CN117035934A - Multi-dimensional cross-border commodity matching method and system - Google Patents
Multi-dimensional cross-border commodity matching method and system Download PDFInfo
- Publication number
- CN117035934A CN117035934A CN202311038416.8A CN202311038416A CN117035934A CN 117035934 A CN117035934 A CN 117035934A CN 202311038416 A CN202311038416 A CN 202311038416A CN 117035934 A CN117035934 A CN 117035934A
- Authority
- CN
- China
- Prior art keywords
- commodity
- cross
- border
- target
- feature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000012216 screening Methods 0.000 claims abstract description 105
- 238000004458 analytical method Methods 0.000 claims abstract description 46
- 238000013461 design Methods 0.000 claims abstract description 25
- 230000014759 maintenance of location Effects 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 5
- 230000003993 interaction Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 description 10
- 230000000694 effects Effects 0.000 description 10
- 238000001514 detection method Methods 0.000 description 7
- 230000006978 adaptation Effects 0.000 description 6
- 238000006243 chemical reaction Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000009467 reduction Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000003708 edge detection Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000033228 biological regulation Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0641—Shopping interfaces
- G06Q30/0643—Graphical representation of items or shoppers
Abstract
The application discloses a multi-dimensional cross-border commodity matching method and a multi-dimensional cross-border commodity matching system, which relate to the technical field of cross-border electronic commerce, wherein the method comprises the following steps: invoking target input information of target cross-border consumers; determining the direction of a target item, and identifying a label with the attribute of the target item; obtaining a target commodity purchase record; obtaining first dimension screening characteristics, wherein the first dimension screening characteristics are determined by price characteristic fluctuation analysis on target commodity purchase records; obtaining second dimension screening characteristics, wherein the second dimension screening characteristics are determined by carrying out design characteristic analysis on the target commodity purchasing record; obtaining an alternative cross-border commodity set by adopting target option direction matching; traversing the alternative cross-border commodity set by taking the first dimension screening feature and the second dimension screening feature as constraints to obtain the cross-border commodity set; presetting a cross-border commodity display rule, and generating a browsing interface of a cross-border commodity set based on the cross-border commodity display rule.
Description
Technical Field
The application relates to the technical field of cross-border electronic commerce, in particular to a multi-dimensional cross-border commodity matching method and system.
Technical Field
With the rapid development of global economy and the advent of the digital age, cross-border e-commerce has become an important way of international trade. However, in cross-border commodity sales, due to the difference of cultural, consumption habit, regulation and other factors of different regions and areas, the adaptation degree of commodity recommendation is not high, and the situation of mismatch exists between the purchasing demand of a user and the recommendation result. Current commodity recommendation techniques often fail to adequately account for the multidimensional demands of users, as well as the cost of cross-border sales, thereby reducing the scientificity and accuracy of the recommendation. The technical problems of low scientific degree of cross-border commodity sales recommendation, low adaptation degree of actual commodity recommendation to user demands and buying habits and high cross-border sales cost exist.
Disclosure of Invention
The application aims to provide a multi-dimensional cross-border commodity matching method and system. The method and the device are used for solving the technical problems that in the prior art, the science of cross-border commodity sales recommendation is low, the adaptation degree of actual commodity recommendation, user requirements and buying habits is low, and the cross-border sales cost is high.
In order to solve the technical problems, the application provides a multi-dimensional cross-border commodity matching method and system.
In a first aspect, the present application provides a multi-dimensional cross-border commodity matching method, where the method includes:
invoking target input information of target cross-border consumers; determining a target option direction, wherein the target option direction is determined by carrying out similarity analysis on the target input information, and the target option direction is provided with a label mark of a target option attribute; the historical commodity purchasing records of the target cross-border consumers are interacted, and screening is conducted on the historical commodity purchasing records based on the target option attributes to obtain target commodity purchasing records; obtaining first dimension screening characteristics, wherein the first dimension screening characteristics are determined by price characteristic fluctuation analysis on the target commodity purchase records; obtaining second dimension screening characteristics, wherein the second dimension screening characteristics are determined by carrying out design characteristic analysis on the target commodity purchasing record; obtaining an alternative cross-border commodity set by adopting the target option direction matching; traversing the alternative cross-border commodity set by taking the first dimension screening feature and the second dimension screening feature as constraints to obtain a cross-border commodity set; presetting a cross-border commodity display rule, and generating a browsing interface of the cross-border commodity set based on the cross-border commodity display rule.
