CN113762678A - Method, apparatus, device and medium for determining preferred manufacturer of product - Google Patents

Method, apparatus, device and medium for determining preferred manufacturer of product Download PDF

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CN113762678A
CN113762678A CN202011199808.9A CN202011199808A CN113762678A CN 113762678 A CN113762678 A CN 113762678A CN 202011199808 A CN202011199808 A CN 202011199808A CN 113762678 A CN113762678 A CN 113762678A
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姜盛乾
康胜苏
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Beijing Jingdong Zhenshi Information Technology Co Ltd
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Abstract

The present disclosure provides a method, apparatus, device and medium for determining a preferred manufacturer of a product, wherein the method for determining a preferred manufacturer of a product comprises: determining product characteristic information, wherein the product characteristic information comprises a plurality of product characteristic labels; determining manufacturer characteristic information, wherein the manufacturer characteristic information comprises a plurality of manufacturer characteristic tags; pairing each product feature tag of the plurality of product feature tags with each manufacturer feature tag of the plurality of manufacturer feature tags one to obtain a plurality of tag pairs; determining the matching degree of each label pair in a plurality of label pairs; determining the matching degree of the product and the manufacturer according to the matching degree of each label pair; and determining the preferred manufacturer of the product from the manufacturers to be selected according to the determined matching degree.

Description

Method, apparatus, device and medium for determining preferred manufacturer of product
Technical Field
The present disclosure relates to the field of information technology, and more particularly, to a method, apparatus, device, and medium for determining a preferred manufacturer of a product.
Background
With the development of market economy, the market demand for products is changed from standardization, mass production to customization, small-batch production, and the product demand of users is also gradually a key factor for driving the development of the manufacturing industry, so that the development of C2M reverse customization in the field of e-commerce is promoted. However, the development of C2M still has some problems, such as lack of effective matching between users and manufacturers, and failure to really solve the product demand of users. Therefore, an efficient and accurate product and manufacturer matching method is needed.
Disclosure of Invention
In view of the above, the present disclosure provides a method, apparatus, device, and medium for determining a preferred manufacturer of a product.
One aspect of the present disclosure provides a method of determining a preferred manufacturer of a product, comprising:
determining product characteristic information, wherein the product characteristic information comprises a plurality of product characteristic labels;
determining manufacturer characteristic information, wherein the manufacturer characteristic information comprises a plurality of manufacturer characteristic tags;
pairing each of the plurality of product feature labels with each of the plurality of manufacturer feature labels one-to-one to obtain a plurality of label pairs;
determining a matching degree of each label pair in the plurality of label pairs;
determining the matching degree of the product and a manufacturer according to the matching degree of each label pair; and
and determining the preferred manufacturer of the product from the manufacturers to be selected according to the determined matching degree.
According to an embodiment of the present disclosure, determining product characteristic information includes:
acquiring product search data, evaluation data and sales data of a user;
classifying the product searching data, the evaluation data and the sales data to determine product classification information;
processing the product classification information to determine optimal product classification information;
matching the plurality of product feature labels with the optimal product classification information to determine product feature information.
According to an embodiment of the present disclosure, determining the manufacturer characteristic information includes:
acquiring manufacturer attribute data;
classifying the manufacturer attribute data to determine manufacturer classification information;
processing the manufacturer classification information to determine the optimal manufacturer classification information;
matching the plurality of manufacturer feature labels with the optimal manufacturer classification information to determine manufacturer feature information.
According to an embodiment of the present disclosure, matching a product feature label with the optimal product classification information, whereby determining product feature information comprises:
constructing a product characteristic label database;
and matching the product characteristic labels in the product characteristic label database by using the optimal product classification information, thereby determining product characteristic information.
According to an embodiment of the present disclosure, matching the plurality of manufacturer feature labels with the optimal manufacturer classification information to determine manufacturer feature information comprises: constructing a manufacturer feature tag database;
matching manufacturer feature tags in the manufacturer feature tag database with the optimal manufacturer classification information to determine manufacturer feature information.
