer in feature unit and constructed input node, with input node value work The first formula group of input node to feature unit is constructed as output valve for input value, feature unit value.

According to the preset complete multiple feature units connected in hidden layer and constructed multiple input nodes, with input node value The first formula group of input node to feature unit is constructed as output valve as input value, feature unit value, wherein first is public Formula group includes formula of all input nodes to all feature units.Input node is in client's Matching Model for a certain The node that the characteristic variable of a client is inputted, the specific value of input node are input node value, that is to say the client The vector value of respective dimensions in characteristic variable, due to a dimension in each input node character pair variable, all inputs The i.e. corresponding client characteristics variable of nodal value, feature unit value are the calculated value of the feature unit in full connection hidden layer.

For example, the input node value of a certain input node is x₁, the feature unit value of a certain feature unit is y₁, then this is defeated The formula of ingress to this feature unit is y₁=a × x₁+b；Wherein, a and b is the parameter in the formula, the parameter value in formula For the number generated at random.

S124, according to it is preset it is full connection hidden layer in feature unit and constructed output node, with feature unit value work The second formula group for input value, output node value as output valve construction feature unit to output node.

According to the multiple feature units and constructed multiple output nodes in preset full connection hidden layer, with feature unit value The second formula group as input value, output node value as output valve construction feature unit to output node, wherein second is public Formula group includes formula of all feature units to all output nodes.Output node is in client's Matching Model for client The node that matching degree between respective client group is exported, the specific value of output node are output node value, output Nodal value can indicate that the matching degree between customers corresponding to a certain client and the output node, feature unit value are to connect entirely Connect the calculated value of the feature unit in hidden layer.

For example, the feature unit value of a certain feature unit is y₁, the output node value of a certain output node is z₁, then the spy The formula for levying unit to the output node is z₁=c × y₁+d；Wherein, c and d is the parameter in the formula, the parameter value in formula For the number generated at random.

S130, constructed client's Matching Model is trained according to preset training parameter with the client after being trained Matching Model.

Constructed client's Matching Model is trained according to preset training parameter and is matched with the client after being trained Model.For the accuracy for improving client's Matching Model, constructed client's Matching Model can be carried out by preset training parameter Training, can be obtained the client's Matching Model trained.Characteristic variable and each visitor in training parameter including multiple clients The matching degree at family and corresponding target customers, target customers are that matching degree is highest between the client in customers' information One customers.

In one embodiment, as shown in figure 5, step S130 includes sub-step S131, S132, S133 and S134.

S131, the characteristic variable for obtaining a client in preset training parameter, according to respective formula in the first formula group and The feature unit value of all feature units in full connection hidden layer is calculated in input node value.

The characteristic variable for obtaining a client in preset training parameter is saved according to respective formula in the first formula group and input The feature unit value of all feature units in full connection hidden layer is calculated in point value.Due to every dimension in the characteristic variable of client An input node is corresponded to, therefore using vector value included in characteristic variable as the input node of corresponding input node The characteristic variable of the user can be inputted client's Matching Model by value.Each feature unit is and institute in client's Matching Model There are input node and all output nodes to be associated, that is to say through respective formula and input node value in the first formula group i.e. The feature unit value of all feature units in full connection hidden layer can be calculated.The calculated result of respective formula in first formula group In a certain feature unit include multiple calculated values, then take the average value of multiple calculated values as the feature unit of this feature unit Value.

S132, the output section that corresponding output node is calculated according to respective formula and feature unit value in the second formula group Point value.

The output node value of corresponding output node is calculated according to respective formula and feature unit value in the second formula group, The customers as included in customers' information and output node correspond, and pass through respective formula and spy in the second formula group Output node value can be calculated in sign cell value, and output node value can indicate visitor corresponding to a certain client and the output node Matching degree between the group of family, that is to say the matching degree being calculated between existing customer and each customers.Since client matches The parameter of formula in model is also adjusted update without training, therefore the output node value being currently calculated is only capable of using It is trained in client's Matching Model, and can not be as final result to match client with corresponding customers.

S133, the matching degree for obtaining existing customer and corresponding target customers and target corresponding with the target customers are defeated Egress value is calculated according to matching degree and target output node value of the mean square error function to target customers to be worked as The square mean error amount of preceding client.