In a second aspect, the present application further provides a multi-dimensional cross-border commodity matching system, where the system includes:
the consumption demand acquisition unit is used for calling target input information of target cross-border consumers; the search direction positioning module is used for determining a target selection direction, wherein the target selection direction is determined by carrying out similarity analysis on the target input information, and the target selection direction is provided with a label mark of a target selection attribute; the historical record interaction module is used for interacting historical commodity purchase records of the target cross-border consumers, screening the historical commodity purchase records based on the target option attributes and obtaining target commodity purchase records; the price dimension screening module is used for obtaining first dimension screening characteristics, and the first dimension screening characteristics are determined by price characteristic fluctuation analysis on the target commodity purchase records; the feature dimension screening module is used for obtaining second dimension screening features, and the second dimension screening features are determined by carrying out design feature analysis on the target commodity purchasing record; the primary screening alternative module is used for obtaining an alternative cross-border commodity set by adopting the target item direction matching; the multi-dimensional matching module is used for traversing the alternative cross-border commodity set by taking the first dimension screening feature and the second dimension screening feature as constraints to obtain a cross-border commodity set; the display module is used for presetting cross-border commodity display rules and generating a browsing interface of the cross-border commodity set based on the cross-border commodity display rules.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
the application calls the target input information of the target cross-border consumer; determining a target option direction, wherein the target option direction is determined by carrying out similarity analysis on target input information, and the target option direction has a label mark of a target option attribute; the historical commodity purchasing records of the cross-border consumers are interacted, and the historical commodity purchasing records are screened based on the target option attributes to obtain target commodity purchasing records; obtaining first dimension screening characteristics, wherein the first dimension screening characteristics are determined by price characteristic fluctuation analysis on target commodity purchase records; obtaining second dimension screening characteristics, wherein the second dimension screening characteristics are determined by carrying out design characteristic analysis on the target commodity purchasing record; obtaining an alternative cross-border commodity set by adopting target option direction matching; traversing the alternative cross-border commodity set by taking the first dimension screening feature and the second dimension screening feature as constraints to obtain the cross-border commodity set; presetting a cross-border commodity display rule, and generating a browsing interface of a cross-border commodity set based on the cross-border commodity display rule. The method has the technical effects of high science of cross-border commodity sales recommendation, improvement of adaptation degree of commodity recommendation and user demand, and reduction of cross-border sales cost.
The foregoing description is only an overview of the present application, and is intended to more clearly illustrate the technical means of the present application, be implemented according to the content of the specification, and be more apparent in view of the above and other objects, features and advantages of the present application, as follows.
Drawings
Embodiments of the application and the following brief description are described with reference to the drawings, in which:
FIG. 1 is a flow chart of a multi-dimensional cross-border commodity matching method according to the present application;
FIG. 2 is a schematic flow chart of obtaining a first feature set in a multi-dimensional cross-border commodity matching method according to the present application;
fig. 3 is a schematic structural diagram of a multi-dimensional cross-border commodity matching system according to the present application.
Reference numerals illustrate: the device model selection module 11, the image acquisition module 12, the image processing module 13, the edge detection module 14, the feature detection module 15 and the damage detection module 16.
Detailed Description
The application solves the technical problems of poor adaptability and low detection accuracy faced by the prior art by providing a multi-dimensional cross-border commodity matching method.
In order to solve the above problems, the technical embodiment adopts the following overall concept:
Firstly, calling target input information of a target cross-border consumer, and performing similarity analysis on the target input information to determine a target selection direction, wherein the target selection direction has a label identification of a target selection attribute; then, the historical commodity purchasing records of the target cross-border consumers are interacted, and the historical commodity purchasing records are screened based on the target option attributes to obtain target commodity purchasing records; then, obtaining first dimension screening features by carrying out price characteristic fluctuation analysis on the target commodity purchase records, and obtaining second dimension screening features by carrying out design characteristic analysis on the target commodity purchase records; then, target item direction matching is adopted to obtain an alternative cross-border commodity set; further, traversing the alternative cross-border commodity set by taking the first dimension screening feature and the second dimension screening feature as constraints to obtain the cross-border commodity set; finally, presetting a cross-border commodity display rule, and generating a browsing interface of a cross-border commodity set based on the cross-border commodity display rule. Therefore, the method has the technical effects of high scientific degree of cross-border commodity sales recommendation, improvement of the adaptation degree of commodity recommendation and user demand and reduction of cross-border sales cost.
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments, and it should be noted that the described embodiments are only some embodiments of the present application, and not all embodiments of the present application, and it should be understood that the present application is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present application are shown.
Example 1
As shown in fig. 1, the present application provides a multi-dimensional cross-border commodity matching method, which includes:
s100: invoking target input information of target cross-border consumers;
the target input information refers to input information that target cross-consumer inputs to cross-consumer platform for retrieval, and the input information includes commodity names, search keywords, query phrases, commodity descriptions, even some specific requirements or preferences and the like input on the shopping platform. By invoking the input information of these targeted cross-border consumers, we can better understand their shopping intentions and needs, thereby providing them with more appropriate merchandise retrieval results.