According to an embodiment of the present disclosure, determining a matching degree of each of the plurality of tag pairs includes: and determining the matching degree of each label pair in the plurality of label pairs according to the weight of the vocabulary of each label pair, the word number coefficient of the vocabulary and the structure influence coefficient of the vocabulary.
According to the embodiment of the disclosure, if the plurality of product feature tags and the plurality of manufacturer feature tags form n tag pairs, the matching degrees of the n tag pairs are respectively A1～AnThen, the matching degree of the product and the manufacturer is calculated as follows:
ψ＝A1τ1+A2τ2+…+Anτn
wherein, tau1Denotes the 1 st tag pair weight coefficient, τ2Represents the 2 nd label pair weight coefficient, τnRespectively, the nth tag pair weight coefficients.
According to the embodiment of the disclosure, determining the preferred manufacturer of the product from the candidate manufacturers according to the determined matching degree comprises: and determining the manufacturer with the highest matching degree with the product in the candidate manufacturers as a preferred manufacturer.
According to the embodiment of the disclosure, determining the preferred manufacturer of the product from the candidate manufacturers according to the determined matching degree comprises:
setting a matching degree threshold value;
and taking the manufacturer with the matching degree with the product exceeding the threshold value in the candidate manufacturers as a preferred manufacturer.
Another aspect of the present disclosure provides an apparatus for determining a preferred manufacturer of a product, comprising:
a first determination module to determine product characteristic information, wherein the product characteristic information comprises a plurality of product characteristic labels;
a second determination module to determine manufacturer characteristic information, wherein the manufacturer characteristic information includes a plurality of manufacturer characteristic tags;
a third determining module, configured to pair each of the plurality of product feature tags with each of the plurality of manufacturer feature tags one to obtain a plurality of tag pairs;
a fourth determining module, configured to determine a matching degree of each of the plurality of tag pairs;
the matching module is used for determining the matching degree of the product and a manufacturer according to the matching degree of each label pair; and
and the fifth determining module is used for determining the preferred manufacturer of the product from the manufacturers to be selected according to the determined matching degree.
Another aspect of the present disclosure provides a computer system comprising: one or more processors; memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described above.
Another aspect of the disclosure provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to implement the method as described above.
According to an embodiment of the present disclosure, a method of determining a preferred manufacturer of a product is employed, comprising: determining product characteristic information; determining manufacturer characteristic information; determining the matching degree of the product and the manufacturer according to the product characteristic information and the manufacturer characteristic information; and determining the preferred manufacturer of the product from the candidate manufacturers according to the determined matching degree. Therefore, the product is efficiently and accurately matched with a manufacturer, and the product requirements of users are favorably met.
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The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an application scenario of a method, apparatus, device and medium of determining a preferred manufacturer of a product according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a method of determining a preferred manufacturer of a product according to an embodiment of the disclosure;
FIG. 3 schematically illustrates a flow chart of a method of determining a preferred manufacturer of a product according to another embodiment of the present disclosure;
FIG. 4 schematically illustrates a cross-directional property and a machine-directional property of a product according to an embodiment of the disclosure;
FIG. 5 schematically illustrates a flow chart for determining product characteristic information using optimal product classification information matching product characteristic labels according to an embodiment of the disclosure;
FIG. 6 schematically illustrates a flow chart for determining manufacturer characteristic information using optimal manufacturer classification information matching manufacturer characteristic labels according to an embodiment of the disclosure;
FIG. 7 schematically illustrates a block diagram of an apparatus for determining a preferred manufacturer of a product according to an embodiment of the disclosure; and
fig. 8 schematically illustrates a block diagram of a computer system 800 suitable for implementing a robot in accordance with an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
FIG. 1 schematically illustrates a scenario of a method, apparatus, device and medium to determine a preferred manufacturer of a product to which embodiments of the disclosure may be applied. It should be noted that fig. 1 is only an example of a scenario in which the embodiment of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiment of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to this embodiment may include a terminal device 101, a network 102, and a server 103. Network 102 is the medium used to provide communication links between terminal devices 101 and server 105. Network 102 may include various connection types, such as wired and/or wireless communication links, and so forth.