The matching degree and target corresponding with target customers output for obtaining existing customer and corresponding target customers save Point value, the matching degree of each client and corresponding target customers in training parameter, target customers be in customers' information with The highest customers of matching degree between the client, then can obtain the client for the matching degree of target customers, and obtain An output node value corresponding with the target customers is as target output node value in all output node values.According to square Error function calculates the matching degree and target output node value of target customers, to obtain the mean square error of existing customer Value, mean square error function (MSE function) is a kind of calculating letter for reflecting difference degree between estimator and the amount of being estimated Number can be calculated the matching degree of target customers and client's Matching Model in training parameter by mean square error function and calculate The obtained error amount between target output node value, the square mean error amount the big, shows client's target in client's training parameter The error between target output node value that the matching degree and client's Matching Model of customers are calculated is bigger, square mean error amount It is smaller, show client's target that the matching degree of target customers and client's Matching Model are calculated in client's training parameter Error between output node value is smaller.

S134, judged according to preset mean square error threshold value whether square mean error amount is less than mean square error threshold value, root It is judged that result and the parameter regulation coefficient are adjusted update to the parameter of formula in client's Matching Model.

Judged according to preset mean square error threshold value whether square mean error amount is less than mean square error threshold value, according to judgement As a result and the parameter regulation coefficient is adjusted update to the parameter of formula in client's Matching Model, specifically, parameter adjusts It include parameter adjustment direction and parameter adjustment magnitude in coefficient, parameter adjustment direction includes positive adjustment and negative sense adjustment, parameter Adjustment amplitude is the specific range value being adjusted, and carries out what primary adjustment updated to the parameter of formula in client's Matching Model If specific steps include: that judging result is square mean error amount no more than preset mean square error threshold value, according to parameter adjustment direction In positive adjustment and the range value in parameter adjustment magnitude update is adjusted to the parameter of formula in client's Matching Model；If Judging result is that square mean error amount is greater than preset mean square error threshold value, then according to the reversed adjustment and parameter in parameter adjustment direction Range value in adjustment amplitude is adjusted update to the parameter of formula in client's Matching Model.It completes in client's Matching Model After the parameter of formula carries out primary adjustment update, the characteristic variable of next client and the client couple in training parameter can be obtained It answers the matching degree of target customers to be iterated training to client's Matching Model, passes through the feature of clients multiple in training parameter Variable and each client correspond to after the matching degree of target customers is iterated training to client's Matching Model, can be obtained with Trained client's Matching Model.

For example, the range value in parameter adjustment magnitude is 2%, judging result is that square mean error amount is not more than preset mean square error Poor threshold value, then this adjustment need to carry out positive adjustment, the parameter original numerical value basis of this adjustment formula in client's Matching Model On multiplied by 1.02 obtain new parameter value.

If S140, the customer information to be recommended that client to be recommended is inputted by user terminal is received, by described wait push away Client's Matching Model after recommending customer information input training, and obtain in client's Matching Model between the client to be recommended The highest customers of matching degree are as target customers.

If receiving client to be recommended passes through the customer information to be recommended that user terminal is inputted, by the client to be recommended Client's Matching Model after information input training, and obtain in client's Matching Model the matching degree between the client to be recommended Highest customers are as target customers.Wherein, client to be recommended is the user of user terminal, customer information to be recommended In include client to be recommended characteristic variable.

In one embodiment, as shown in fig. 6, step S140 includes sub-step S141, S142 and S143.

S141, input node value of the characteristic variable of client to be recommended as respective formula in the first formula group, root are obtained The feature unit of all feature units in full connection hidden layer is calculated according to respective formula in the first formula group and input node value Value.

Input node value of the characteristic variable of client to be recommended as respective formula in the first formula group is obtained, according to first The feature unit value of all feature units in full connection hidden layer is calculated in respective formula and input node value in formula group.Due to Every dimension corresponds to an input node in the characteristic variable of client to be recommended, therefore by vector included in characteristic variable It is worth the input node value as corresponding input node, the characteristic variable of the user can be inputted client's Matching Model.Client It is associated, that is to say through the first public affairs with all input nodes and all output nodes with each feature unit in model The feature unit value of all feature units in full connection hidden layer can be calculated in respective formula and input node value in formula group.The A certain feature unit includes multiple calculated values in the calculated result of respective formula in one formula group, then takes being averaged for multiple calculated values It is worth the feature unit value as this feature unit.