S200: determining a target option direction, wherein the target option direction is determined by carrying out similarity analysis on the target input information, and the target option direction is provided with a label mark of a target option attribute;
the target item direction is determined, and based on similarity analysis of the target input information, the commodity category or attribute which is most suitable for the retrieval intention of the consumer is determined. Optionally, the target article selecting direction is based on fuzzy search technology, the input information is processed and analyzed, the key information is extracted, the related commodity category, attribute or characteristic is found, the related commodity category, attribute or characteristic is marked as the label identification of the target article selecting attribute, and a plurality of label identifications are combined and stored to obtain the target article selecting direction.
Among them, fuzzy search is an information retrieval technology for searching information related to a user query or a keyword in large-scale data, and uses various algorithms and technologies to calculate similarity between texts, including string matching, edit distance, word vector, etc. And finding similar results, ranking according to the similarity, and displaying relevant search results to the user. The fuzzy search aims at finding a result similar to or related to the input of the user, so that the search range is enlarged, and the hit rate of the search is improved.
Optionally, the tag identification of the target item direction has a target item attribute, which can help consumers to find interesting commodities more quickly, and also provides a more intuitive way for the platform to display commodity categories. By way of example, these tag identifications may be presented on a merchandise listing page, search results page, or the like, for easier purchase by consumers.
According to the embodiment, based on similarity analysis of fuzzy search, target input information of a consumer is converted into more specific tag identification with target option attribute, so that the direction of options is guided, more accurate commodity recommendation is provided, and shopping experience is improved. Meanwhile, the shopping requirements and preferences of consumers are better understood by the electronic commerce platform, and goods and services which are more expected are provided for the electronic commerce platform.
S300: the historical commodity purchasing records of the target cross-border consumers are interacted, and screening is conducted on the historical commodity purchasing records based on the target option attributes to obtain target commodity purchasing records;
in a possible embodiment, the historical commodity purchase record can be obtained through a consumption platform server, a big data commodity evaluation search and the like.
First, a history of commodity purchase records thereof is acquired. These historical purchase records include information on items purchased by consumers in the past, time of purchase, number of purchases, etc. These historical merchandise purchase records are then screened based on the previously determined target item attributes. The screening process aims to identify purchasing behavior associated with the target option attributes from the historical purchasing record. Illustratively, the target item attribute is "kitchen ware", and the commodity record related to the kitchen ware purchased by the consumer in the past is screened out. Thus better understanding consumer interests and purchasing preferences and providing valuable information for subsequent merchandise recommendation and option orientation determinations.
S400: obtaining first dimension screening characteristics, wherein the first dimension screening characteristics are determined by price characteristic fluctuation analysis on the target commodity purchase records;
The first dimension screening feature analyzes the price acceptance of the consumer to the target commodity from the time dimension, and fits a time-price curve to predict, so that the consumer acceptance interval of the current time is obtained. The fluctuation feature refers to consumer's consumption ability and the trend of change in consumption ability.
It should be appreciated that as consumer individual life stages, economic conditions, purchase preferences, etc. change, the price acceptance interval for the same commodity is exemplified by one person purchasing a cell phone while not engaged in labor to obtain the revenue, who may pay more attention to the price availability, and may have a higher purchase budget after work, willing to accept a higher price cell phone. When the price characteristic fluctuation analysis is carried out, the sensitivity and the acceptance degree of the consumer to the price in different time periods are considered, and a relatively accepted price interval, namely a first dimension screening characteristic, is calculated.
Further, a first dimension screening feature is obtained, wherein the first dimension screening feature is determined by carrying out price feature fluctuation analysis on the target commodity purchase record, and step S400 includes:
s410: the target commodity purchase record is provided with a commodity purchase time identifier;
S420: presetting a time division threshold, and dividing the target commodity purchase record into a plurality of target commodity subsets based on the time division threshold and the commodity purchase time identifier;
s430: extracting price characteristics of the target commodity subsets to obtain a plurality of target price characteristic subsets;
s440: carrying out price fluctuation feature analysis on the plurality of target price feature subsets to obtain a plurality of target price fluctuation intervals;
s450: fitting the multiple target price fluctuation intervals to obtain price fluctuation adjustment parameters;
s460: and calculating and obtaining a real-time price fluctuation interval based on the price fluctuation adjustment parameter, and taking the real-time price fluctuation interval as the first dimension screening characteristic.
The time span of the target commodity purchase record is N years, the time division threshold is set to be one year, and the target commodity purchase record is divided into K subsets according to the time division threshold; wherein the K subsets correspond to tag identifications of the K target option attributes. Each subset includes a plurality of target commodity purchase records M, 1.ltoreq.M.