A user may use terminal device 101 to interact with server 103 over network 102 to receive or send messages and the like. Various communication client applications, such as a shopping-type application (for example only), may be installed on the terminal device 101.
The terminal device 101 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 103 may be a server that provides various services, such as a background management server (for example only) that provides support for websites browsed by users using the terminal devices 101. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a manufacturer that generates a match according to the product request output by the user, etc.) to the terminal device.
It should be noted that the method for determining a preferred manufacturer of a product provided by the embodiments of the present disclosure may be generally performed by the server 103. The method of determining a preferred manufacturer of a product provided by embodiments of the present disclosure may also be performed by a server or a cluster of servers different from the server 103 and capable of communicating with the terminal device 101 and/or the server 103. Alternatively, the method for determining the preferred manufacturer of the product provided by the embodiment of the present disclosure may also be executed by the terminal device 101, or may also be executed by another terminal device different from the terminal device 101.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2 schematically illustrates a flow chart of a method of determining a preferred manufacturer of a product according to an embodiment of the disclosure. As shown in FIG. 2, the method operates S210-S260.
Determining product characteristic information, wherein the product characteristic information includes a plurality of product characteristic tags, in operation S210;
determining manufacturer characteristic information in operation S220, wherein the manufacturer characteristic information includes a plurality of manufacturer characteristic tags;
in operation S230, pairing each of the plurality of product feature tags with each of the plurality of manufacturer feature tags one to obtain a plurality of tag pairs;
in operation S240, determining a matching degree of each of a plurality of tag pairs;
in operation S250, determining a matching degree of the product with the manufacturer according to the matching degree of each of the plurality of label pairs; and
in operation S260, a preferred manufacturer of the product is determined from the candidate manufacturers according to the determined matching degree.
Fig. 3 schematically illustrates a flow chart of a method of determining a preferred manufacturer of a product according to another embodiment of the present disclosure. As shown in fig. 3, the method includes operations S310 to S390.
In operation S310, product search data, evaluation data, and sales data of a user are acquired. Illustratively, the real search hotspot data (search data), evaluation data (evaluation data) and sales data (sale data) of the user are acquired through a platform (e.g. a power company platform) as a basis for determining product characteristic information required by the user.
In operation S320, the product search data, the evaluation data, and the sales data are classified to determine product classification information. For example, the reduction and classification processing is performed on the obtained data to determine the attention point of the user to the product and the product classification information, that is, the product classification information, and the product classification information may be determined according to the attributes of the product itself, the applicable objects, the functions, and the like, but is not limited thereto.
According to the embodiment of the disclosure, classification processing is required for obtaining real hot spot searching data, evaluation data and sales data of a user, data is drilled down, proportion statistics processing is carried out on the data drilled down to each layer, and specific gravity of characteristic attributes of products is obtained.
For example, there are a large number of consumers' search data, evaluation data and consumption data for clothing, from which to find products with obvious characteristics and their characteristic attributes.
According to an embodiment of the present disclosure, in operation S320, the product search data, the evaluation data, and the sales data are classified, and determining the product classification information may include, for example, data classification and determination of a final index.
According to an embodiment of the present disclosure, data is first classified, and the data is divided according to a data attribute m, for example, in a manner of being divided into horizontal attributes (Hm)i) And longitudinal attributes (Vm)j) As shown in fig. 4, where i and j are both greater than 0.