S142, the output section that all output nodes are calculated according to respective formula and feature unit value in the second formula group Point value.

The output node value of all output nodes is calculated according to respective formula and feature unit value in the second formula group, It is updated due to having carried out adjustment into the parameter for crossing all formula in the second formula group in client's Matching Model after training, because The output node value of all output nodes can be calculated by all formula in the second formula group and feature unit value for this, defeated Egress value can indicate the matching degree between customers corresponding to a certain client and the output node, that is to say be calculated to Recommend the matching degree between client and each customers.

S143, the customers according to corresponding to the output node value acquisition highest output node value being calculated are as mesh Mark customers.

According to customers corresponding to the output node value acquisition highest output node value being calculated as target visitor Family group.Each output node corresponds to a customers in client's Matching Model, obtain the highest customers of output node value and Target customers corresponding with client to be recommended can be obtained.

S150, according to preset target product screening rule and the target customers to the product in the client information table Information is screened to obtain target product and push to the user terminal.

According to preset target product screening rule and the target customers to the product information in the client information table It is screened to obtain target product and push to the user terminal.Specifically, comprising each client in client information table The product information that customer information and each client are bought is produced comprising at least one in the corresponding product information of each client Product include multiple clients in target customers, can be in the Customer Acquisition client information table according to included in target customers Target product information corresponding with the target customers, and target product information is screened according to target product screening rule To obtain target product, and target product is pushed into user terminal to complete the recommendation for carrying out product to client to be recommended.

In one embodiment, as shown in fig. 7, step S150 includes sub-step S151 and S152.

S151, target product information corresponding with the target customers in the product information of client information table is obtained.

Obtain target product information corresponding with the target customers in the product information of client information table.Specifically, The product information that customer information and the client in client information table comprising each client are bought, then obtain target customer Group in each client corresponding product information in client information table, can be obtained in product information with the target customers couple The target product information answered.

S152, it is screened according to target product screening rule pair and the target product information to obtain target product simultaneously Push to the user terminal.

It is screened according to target product screening rule pair with the target product information to obtain target product and push To the user terminal.Specifically, the number occurred to each product in target product information is counted to obtain product Number statistical result, and obtained product number statistical result is screened according to target product screening rule, final User terminal is pushed to the target product recommended to client to be recommended, and by target product.

For example, the product quantity in settable target product recommendation rules is three, then product number statistical result is obtained First three product of middle quantity ranking is recommended as target product to client to be recommended.

Based in the matched Products Show method of user provided by the embodiment of the present invention, according to clustering rule to client Client included in information table is clustered to obtain customers' information, constructs client according to customers' information and full connection hidden layer Matching Model is trained client's Matching Model according to training parameter, according to after training client's Matching Model obtain with to Recommend the corresponding target customers of client, according to target product screening rule and target customers to the product in client information table Information is screened to obtain target product and be pushed.By the above method, the feelings of our company's product can not be bought in user Under condition, the user group to match with the user is obtained based on the matched mode of user, and is further obtained corresponding with the user group Target product recommend to the client, there is more wide applicability, achieve good technology in actual application Effect.

The embodiment of the present invention also provides one kind based on the matched Products Show device of user, should be based on the matched product of user Recommendation apparatus is for executing the aforementioned any embodiment based on the matched Products Show method of user.Specifically, referring to Fig. 8, Fig. 8 is the schematic block diagram provided in an embodiment of the present invention based on the matched Products Show device of user.It should be matched based on user Products Show device can be configured in management server 10.

As shown in figure 8, including cluster cell 110, client's Matching Model structure based on the matched Products Show device 100 of user Build unit 120, client's Matching Model training unit 130, target customers' matching unit 140 and product information screening unit 150.

Cluster cell 110, if the client information table inputted for receiving administrator, according to preset clustering rule to institute Client included in client information table is stated to be clustered to obtain customers' information.

In other inventive embodiments, the cluster cell 110 includes subelement: characteristic variable converting unit 111 and client Cluster cell 112.