Extracting price characteristics of a plurality of target commodity subsets, wherein the price characteristics comprise: average consumption price: the average price of the goods purchased by the consumer in a specific time period is calculated, and the average consumption level of the consumer in the time period can be known. Price fluctuation: and analyzing the price fluctuation condition of the same commodity in different time periods to know whether the consumer is sensitive to price fluctuation. Price fluctuations may greatly affect consumer purchasing decisions. Price preference interval: and analyzing preference intervals of consumers for commodity prices in different time periods according to the historical purchase data. For example, a consumer may prefer to purchase items with a price within a certain interval for a certain period of time. Price elasticity: the degree of influence of price change on purchasing behavior is analyzed by observing the change in the amount purchased by the consumer. The price elasticity is large indicating that consumers are sensitive to price changes.
The price fluctuation feature analysis refers to analysis on the time dimension of a plurality of target commodity subsets, and optionally, analysis on the same-ratio change trend and the ring-ratio change trend of the prices of the plurality of target commodity subsets is performed to obtain a plurality of target price fluctuation intervals. The multiple target price fluctuation intervals have periodic variation characteristics which reflect the price of the target commodity along with time. Wherein the period of variation is consistent with the time division threshold.
Further, fitting is performed on a plurality of target price fluctuation intervals to obtain price fluctuation adjustment parameters, wherein fitting is a mathematical and statistical method for fitting a set of data points into a curve so as to better describe the trend and relationship of the data. Fitting is carried out on a plurality of target price fluctuation intervals to obtain a smoother price fluctuation trend curve, so that the price fluctuation condition is known more accurately, and the real-time price fluctuation is predicted. Illustratively, first from the perspective of the trend of ring ratio variationFitting the price fluctuation curve of each time point within the same time division threshold, such as the change along with month or season, to obtain a first price fluctuation adjustment parameter B 1 The method comprises the steps of carrying out a first treatment on the surface of the Then from the same ratio, fitting the variation of the same month or season consumption interval with the years among different threshold divisions to obtain a second price fluctuation adjustment parameter B 2 . Price fluctuation adjustment parameter b=b 1 ×B 2 。
Alternatively, the price fluctuation adjusting parameter may be represented as two adjacent curved surfaces in a three-dimensional space, where, for example, the upper curved surface represents an upper limit of a real-time price fluctuation interval, the lower curved surface represents a lower limit of the real-time price fluctuation interval, the X-axis is the month of the independent variable, and the Y-week is the year of the independent variable. Through the price fluctuation adjustment parameters and the combination of the target consumer retrieval time points, accurate prediction of a real-time price fluctuation interval is realized, more valuable information is provided for matching and recommending commodities, and accuracy and fitness of cross-border commodity sales recommendation are improved.
S500: obtaining second dimension screening characteristics, wherein the second dimension screening characteristics are determined by carrying out design characteristic analysis on the target commodity purchasing record;
the second dimension screening feature analyzes the practicability degree preference of the consumer to the target commodity from the appearance image, and the commodity practicability preference threshold of the target consumer is obtained through contour comparison. The fluctuation feature refers to consumer's consumption ability and the trend of change in consumption ability.
It will be appreciated that there are aesthetic differences in the physical requirements of consumers for products having the same functionality, and that consumer preferences of the user are better understood in view of the different consumer preferences regarding practicality and aesthetic needs, for more accurate product recommendation and selection analysis.
Further, a second dimension screening feature is obtained, and step S500 includes:
s510: obtaining a sample commodity image set according to the target option attribute, wherein the sample commodity image set comprises K sample commodity standard images;
s520: acquiring contour features based on the sample commodity image set to obtain a standard commodity contour feature set, wherein the standard commodity contour feature set comprises K standard commodity contour features;
s530: acquiring contour features of the target commodity purchase record to obtain a historical commodity contour feature set;
s540: traversing the standard commodity contour feature set based on the historical commodity contour feature set to obtain a contour design influence index and a deviation contour feature set;
s550: presetting a profile influence retention threshold, and judging whether the profile design influence index meets the profile influence retention threshold;
s560: if the profile design impact index meets the profile impact retention threshold, generating commodity profile trend features based on the deviated profile feature set;
s570: the commodity contour trend feature is added to the second dimension screening feature.
The sample commodity image refers to an image of a commodity with the attribute of the target selected object adapted, and is preferably extracted through a background database of the consumption platform.
Optionally, the standard commodity contour feature comprises a contour feature parameter of the commodity and a contour design influence index corresponding to the contour feature parameter. The outline design influence index is used for representing the deviation degree of commodity practical attributes and aesthetic attributes. Illustratively, the standard commodity profile design impact index ranges from (1-10), where 1 is fully functional and 10 is fully aesthetic oriented. The profile characteristic parameters are used for performing traversal matching between the historical commodity profile characteristic set and the standard commodity profile characteristic set.