Illustratively, the lateral attributes primarily include peer-level attribute features of the product, e.g., where the product requirements are garments, the lateral attributes may include attributes for men's clothing, women's clothing, children's clothing, undergarments, etc. The data judgment method of the transverse attribute is as follows: a sample space x ═ x (x) consisting of s data1，x2，，，xs) Each data has i attribute index observations, for the t-th sample observation xt＝(xt1，xt2，，，xtc)，xtcIs the observed value of the sample t in the attribute c, wherein t is more than or equal to 1 and less than or equal to s. Non-negative function f of the disclosuret＝f(xt，xt+1) Represents the correlation between the t sample and the t +1 sample, and t is more than or equal to 1 and less than or equal to s-1. Wherein f istThe calculation method of (c) is as follows:
Figure BDA0002754436330000081
after the data in the above formula are classified according to the attributes of the data, the horizontal attribute Hm will be classified according to the data sizeiSorting, judging the aggregation degree of the data, and automatically judging the maximum data ratioAnd entering longitudinal attribute judgment. For example, when the man clothing proportion is the largest, the determination of the next level attribute (vertical attribute) is automatically performed. Here, by calculating the degree of correlation ftTwo sets of data t samples x can be confirmedtAnd t +1 th sample xt+1Whether or not there is a correlation, and if there is a correlation, the two sets of data can be classified into one type of data, for example, both can be classified into men's clothing.
Illustratively, the longitudinal attributes mainly include attributes obtained by performing a drilling process on the transverse attributes of the product. That is, the longitudinal attribute determination is to drill down the next layer of the first-level attribute after determining the first-level attribute (transverse attribute) to obtain the second-level attribute (longitudinal attribute). For example, where the lateral attributes are determined to be men's wear, their corresponding longitudinal attributes may include attributes of men's casual wear, men's sportswear, men's suit, men's underwear, and the like. The data judgment mode of the longitudinal attribute is similar to that of the transverse attribute as follows, and is not repeated herein until the final layer is executed to determine the characteristic requirement of a specific product. For example, the user demands personalized requirements for various indexes of men's western-style clothes in the recent period of time.
According to an embodiment of the disclosure, the final index is determined as follows: in order to explain the characteristic determination mode of the index of the final product, 4 indexes of the size of the suit, the type of a collar, the number of cuff buttons, the number of chest buttons and the like are selected as the evaluation indexes of the final product, and an index set x is { x ═ x {1，x2，x3，x4}. It should be noted that, a person skilled in the art may select one or more indexes as a final index according to actual conditions and replace the final index, and for example, one or more indexes with the highest attention degree may be selected as the final index. The present disclosure will now describe the manner of determining the 4 indices by taking men's suit as an example.
According to an embodiment of the present disclosure, the size is determined as follows: the customization of suit needs to collect 19 data of 10 parts of human body as the basis of confirming suit size, wherein 10 parts are { neck, shoulder, chest, waist, arm, ankle, buttock, shank, back, crotch }, 19 data are { clothing length, neck circumference, shoulder width, chest circumference, well waist, waistline, lower hem, sleeve length, arm fat, trousers waist, buttockss circumference, trousers length, middle rail, foot mouth, standing, straight rail, crosspiece, well circumference }, this disclosure sets up 22 different interval numerical values of data, through the combination of different interval numerical values, confirms the size of final clothes. According to the analysis of the data, the high data ratio of a certain combination q is determined to be a certain size.
According to the embodiment of the disclosure, the collar model is determined as follows: the general types of the necklines are three, the type set is N ═ T type, X type and H type, the data occupation ratio of each type of the neckline is respectively alpha, beta and gamma according to the data, and the data occupation ratio is more than beta and more than gamma, and the trend of the product is displayed.
According to an embodiment of the present disclosure, the number of cufflinks is determined as follows: the number of the cuffs has two forms c1，c2I.e. c ═ c1，c2The results are determined from an analysis of the data.
According to an embodiment of the present disclosure, the number of chest buttons is determined as follows: the number of the buttons at the chest has three types d1，d2And d3I.e. d ═ d1，d2，d3The results are determined from an analysis of the data.