Characteristic variable converting unit 111, for according to the characteristic variable transformation rule in the clustering rule by the visitor Customer information included in the information table of family is converted to characteristic variable.

Customer clustering unit 112, for according in the clustering rule clustering algorithm and the characteristic variable to client It is clustered to obtain customers' information comprising multiple customers.

Client's Matching Model construction unit 120, for according to customers' information and preset full connection hidden layer building packet Client's Matching Model containing input node and output node.

In other inventive embodiments, client's Matching Model construction unit 120 includes subelement: input node building is single Member 121, output node construction unit 122, the first formula group construction unit 123 and the second formula group construction unit 124.

Input node construction unit 121, for constructing client according to the dimension of client characteristics variable in customers' information Input node with model.

Output node construction unit 122, for constructing client's Matching Model according to the quantity of customers in customers' information Output node.

First formula group construction unit 123, for according to feature unit in preset full connection hidden layer and constructed defeated Ingress constructs input node to the of feature unit as input value, feature unit value as output valve using input node value One formula group.

Second formula group construction unit 124, for according to feature unit in preset full connection hidden layer and constructed defeated Egress, using feature unit value as input value, output node value as output valve construction feature unit to output node Two formula groups.

Client's Matching Model training unit 130, for according to preset training parameter to constructed client's Matching Model into Row training is with client's Matching Model after being trained.

In other inventive embodiments, client's Matching Model training unit 130 includes subelement: feature unit value is calculated Unit 131, output node value acquiring unit 132, square mean error amount computing unit 133 and parameter adjustment unit 134.

Feature unit value computing unit 131, for obtaining the characteristic variable of a client in preset training parameter, according to The feature unit value of all feature units in full connection hidden layer is calculated in respective formula and input node value in one formula group.

Output node value acquiring unit 132, for being calculated according to respective formula and feature unit value in the second formula group To the output node value of corresponding output node.

Square mean error amount computing unit 133, for obtain existing customer and the matching degree of corresponding target customers and with this The corresponding target output node value of target customers is exported according to matching degree and target of the mean square error function to target customers Nodal value is calculated to obtain the square mean error amount of existing customer.

Parameter adjustment unit 134, for whether being less than mean square error to square mean error amount according to preset mean square error threshold value Threshold value is judged, is adjusted according to judging result and the parameter regulation coefficient to the parameter of formula in client's Matching Model It updates.

Target customers' matching unit 140, if for receive client to be recommended by user terminal inputted wait push away Customer information is recommended, by client's Matching Model after the customer information input training to be recommended, and the client is obtained and matches mould In type between the client to be recommended the highest customers of matching degree as target customers.

In other inventive embodiments, target customers' matching unit 140 includes subelement: the first computing unit 141, Second computing unit 142 and target customers' acquiring unit 143.

First computing unit 141, for obtaining the characteristic variable of client to be recommended as respective formula in the first formula group Input node value, all features in full connection hidden layer are calculated according to respective formula and input node value in the first formula group The feature unit value of unit.

Second computing unit 142, it is all for being calculated according to respective formula and feature unit value in the second formula group The output node value of output node.

Target customers' acquiring unit 143, for obtaining highest output node according to the output node value being calculated The corresponding customers of value are as target customers.

Product information screening unit 150 is used for according to preset target product screening rule and the target customers to institute The product information in client information table is stated to be screened to obtain target product and push to the user terminal.

In other inventive embodiments, the product information screening unit 150 includes subelement: target product acquisition of information list Member 151 and target product acquiring unit 152.

Target product information acquisition unit 151, in the product information for obtaining client information table with the target customer The corresponding target product information of group.

Target product acquiring unit 152, for being carried out according to target product screening rule pair and the target product information Screening is to obtain target product and push to the user terminal.

It is matched using above-mentioned based on user based on the matched Products Show device of user provided by the embodiment of the present invention Products Show method, client included in client information table is clustered according to clustering rule to obtain customers' information, Client's Matching Model is constructed according to customers' information and full connection hidden layer, client's Matching Model is instructed according to training parameter Practice, target customers corresponding with client to be recommended are obtained according to client's Matching Model after training, are screened according to target product Rule and target customers are screened to obtain target product and be pushed to the product information in client information table.By upper Method is stated, can be obtained based on the matched mode of user and be matched with the user in the case where user does not buy our company's product User group, and further obtain corresponding with user group target product and recommend to the client, have more extensive applicable Property, good technical effect is achieved in actual application.