The deviation profile feature set refers to a set of historical commodity profile features, wherein the set of historical commodity profile features cannot be matched with the corresponding standard commodity profile features in the standard commodity profile feature set, and the historical commodity profile features of the profile design influence index are obtained. The confidence of the target user profile design impact index is embodied.
Optionally, the profile design impact index corresponds to an impact confidence level that is numerically equal to a ratio of the number of features in the set of off-profile features to the number of features in the set of historical merchandise profile features. Preferably, if the influence confidence corresponding to the contour design influence index is greater than the contour influence retention threshold, generating a commodity contour trend feature based on the deviation contour feature set, wherein the commodity contour trend feature refers to the contour design influence index of the historical commodity contour features except for the deviation contour feature set in the historical commodity contour feature set. And storing the commodity contour tendency characteristic marks to obtain second dimension screening characteristics.
Further, the method further comprises:
s580: performing RGB value characteristic collection on the target commodity purchase record to obtain a historical commodity RGB value characteristic set;
s590: determining commodity color trend characteristics based on the historical commodity RGB value characteristic set analysis;
s5100: the merchandise color propensity feature is added to the second dimension screening feature.
The RGB value characteristic refers to the color characteristic of the target commodity and is used for expressing the color tendency of the consumer to the commodity. Alternatively, first, through image acquisition, RGB value features of the historical merchandise are collected, and these features describe the numerical values of the merchandise in the three color channels of red, green and blue. These RGB value characteristics are then analyzed to determine the color propensity of the commodity, i.e., the preference or trend of the commodity in color.
Optionally, based on the HOG method, combining with HIS conversion, analyzing the historical commodity RGB value feature set to determine commodity color trend features. HOG (Histogram of Oriented Gradients) is a method for extracting image features, and is mainly used for target detection and recognition tasks. HOG features can capture edge and texture information of objects in an image, thereby describing appearance features of the objects. The HOG method includes: dividing image blocks: first, the image is divided into small partial blocks. Each partial block typically contains a plurality of pixels; gradient calculation: for each local block, the gradient of its internal pixels is calculated. The gradient represents the change condition of pixel values, and can capture the edge and texture information; direction histogram: the gradient direction of each pixel is divided into different angle intervals to form a direction histogram. The magnitude of the gradient is counted in each interval. Block normalization: for adjacent local blocks, their direction histograms are normalized. This helps to reduce the effects of illumination variations. Feature vector: the normalized local block direction histograms are concatenated to form a vector. This vector is the HOG feature and represents the texture and edge features of the image.
In addition, the HIS space is a color space, consisting of the following three components: brightness (L): the brightness of the image, that is, the brightness information of the image. In the HIS color space, the luminance component is a gray-scale image; saturation (S): indicating the vividness or purity of the color. When the saturation is lower, the color will be closer to gray; hue (H): indicating the type or base hue of the color. The hue typically ranges from 0 to 360 degrees, corresponding to different colors. The HIS conversion refers to a process of converting an image from an RGB color space to an HIS color space to change color information of the image and to better understand color characteristics of the image. Wherein, the brightness (L) in the HIS space is obtained as follows:
hue (H) in HIS space, which is obtained by the following formula:
saturation in HIS space (S) its acquisition formula is as follows:
further, firstly, performing HIS conversion on a historical commodity RGB value feature set, and converting RGB values into brightness (L), saturation (S) and hue (H); then dividing the image blocks of the historical commodity; then, based on the gradient calculation principle: for each local block, calculating the gradient direction of the internal pixel as the direction corresponding to theta in the tone (H), wherein the gradient is the arithmetic average value of the saturation (S) and the brightness (L); then, for a plurality of adjacent local blocks, normalizing their direction histograms; and finally, connecting the normalized local block direction histograms to form a vector, wherein the vector is the HOG characteristic, and collecting a plurality of HOG characteristics to obtain the commodity color tendency characteristic.
S600: obtaining an alternative cross-border commodity set by adopting the target option direction matching;
optionally, obtaining an alternative cross-border commodity set based on target option direction matching; firstly, obtaining a standard commodity library; and then, carrying out label matching on the commodities in the standard commodity library according to the label identifications of the plurality of target commodity attributes contained in the target commodity direction, extracting the matched standard commodity library commodities, and obtaining the alternative cross-border commodity set. The target object selection direction matching is adopted to obtain the alternative cross-border commodity set, so that the range of subsequent feature matching is reduced, the calculation power consumption is reduced, and meanwhile, the cross-border sales cost is reduced to a certain extent.