In operation S330, the product classification information is processed to determine optimal product classification information. According to an embodiment of the present disclosure, in operation S330, the processing the product classification information to determine the optimal product classification information may include, for example: and evaluating the optimal solution of the final characteristic attributes of the product, and judging whether the classification result meets the global optimum. The specific determination method is as follows:
illustratively, the data set of the demand of the user for the product is obtained from the database, and m is an european space data set R, where X is { X ═ X }1，x2，，，xnE R, where the data includes data on the type of garment, such as suit, casual wear, T-shirt, etc., the user's needs, such as color, style, cuff or collar style, etc., n represents the number of data in the data set,s represents the clustering center of the data, where 1 < s < n, and k is the weight coefficient (k > 1). After the data is classified in operation S330, the classification result needs to be evaluated in the following steps.
Let dij＝||xi-VjI represents the sample point xiAnd a cluster center VjIn the Euclidean distance of, wherein VjAre R, uijIs the membership degree of the ith sample belonging to the jth class, such as how great the probability of the neckline data belongs to the business suit class, and U ═ Uij]Is an n x s matrix, such as how many of all garment attributes are affiliated with the suit; v ═ V1，V2，，，Vc]Is an m × s matrix, let:
Figure BDA0002754436330000101
so that
Figure BDA0002754436330000102
Figure BDA0002754436330000103
Illustratively, the present disclosure adopts a function obtained by deviatoderivative of the weight coefficient as a basis for determining whether the data classification effect is reasonable, wherein the function obtained by deviatoderivative of the weight coefficient is specifically as follows:
Figure BDA0002754436330000104
under the condition that the fixed weight coefficient is not changed, the better the data classification effect is, the larger the value of A (U) is; the poorer the data classification effect, the smaller the value of a (u).
Of course, the manner of determining the optimal product classification information is not limited to the above manner.
In operation S340, the plurality of product feature labels are matched using the optimal product classification information, thereby determining product feature information. Exemplary, generalAfter data analysis and generalization of product characteristics, a product characteristic label is obtained, and the product characteristic information is, for example, a first label library σ ═ { σ ═ σ1，σ2，，，σnIn which σ is1，σ2，，，σnA label included for product characteristic information. According to an embodiment of the present disclosure, as shown in fig. 5, in operation S340, matching the product feature label with the optimal product classification information, thereby determining the product feature information may include, for example:
in operation S341, a product feature tag database is constructed. According to the embodiment of the disclosure, a product feature tag database is constructed according to the product features of various products of the platform. Illustratively, the data within the platform includes feature tags in all product libraries and data tags in reviews by users that can display product features, such as brand, model, size, color, genre, breast size, collar, etc.
In operation S342, the optimal product classification information is used to match the product feature labels in the product feature label database, thereby determining product feature information. According to the embodiment of the disclosure, the characteristic attribute of the required product is obtained through data analysis, and the obtained characteristic attribute of the product is matched with the characteristic attribute of the database to form the product label. Such as men, business suit, T-shaped neckline, 2 buttons on the chest, 4 buttons on the cuff, optional size and the like.
According to an embodiment of the present disclosure, the product characteristic information may be determined through the above-described operations S310 to S350.
In operation S350, manufacturer attribute data is acquired.
In operation S360, the manufacturer attribute data is classified, and manufacturer classification information is determined. For example, the manufacturer classification information, that is, the category information of the manufacturer, may be determined according to the attribute of the manufacturer itself, the object manufactured by the manufacturer, the quality of the manufacturer, and the like, but is not limited thereto. According to an embodiment of the present disclosure, in operation S360, the process of classifying the manufacturer' S data is as follows:
the manufacturer is divided into star grades, the standard of the star grade division is brand popularity f, and the weight occupied is omega1The weight of market credit c is omega2Product quality q, weight of omega3User's preference p, with a weight of ω4Market response speed s, weighted by ω5And satisfy omegaiIs not less than 0 and
Figure BDA0002754436330000111
the composite score of these five factors determines the manufacturer's rating, with the ratings being shown in table 1. The comprehensive rating is divided into:
d＝ω1f+ω2c+ω3q+ω4p+ω5s
TABLE 1 grading Table
Figure BDA0002754436330000112
In operation S370, the manufacturer classification information is processed to determine optimal manufacturer classification information. According to an embodiment of the present disclosure, the specific operation manner at operation S370 may be identical to that at operation S330.