The above-mentioned form that can be implemented as computer program based on the matched Products Show device of user, the computer program It can be run in computer equipment as shown in Figure 9.

Referring to Fig. 9, Fig. 9 is the schematic block diagram of computer equipment provided in an embodiment of the present invention.

Refering to Fig. 9, which includes processor 502, memory and the net connected by system bus 501 Network interface 505, wherein memory may include non-volatile memory medium 503 and built-in storage 504.

The non-volatile memory medium 503 can storage program area 5031 and computer program 5032.The computer program 5032 are performed, and processor 502 may make to execute based on the matched Products Show method of user.

The processor 502 supports the operation of entire computer equipment 500 for providing calculating and control ability.

The built-in storage 504 provides environment for the operation of the computer program 5032 in non-volatile memory medium 503, should When computer program 5032 is executed by processor 502, processor 502 may make to execute based on the matched Products Show side of user Method.

The network interface 505 is for carrying out network communication, such as the transmission of offer data information.Those skilled in the art can To understand, structure shown in Fig. 9, only the block diagram of part-structure relevant to the present invention program, is not constituted to this hair The restriction for the computer equipment 500 that bright scheme is applied thereon, specific computer equipment 500 may include than as shown in the figure More or fewer components perhaps combine certain components or with different component layouts.

Wherein, the processor 502 is for running computer program 5032 stored in memory, to realize following function Can: if the client information table that administrator is inputted is received, according to preset clustering rule to included in the client information table Client clustered to obtain customers' information, wherein further include the product of each client purchase in the client information table Information；Mould is matched comprising the client of input node and output node according to customers' information and preset full connection hidden layer building Type；Constructed client's Matching Model is trained according to preset training parameter with client's Matching Model after being trained； If receiving client to be recommended passes through the customer information to be recommended that user terminal is inputted, the customer information to be recommended is inputted Client's Matching Model after training, and obtain in client's Matching Model the highest visitor of matching degree between the client to be recommended Family group is used as target customers；According to preset target product screening rule and the target customers in the client information table Product information screened to obtain target product and push to the user terminal.

In one embodiment, if processor 502 is executing the client information table for receiving administrator and being inputted, according to preset When clustering rule clusters client included in the client information table to obtain the step of customers' information, execute such as Lower operation: according to the characteristic variable transformation rule in the clustering rule by customer information included in the client information table Be converted to characteristic variable；According in the clustering rule clustering algorithm and the characteristic variable client is clustered to obtain Customers' information comprising multiple customers.

In one embodiment, processor 502 is being executed according to customers' information and preset full connection hidden layer building packet It when the step of client's Matching Model containing input node and output node, performs the following operations: according to client in customers' information The input node of dimension building client's Matching Model of characteristic variable；Client is constructed according to the quantity of customers in customers' information The output node of Matching Model；According to the preset full feature unit connected in hidden layer and constructed input node, to input section Point value constructs the first formula group of input node to feature unit as input value, feature unit value as output valve；According to pre- The feature unit and constructed output node in full connection hidden layer are set, using feature unit value as input value, output node value The second formula group as output valve construction feature unit to output node.

In one embodiment, processor 502 execute according to preset training parameter to constructed client's Matching Model into When row training is with the step of client's Matching Model after being trained, performs the following operations: obtaining in preset training parameter one All spies in full connection hidden layer are calculated according to respective formula and input node value in the first formula group in the characteristic variable of client Levy the feature unit value of unit；Corresponding output node is calculated according to respective formula and feature unit value in the second formula group Output node value；The matching degree and target corresponding with the target customers for obtaining existing customer and corresponding target customers export Nodal value is calculated current to obtain according to matching degree and target output node value of the mean square error function to target customers The square mean error amount of client；Sentenced according to preset mean square error threshold value to whether square mean error amount is less than mean square error threshold value It is disconnected, update is adjusted to the parameter of formula in client's Matching Model according to judging result and the parameter regulation coefficient.