The standard commodity library is stored in the target consumption platform server. The standard commodity library contains various information such as names, descriptions, prices, stock amounts, pictures, attributes, categories, labels, and the like of commodities. The commodity library comprises commodities from different regions or areas so as to meet the requirements of consumers in different regions or areas. These goods have different characteristics, specifications, prices, etc.
S700: traversing the alternative cross-border commodity set by taking the first dimension screening feature and the second dimension screening feature as constraints to obtain a cross-border commodity set;
Screening the alternative cross-border commodity set based on the first dimension screening feature and the second dimension screening feature, extracting the cross-border commodity set, and exemplarily, firstly, screening the alternative cross-border commodity set by using the first dimension screening feature to remove commodities which do not accord with the first dimension feature; then, using the second dimension screening features, taking the features as conditions, and further screening the commodity set which is subjected to the first dimension screening to remove commodities which do not accord with the second dimension features; and finally, integrating the screening results of the alternative cross-border commodity sets screened by the first dimension and the second dimension to obtain a final cross-border commodity set, wherein the realization commodities meet the technical effects of user requirements and preferences in multiple dimensions such as price, appearance, color and the like.
S800: presetting a cross-border commodity display rule, and generating a browsing interface of the cross-border commodity set based on the cross-border commodity display rule.
The commodity display rules comprise a display commodity arrangement sequence, a display mode, screening conditions and the like of commodities. Through presetting the cross-border commodity display rules, the cross-border commodity collection obtained by the user can be better presented, and an intuitive browsing interface is generated for the user, so that proper commodities can be browsed and selected more conveniently, and user experience and shopping efficiency are improved.
Further, preset cross-border merchandise display rules, and generate a browsing interface of the cross-border merchandise set based on the cross-border merchandise display rules, before step S800 further includes:
s801: acquiring target location information, wherein the target location information is obtained by extracting receiving address information of the historical commodity purchase record;
s802: pre-constructing a commodity cross-border standard information base, and interactively matching the commodity cross-border standard information base based on the target location information and the target option attribute to obtain option attribute specification constraint;
s803: generating a third dimension screening feature according to the constraint of the attribute specification of the selected product;
s804: traversing the cross-border commodity set by taking the third dimension screening feature as a constraint to obtain a second cross-border commodity set;
s805: and generating a browsing interface of the second cross-border commodity set based on the cross-border commodity display rule.
Optionally, the commodity cross-border standard information base is pre-built on the cloud server, wherein the commodity cross-border standard information base contains information of various commodities, including attributes, specifications, production places and the like. And interacting with the library based on the information of the target location and the target option attribute of the user to obtain commodity information of specific regions and attributes. The target commodities with the same target option attribute have different adaptability to different regions, and for the electric appliance target commodities, the power supply voltage, frequency and interface standards of different regions are different; in addition, the restrictions on the target commodities in different regions are different, and for example, the customs clearance records of the target commodities such as vehicles, medicines and the like are different, so that the situations such as product withholding and the like can occur.
The third dimension screening feature is used for further finely screening commodities, so that conditions of incapability of using target commodities, target commodity withholding and the like caused by different constraint of the attribute specifications of the selected commodities are reduced, and the technical effect of cross-border sales cost reduction is achieved.
Further, the step S805 further includes:
s8051: the historical commodity browsing records of the target cross-border consumers are interacted;
s8052: splitting the historical commodity browsing records according to the target commodity purchasing records to obtain a plurality of commodity browsing record subsets;
s8053: carrying out commodity comparison trend analysis based on the commodity browsing record subsets to obtain the cross-border commodity display rule;
s8054: serializing the second cross-border commodity set based on the cross-border commodity display rule to obtain a cross-border commodity display sequence;
s8055: and interacting a target display matrix, and carrying out the cross-border commodity display sequence arrangement based on the target display matrix to generate a target browsing interface.
The commodity comparison trend analysis refers to analysis of comparison habits of target consumers during purchase, and comprises comparison of sales volume of multiple stores, comparison of price of multiple stores, priority of scoring of multiple stores and the like.
Optionally, the second cross-border commodity set is ordered based on cross-border commodity display rule serialization, and the ordering algorithm includes: bubble sorting, rapid sorting and the like, wherein the cross-border commodity display rule is exemplified by the best multi-store scoring, the highest multi-store sales and the best multi-store price comprehensive display, and if the multi-store scoring and sorting result is a 1 、a 2 、…、a i The method comprises the steps of carrying out a first treatment on the surface of the The sorting result of the sales of multiple stores is b 1 、b 2 、…、b i The method comprises the steps of carrying out a first treatment on the surface of the The optimal sorting result of the multi-store price is c 1 、c 2 、…、c i Then the order is according to a 1 、b 1 、c 1 、a 2 、b 2 、c 2 、…、a i 、b i 、c i Is used for generating a cross-border commodity display sequence.