In operation S380, the plurality of manufacturer feature tags are matched using the optimal manufacturer classification information, thereby determining manufacturer feature information. Illustratively, a manufacturer signature tag is derived by data analysis and generalization of product signatures, such as a second library of tags
Figure BDA0002754436330000121
Wherein
Figure BDA0002754436330000122
A label included for the manufacturer characteristic information. According to an embodiment of the present disclosure, the specific operation manner at operation S380 may be identical to that at operation S340.
According to an embodiment of the present disclosure, as shown in fig. 6, in operation S380, matching the manufacturer feature tag with the optimal manufacturer classification information, thereby determining the manufacturer feature information may include, for example:
in operation S381, a manufacturer signature tag database is constructed.
In operation S382, the manufacturer feature tag in the manufacturer feature tag database is matched using the optimal manufacturer classification information, thereby determining manufacturer feature information.
According to an embodiment of the present disclosure, the manufacturer characteristic information may be determined through the above-described operations S350 to S380.
In operation S390, determining a matching degree of the product and the manufacturer according to the product characteristic information and the manufacturer characteristic information; and determining the preferred manufacturer of the product from the manufacturers to be selected according to the determined matching degree.
According to an embodiment of the present disclosure, the characteristic tag of the product is a first tag library of z ═ { σ ═ a1，σ2，，，σn}, the manufacturer's property label is a second library of labels
Figure BDA0002754436330000123
The tags of the first tag library correspond to the tags of the second tag library one by one to form a plurality of tag pairs, the weight of morphemes needs to be considered when the tags of the two tag libraries are matched, the word number of the vocabulary a of the product feature tag is defined as m (a), the word number of the vocabulary of the supplier tag vocabulary b is defined as m (b), p is the number of matched words, q is the influence coefficient of the vocabulary structure on the similarity, and p and q are less than or equal to 1, and p + q is 1.
Figure BDA0002754436330000124
The weight of each Chinese character from left to right for the vocabulary of each label, an
Figure BDA0002754436330000125
The matching degree of each label pair is:
Figure BDA0002754436330000131
wherein L (a, b) is of two tags participating in matchingThe length ratio of the words, specifying that when m (a) < m (b),
Figure BDA0002754436330000132
when m (a) > m (b),
Figure BDA0002754436330000133
and ensuring that L (a, b) < 1 and P (a, b) < 1, wherein the matching of each pair of labels has the probability, and the probability is endowed with a certain weight according to different matching degrees. Let p beiP (a, b), the supplier indices are summarized as:
Figure BDA0002754436330000134
illustratively, after data collection and data analysis processing, an attribute label of a suit product for men is obtained, the label of the product attribute is suit for men, 3 cuff buttons, 2T-shaped necklines and chest buttons, and the corresponding supplier database is E ═ (w ═ b)1，w2...wt) By using the above to calculate P (a)i，bi) Calculates a match value with each supplier in the supplier database, the total division of the first supplier is psi1＝p1τ1+p2τ2And by analogy, calculating the overall score condition of other suppliers through t rounds of calculation. Are respectively psi1，ψ2...ψt. The final score is then correlated to the supplier rating to determine a determined rating for the supplier.
According to the embodiment of the disclosure, the manufacturer with the highest matching degree with the product in the candidate manufacturers can be determined as the preferred manufacturer, and a matching degree threshold value can also be set, and the manufacturer with the matching degree with the product exceeding the matching degree threshold value in the candidate manufacturers is determined as the preferred manufacturer.
Illustratively, the matching correlation coefficients are divided into four classes: the gear is more than or equal to 0.8, the gear represents complete matching and can be directly used as an optimal manufacturer and produced by the manufacturer; the gear is < 0.8, is a non-preferred manufacturer, specifically is a gear from 0.6 to 0.8, represents similarity matching, and can be negotiated with the manufacturer to change certain attributes to cooperate with production; the gear 0.4-0.6 belongs to weak matching, which means that the manufacturer has few matching labels and most of the labels are not matched, and the manufacturer is not recommended to produce the gear; the < 0.4 gear indicates no match and is not recommended for production by the plant. As shown in table 2 below.