In one embodiment, if processor 502 execute receive client to be recommended by user terminal inputted to Recommend customer information, by client's Matching Model after the customer information input training to be recommended, and obtains client's matching In model between the client to be recommended when step of the highest customers of matching degree as target customers, following behaviour is executed Make: input node value of the characteristic variable of client to be recommended as respective formula in the first formula group is obtained, according to the first formula The feature unit value of all feature units in full connection hidden layer is calculated in respective formula and input node value in group；According to second The output node value of all output nodes is calculated in respective formula and feature unit value in formula group；According to what is be calculated Output node value obtains customers corresponding to highest output node value as target customers.

In one embodiment, processor 502 is being executed according to preset target product screening rule and the target customers The step of product information in the client information table being screened to obtain target product and push to the user terminal When, it performs the following operations: obtaining target product information corresponding with the target customers in the product information of client information table； It is screened according to target product screening rule pair and the target product information to obtain target product and push to the use Family terminal.

It will be understood by those skilled in the art that the embodiment of computer equipment shown in Fig. 9 is not constituted to computer The restriction of equipment specific composition, in other embodiments, computer equipment may include components more more or fewer than diagram, or Person combines certain components or different component layouts.For example, in some embodiments, computer equipment can only include depositing Reservoir and processor, in such embodiments, the structure and function of memory and processor are consistent with embodiment illustrated in fig. 9, Details are not described herein.

It should be appreciated that in embodiments of the present invention, processor 502 can be central processing unit (Central Processing Unit, CPU), which can also be other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit (Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic Device, discrete gate or transistor logic, discrete hardware components etc..Wherein, general processor can be microprocessor or Person's processor is also possible to any conventional processor etc..

Computer readable storage medium is provided in another embodiment of the invention.The computer readable storage medium can be with For non-volatile computer readable storage medium.The computer-readable recording medium storage has computer program, wherein calculating If machine program performs the steps of when being executed by processor receives the client information table that administrator is inputted, according to preset poly- Rule-like clusters client included in the client information table to obtain customers' information, wherein client's letter It further include the product information of each client's purchase in breath table；Include according to customers' information and preset full connection hidden layer building Client's Matching Model of input node and output node；Constructed client's Matching Model is instructed according to preset training parameter Practice with client's Matching Model after being trained；If receiving client to be recommended passes through the client to be recommended that user terminal is inputted Information, by client's Matching Model after the customer information to be recommended input training, and obtain in client's Matching Model with The highest customers of matching degree are as target customers between the client to be recommended；According to preset target product screening rule and institute Target customers are stated to screen the product information in the client information table to obtain target product and push to the use Family terminal.

In one embodiment, if the client information table for receiving administrator and being inputted, according to preset clustering rule pair Client included in the client information table is clustered the step of to obtain customers' information, comprising: according to the cluster Customer information included in the client information table is converted to characteristic variable by the characteristic variable transformation rule in rule；According to Clustering algorithm and the characteristic variable in the clustering rule cluster client to obtain the visitor comprising multiple customers Family group's information.

In one embodiment, it is described according to customers' information and the building of preset full connection hidden layer comprising input node and The step of client's Matching Model of output node, comprising: client is constructed according to the dimension of client characteristics variable in customers' information The input node of Matching Model；According to the output node of quantity building client's Matching Model of customers in customers' information；Root According to the preset full feature unit connected in hidden layer and constructed input node, using input node value as input value, feature list Member value constructs the first formula group of input node to feature unit as output valve；According to the feature list in preset full connection hidden layer First and constructed output node, using feature unit value as input value, output node value as output valve construction feature unit To the second formula group of output node.

In one embodiment, described that constructed client's Matching Model is trained to obtain according to preset training parameter The step of client's Matching Model after training, comprising: the characteristic variable for obtaining a client in preset training parameter, according to first The feature unit value of all feature units in full connection hidden layer is calculated in respective formula and input node value in formula group；According to The output node value of corresponding output node is calculated in respective formula and feature unit value in second formula group；Obtain existing customer And the matching degree of corresponding target customers and target output node value corresponding with the target customers, according to mean square error function Matching degree and target output node value to target customers are calculated to obtain the square mean error amount of existing customer；According to pre- It sets mean square error threshold value and judges whether square mean error amount is less than mean square error threshold value, according to judging result and the parameter Regulation coefficient is adjusted update to the parameter of formula in client's Matching Model.