The target display matrix is used for storing commodity information, characteristics, ordering indexes and display rules. Each row represents a commodity and each column represents a feature or rule. Is a collection of data that is used to help generate the proper merchandise arrangement.
Through the cross-border commodity display rule, on the premise of ensuring shopping experience and autonomy selection of users, the technical effects of finding out suitable cross-border commodities more quickly and improving purchase conversion rate and user satisfaction are achieved.
Further, before the generating of the browsing interface of the second cross-border merchandise set based on the cross-border merchandise display rule, step S805 further includes
S805-1: obtaining a delivery address set, wherein the delivery address set is determined according to a storage warehouse of the second cross-border commodity set;
S805-2: generating a cross-border transportation cost set, wherein the cross-border transportation cost set performs cross-border path planning determination based on the target location information and the delivery address set, and the cross-border transportation cost set comprises a transportation time cost set and a transportation price cost set;
s805-3: presetting a cost weight assignment, wherein the cost weight assignment comprises a first weight of transportation time cost and a second weight of transportation price cost;
s805-4: calculating to obtain a cross-border comprehensive cost information set according to the cost weight assignment and the cross-border transportation cost set;
s805-5: presetting transportation cost constraint, traversing and screening the cross-border comprehensive cost information set based on the transportation cost constraint, and obtaining a third cross-border commodity set;
s805-6: and generating a browsing interface based on the third cross-border commodity set.
Where the transit time cost refers to the time cost required for the goods to be shipped from the seller to the buyer. Cross-border transportation involves international logistics and cross-country border circulation, and therefore the transit time of goods can be affected by a variety of factors including transportation, export and ingress inspection, etc. The shipping time cost set considers the time from order confirmation to actual receipt to meet the consumer's demand for timely delivery of goods. Preferably, the transit time costs are obtained based on historical cross-border transit logistics records.
The transport price cost refers to freight cost or transport price in the commodity transport process. The cross-border transportation involves transportation fees for different regions and areas. Thus, the final selling price of the commodity may be affected by these shipping prices. The shipping price costs comprehensively take into account all of the fees paid to ship the goods from the seller to the buyer for receipt, including shipping fees, tariffs, shipping losses, and the like.
Optionally, the first weight of the transportation time cost and the second weight of the transportation price cost are preset by an expert system, the influence of the transportation time cost and the transportation price cost on the commodity price is standardized by the expert system, and the cost weight assignment is determined according to the influence ratio.
Optionally, the preset shipping cost constraints include a shipping time cost constraint and a shipping price cost constraint. Firstly, screening transportation costs of a plurality of cross-border paths according to transportation time cost constraint, and eliminating the cross-border paths which do not meet the transportation time cost constraint; and then, screening the transportation cost of the plurality of cross-border paths according to the transportation price cost constraint, and eliminating the cross-border paths which do not meet the transportation price cost constraint. And finally, setting the target commodity corresponding to the cross-border path reserved in the cross-border comprehensive cost information set as a third cross-border commodity set.
The cross-border transportation cost set integrates two key factors of time and price, and helps sellers and consumers evaluate commodity transportation costs under different cross-border logistics schemes. The technical effects of making reasonable selling price and transportation plan and reducing cross-border selling cost are achieved.
In summary, the multi-dimensional cross-border commodity matching method provided by the application has the following technical effects:
the application selects the image acquisition equipment based on the field environment; then, determining an image acquisition scheme according to the type of the selected image acquisition equipment, and acquiring an image of a target vehicle to obtain a first acquired image; then, performing image processing on the first acquired image to obtain a first target image, wherein the image processing comprises graying, image noise reduction and image enhancement; then, detecting the first target image based on an edge detection algorithm to obtain an edge point set; then, based on the edge point set, performing damage feature detection on the first target image to obtain a first feature set; and finally, combining the standard damage feature set, carrying out damage detection on the first feature set feature, and obtaining a first damage detection result. By using the image processing technology, the image is enhanced, noise is reduced, and the cross-border commodity sales recommendation method has the technical effects of high scientificity, adaptation degree of commodity recommendation and user requirements is improved, and cross-border sales cost is reduced.