TABLE 2 matching results table
Value of correlation coefficient Degree of correlation Description of the invention
≥0.8 Complete matching Can be produced entirely by the manufacturer
0.6-0.8 Similarity matching Can negotiate with the manufacturer for production
0.4-0.6 Weak matching Is not recommended to be produced by the manufacturer
＜0.4 Mismatch The manufacturer cannot produce
Aiming at the problems existing in the current C2M reverse customization mode, the data acquisition advantage of the E-commerce platform is used, so that a large amount of data purchased by a consumer can be acquired through the E-commerce platform, the problem of commercial confidentiality of a public manufacturer can be avoided, the demand data of the consumer can be analyzed, and the commodity characteristic information required by the consumer can be reversely deduced; meanwhile, the product characteristic label and the manufacturer attribute label can be matched, so that the most suitable manufacturer is matched for the user, and the efficiency of the C2M reverse customization mode is improved.
Fig. 7 schematically illustrates a block diagram of an apparatus for determining a preferred manufacturer of a product according to an embodiment of the disclosure.
As shown in fig. 7, the apparatus 700 for determining a preferred manufacturer of a product includes a first determining module 710, a second determining module 720, a third determining module 730, a fourth determining module 740, a matching module 750, and a fifth determining module 760.
The first determining module 710 is used for determining product characteristic information, wherein the product characteristic information comprises a plurality of product characteristic labels; in an embodiment, the first determining module 710 may be configured to perform operation S210 described in fig. 2, for example, and is not described herein again.
The second determining module 720 is configured to determine manufacturer characteristic information, wherein the manufacturer characteristic information includes a plurality of manufacturer characteristic tags; in an embodiment, the second determining module 720 may be configured to perform operation S220 described in fig. 2, for example, and is not described herein again.
The third determining module 730 is configured to pair each of the plurality of product feature tags with each of the plurality of manufacturer feature tags one to obtain a plurality of tag pairs. In an embodiment, the third determining module 730 may be configured to perform operation S230 described in fig. 2, for example, and is not described herein again.
A fourth determining module 740, configured to determine a matching degree of each of the plurality of tag pairs. In an embodiment, the fourth determining module 740 may be configured to perform the operation S240 described in fig. 2, for example, and is not described herein again.
The matching module 750 is configured to determine a matching degree between the product and the manufacturer according to the product characteristic information and the manufacturer characteristic information; in an embodiment, the matching module 750 may be used to perform the operation S250 described in fig. 2, for example, and is not described herein again.
A fifth determining module 760, configured to determine a preferred manufacturer of the product from the manufacturers to be selected according to the determined matching degree; in an embodiment, the fifth determining module 760 may be configured to perform operation S260 described in fig. 2, for example, and is not described herein again.
FIG. 8 schematically illustrates a block diagram of a computer system for a method of determining a preferred manufacturer of a product according to an embodiment of the disclosure. The computer system illustrated in FIG. 8 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 8, a computer system 800 according to an embodiment of the present disclosure includes a processor 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. The processor 801 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.
In the RAM 803, various programs and data necessary for the operation of the system 800 are stored. The processor 801, the ROM802, and the RAM 803 are connected to each other by a bus 804. The processor 801 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM802 and/or RAM 803. Note that the programs may also be stored in one or more memories other than the ROM802 and RAM 803. The processor 801 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
System 800 may also include an input/output (I/O) interface 805, also connected to bus 804, according to an embodiment of the disclosure. The system 800 may also include one or more of the following components connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program, when executed by the processor 801, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples may include, but are not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM802 and/or RAM 803 described above and/or one or more memories other than the ROM802 and RAM 803.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (12)

1. A method of determining a preferred manufacturer of a product, comprising:
determining product characteristic information, wherein the product characteristic information comprises a plurality of product characteristic labels;
determining manufacturer characteristic information, wherein the manufacturer characteristic information comprises a plurality of manufacturer characteristic tags;
pairing each of the plurality of product feature labels with each of the plurality of manufacturer feature labels one-to-one to obtain a plurality of label pairs;
determining a matching degree of each label pair in the plurality of label pairs;
determining the matching degree of the product and a manufacturer according to the matching degree of each label pair; and
and determining the preferred manufacturer of the product from the manufacturers to be selected according to the determined matching degree.