In one embodiment, if the client to be recommended letter for receiving client to be recommended and being inputted by user terminal Breath by client's Matching Model after the customer information input training to be recommended, and is obtained in client's Matching Model and is somebody's turn to do The step of highest customers of matching degree are as target customers between client to be recommended, comprising: obtain the spy of client to be recommended Input node value of the variable as respective formula in the first formula group is levied, according to respective formula and input node in the first formula group The feature unit value of all feature units in full connection hidden layer is calculated in value；According to respective formula and feature in the second formula group The output node value of all output nodes is calculated in cell value；Highest output is obtained according to the output node value being calculated Customers corresponding to nodal value are as target customers.

In one embodiment, described that the client is believed according to preset target product screening rule and the target customers Product information in breath table is screened the step of to obtain target product and push to the user terminal, comprising: obtains visitor Target product information corresponding with the target customers in the product information of family information table；According to target product screening rule pair It is screened with the target product information to obtain target product and push to the user terminal.

It is apparent to those skilled in the art that for convenience of description and succinctly, foregoing description is set The specific work process of standby, device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein. Those of ordinary skill in the art may be aware that unit described in conjunction with the examples disclosed in the embodiments of the present disclosure and algorithm Step can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware and software Interchangeability generally describes each exemplary composition and step according to function in the above description.These functions are studied carefully Unexpectedly the specific application and design constraint depending on technical solution are implemented in hardware or software.Professional technician Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed The scope of the present invention.

In several embodiments provided by the present invention, it should be understood that disclosed unit and method, it can be with It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit It divides, only logical function partition, there may be another division manner in actual implementation, can also will be with the same function Unit set is at a unit, such as multiple units or components can be combined or can be integrated into another system or some Feature can be ignored, or not execute.In addition, shown or discussed mutual coupling, direct-coupling or communication connection can Be through some interfaces, the indirect coupling or communication connection of device or unit, be also possible to electricity, mechanical or other shapes Formula connection.

The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple In network unit.Some or all of unit therein can be selected to realize the embodiment of the present invention according to the actual needs Purpose.

It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit It is that each unit physically exists alone, is also possible to two or more units and is integrated in one unit.It is above-mentioned integrated Unit both can take the form of hardware realization, can also realize in the form of software functional units.

If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product When, it can store in a computer readable storage medium.Based on this understanding, technical solution of the present invention substantially or Person says that all or part of the part that contributes to existing technology or the technical solution can body in the form of software products Reveal and, which is stored in a computer readable storage medium, including some instructions are used so that one Platform computer equipment (can be personal computer, server or the network equipment etc.) executes described in each embodiment of the present invention The all or part of the steps of method.And computer readable storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory The various media that can store program code such as (ROM, Read-Only Memory), magnetic or disk.

The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right It is required that protection scope subject to.

Claims

1. one kind is based on the matched Products Show method of user characterized by comprising

If the client information table that administrator is inputted is received, according to preset clustering rule to included in the client information table Client clustered to obtain customers' information, wherein further include the product of each client purchase in the client information table Information；

Mould is matched comprising the client of input node and output node according to customers' information and preset full connection hidden layer building Type；

Constructed client's Matching Model is trained according to preset training parameter with client's Matching Model after being trained；

If receiving client to be recommended passes through the customer information to be recommended that user terminal is inputted, by the customer information to be recommended Client's Matching Model after input training, and obtain in client's Matching Model the matching degree highest between the client to be recommended Customers as target customers；

The product information in the client information table is carried out according to preset target product screening rule and the target customers Screening is to obtain target product and push to the user terminal.

2. according to claim 1 be based on the matched Products Show method of user, which is characterized in that described according to preset poly- Rule-like clusters client included in the client information table to obtain customers' information, comprising:

Customer information included in the client information table is turned according to the characteristic variable transformation rule in the clustering rule It is changed to characteristic variable；

According in the clustering rule clustering algorithm and the characteristic variable client is clustered to obtain comprising multiple visitors Customers' information of family group.