2. The method of claim 1, wherein determining product characteristic information comprises:
acquiring product search data, evaluation data and sales data of a user;
classifying the product searching data, the evaluation data and the sales data to determine product classification information;
processing the product classification information to determine optimal product classification information;
matching the plurality of product feature labels with the optimal product classification information to determine product feature information.
3. The method of claim 2, wherein determining manufacturer characteristic information comprises:
acquiring manufacturer attribute data;
classifying the manufacturer attribute data to determine manufacturer classification information;
processing the manufacturer classification information to determine the optimal manufacturer classification information;
matching the plurality of manufacturer feature labels with the optimal manufacturer classification information to determine manufacturer feature information.
4. The method of claim 3, wherein matching the plurality of product feature labels with the optimal product classification information to determine product feature information comprises:
constructing a product characteristic label database;
and matching the product characteristic labels in the product characteristic label database by using the optimal product classification information to determine product characteristic information.
5. The method of claim 4, wherein matching the plurality of manufacturer feature labels using the optimal manufacturer classification information to determine manufacturer feature information comprises
Constructing a manufacturer feature tag database;
matching manufacturer feature tags in the manufacturer feature tag database with the optimal manufacturer classification information to determine manufacturer feature information.
6. The method of claim 1, wherein determining a degree of match for each of the plurality of tag pairs comprises: and determining the matching degree of each label pair in the plurality of label pairs according to the weight of the vocabulary of each label pair, the word number coefficient of the vocabulary and the structure influence coefficient of the vocabulary.
7. The method of claim 6, wherein if the plurality of product feature tags and the plurality of manufacturer feature tags form n tag pairs, the matching degree of the n tag pairs is A1～AnThen, the matching degree of the product and the manufacturer is calculated as follows:
ψ＝A1τ1+A2τ2+…+Anτn
wherein, tau1Denotes the 1 st tag pair weight coefficient, τ2Represents the 2 nd label pair weight coefficient, τnRespectively, the nth tag pair weight coefficients.
8. The method of claim 1, wherein determining a preferred manufacturer of products from the candidate manufacturers based on the determined degree of match comprises: and determining the manufacturer with the highest matching degree with the product in the candidate manufacturers as a preferred manufacturer.
9. The method of claim 1, wherein determining a preferred manufacturer of products from the candidate manufacturers based on the determined degree of match comprises:
setting a matching degree threshold value;
and taking the manufacturer with the matching degree with the product exceeding the threshold value in the candidate manufacturers as a preferred manufacturer.
10. An apparatus for determining a preferred manufacturer of a product, comprising:
a first determination module to determine product characteristic information, wherein the product characteristic information comprises a plurality of product characteristic labels;
a second determination module to determine manufacturer characteristic information, wherein the manufacturer characteristic information includes a plurality of manufacturer characteristic tags;
a third determining module, configured to pair each of the plurality of product feature tags with each of the plurality of manufacturer feature tags one to obtain a plurality of tag pairs;
a fourth determining module, configured to determine a matching degree of each of the plurality of tag pairs;
the matching module is used for determining the matching degree of the product and a manufacturer according to the matching degree of each label pair; and
and the fifth determining module is used for determining the preferred manufacturer of the product from the manufacturers to be selected according to the determined matching degree.
11. A computer system, comprising:
one or more processors;
a memory for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-9.
12. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 9.
CN202011199808.9A 2020-10-30 2020-10-30 Method, apparatus, device and medium for determining preferred manufacturer of product Pending CN113762678A (en)

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