3. according to claim 1 be based on the matched Products Show method of user, which is characterized in that described according to the visitor Family group's information and preset full client Matching Model of the connection hidden layer building comprising input node and output node, comprising:

According to the input node of dimension building client's Matching Model of client characteristics variable in customers' information；

According to the output node of quantity building client's Matching Model of customers in customers' information；

According to it is preset it is full connection hidden layer in feature unit and constructed input node, using input node value as input value, Feature unit value constructs the first formula group of input node to feature unit as output valve；

According to it is preset it is full connection hidden layer in feature unit and constructed output node, using feature unit value as input value, Second formula group of the output node value as output valve construction feature unit to output node.

4. according to claim 1 be based on the matched Products Show method of user, which is characterized in that in the training parameter Matching degree including parameter regulation coefficient, the characteristic variable of multiple clients and each client and corresponding target customers, described Constructed client's Matching Model is trained according to preset training parameter with client's Matching Model after being trained, comprising:

The characteristic variable for obtaining a client in preset training parameter, according to respective formula and input node value in the first formula group The feature unit value of all feature units in full connection hidden layer is calculated；

The output node value of corresponding output node is calculated according to respective formula and feature unit value in the second formula group；

The matching degree and target output node value corresponding with the target customers of existing customer and corresponding target customers are obtained, It is calculated according to matching degree and target output node value of the mean square error function to target customers to obtain existing customer Square mean error amount；

Judged according to preset mean square error threshold value whether square mean error amount is less than mean square error threshold value, according to judging result And the parameter regulation coefficient is adjusted update to the parameter of formula in client's Matching Model.

5. according to claim 1 be based on the matched Products Show method of user, which is characterized in that the client to be recommended It include the characteristic variable of client to be recommended in information, the client by after the customer information input training to be recommended matches mould Type, and the highest customers of matching degree are obtained in client's Matching Model between the client to be recommended as target customer Group, comprising:

Input node value of the characteristic variable of client to be recommended as respective formula in the first formula group is obtained, according to the first formula The feature unit value of all feature units in full connection hidden layer is calculated in respective formula and input node value in group；

The output node value of all output nodes is calculated according to respective formula and feature unit value in the second formula group；

According to customers corresponding to the output node value acquisition highest output node value being calculated as target customers.

6. according to claim 1 be based on the matched Products Show method of user, which is characterized in that described according to preset mesh Mark product screening rule and the target customers screen to obtain target the product information in the client information table Product simultaneously pushes to the user terminal, comprising:

Obtain target product information corresponding with the target customers in the product information of client information table；

It is screened according to target product screening rule pair and the target product information to obtain target product and push to institute State user terminal.

7. one kind is based on the matched Products Show device of user characterized by comprising

Cluster cell, if the client information table inputted for receiving administrator, according to preset clustering rule to the client Client included in information table is clustered to obtain customers' information, wherein further includes each in the client information table The product information of client's purchase；

Client's Matching Model construction unit, for including input section according to customers' information and preset full connection hidden layer building Client's Matching Model of point and output node；

Client's Matching Model training unit, for according to preset training parameter to constructed client's Matching Model be trained with Client's Matching Model after being trained；

Target customers' matching unit, if the client to be recommended letter inputted for receiving client to be recommended by user terminal Breath by client's Matching Model after the customer information input training to be recommended, and is obtained in client's Matching Model and is somebody's turn to do The highest customers of matching degree are as target customers between client to be recommended；

Product information screening unit, for being believed according to preset target product screening rule and the target customers the client Product information in breath table is screened to obtain target product and push to the user terminal.

8. according to claim 7 be based on the matched Products Show device of user, which is characterized in that the cluster cell, Include:

Characteristic variable converting unit, for according to the characteristic variable transformation rule in the clustering rule by the client information table Included in customer information be converted to characteristic variable；

Customer clustering unit, for according in the clustering rule clustering algorithm and the characteristic variable client is clustered To obtain customers' information comprising multiple customers.

9. a kind of computer equipment, including memory, processor and it is stored on the memory and can be on the processor The computer program of operation, which is characterized in that the processor realizes such as claim 1 to 6 when executing the computer program Any one of described in based on the matched Products Show method of user.

10. a kind of computer readable storage medium, which is characterized in that the computer-readable recording medium storage has computer journey Sequence, the computer program make the processor execute such as base as claimed in any one of claims 1 to 6 when being executed by a processor In the matched Products Show method of user.