Example two
Based on the same concept as the multi-dimensional cross-border commodity matching method in the embodiment, as shown in fig. 3, the application further provides a multi-dimensional cross-border commodity matching system, which comprises:
the consumption demand acquisition module 11 is used for calling target input information of target cross-border consumers;
a search direction positioning module 12, configured to determine a target option direction, where the target option direction is determined by performing a similarity analysis on the target input information, and the target option direction has a tag identifier of a target option attribute;
the historical record interaction module 13 is used for interacting historical commodity purchase records of the target cross-border consumer, screening the historical commodity purchase records based on the target option attribute and obtaining target commodity purchase records;
a price dimension screening module 14 for obtaining a first dimension screening feature determined by price feature fluctuation analysis of the target commodity purchase record;
the feature dimension screening module 15 is configured to obtain a second dimension screening feature, where the second dimension screening feature is determined by performing design feature analysis on the target commodity purchase record;
A primary screening candidate module 16, configured to obtain a candidate cross-border commodity set by using the target option direction matching;
the multidimensional matching module 17 is configured to traverse the alternative cross-border commodity set with the first dimension screening feature and the second dimension screening feature as constraints, so as to obtain a cross-border commodity set;
the display module 18 is configured to preset cross-border merchandise display rules, and perform browsing interface generation of the cross-border merchandise set based on the cross-border merchandise display rules.
Further, the price dimension screening module 14 further includes:
the time dividing unit is used for presetting a time dividing threshold value and dividing the target commodity purchasing record into a plurality of target commodity subsets based on the time dividing threshold value and the commodity purchasing time mark;
the price characteristic extraction unit is used for extracting price characteristics of the plurality of target commodity subsets to obtain a plurality of target price characteristic subsets;
and the fluctuation interval acquisition unit is used for carrying out price fluctuation characteristic analysis on the plurality of target price characteristic subsets to obtain a plurality of target price fluctuation intervals.
Further, the feature dimension filtering module 15 further includes:
the sample commodity image acquisition unit is used for acquiring a sample commodity image set according to the target option attribute, wherein the sample commodity image set comprises K sample commodity standard images;
The contour feature acquisition and extraction unit is used for acquiring contour features based on the sample commodity image set to obtain a standard commodity contour feature set, wherein the standard commodity contour feature set comprises K standard commodity contour features;
the contour feature analysis unit is used for traversing the standard commodity contour feature set based on the historical commodity contour feature set to obtain a contour design influence index and a deviation contour feature set;
it should be understood that the embodiments mentioned in this specification focus on differences from other embodiments, and the specific embodiment in the first embodiment is equally applicable to a multi-dimensional cross-border commodity matching system described in the second embodiment, which is not further developed herein for brevity of description.
It is to be understood that both the foregoing description and the embodiments of the present application enable one skilled in the art to utilize the present application. While the application is not limited to the embodiments described above, obvious modifications and variations of the embodiments described herein are possible and are within the principles of the application.
Claims (8)
1. A multi-dimensional cross-border commodity matching method, the method comprising:
Invoking target input information of target cross-border consumers;
determining a target option direction, wherein the target option direction is determined by carrying out similarity analysis on the target input information, and the target option direction is provided with a label mark of a target option attribute;
the historical commodity purchasing records of the target cross-border consumers are interacted, and screening is conducted on the historical commodity purchasing records based on the target option attributes to obtain target commodity purchasing records;
obtaining first dimension screening characteristics, wherein the first dimension screening characteristics are determined by price characteristic fluctuation analysis on the target commodity purchase records;
obtaining second dimension screening characteristics, wherein the second dimension screening characteristics are determined by carrying out design characteristic analysis on the target commodity purchasing record;
obtaining an alternative cross-border commodity set by adopting the target option direction matching;
traversing the alternative cross-border commodity set by taking the first dimension screening feature and the second dimension screening feature as constraints to obtain a cross-border commodity set;
presetting a cross-border commodity display rule, and generating a browsing interface of the cross-border commodity set based on the cross-border commodity display rule.
2. The method of claim 1, wherein a first dimension screening feature is obtained, the first dimension screening feature determined by price characteristic fluctuation analysis of the target commodity purchase record, the method further comprising:
the target commodity purchase record is provided with a commodity purchase time identifier;
presetting a time division threshold, and dividing the target commodity purchase record into a plurality of target commodity subsets based on the time division threshold and the commodity purchase time identifier;
extracting price characteristics of the target commodity subsets to obtain a plurality of target price characteristic subsets;
carrying out price fluctuation feature analysis on the plurality of target price feature subsets to obtain a plurality of target price fluctuation intervals;
fitting the multiple target price fluctuation intervals to obtain price fluctuation adjustment parameters;
and calculating and obtaining a real-time price fluctuation interval based on the price fluctuation adjustment parameter, and taking the real-time price fluctuation interval as the first dimension screening characteristic.
3. The method of claim 1, wherein a second dimension screening feature is obtained, the second dimension screening feature determined by design feature analysis of the target commodity purchase record, the method further comprising:
Obtaining a sample commodity image set according to the target option attribute, wherein the sample commodity image set comprises K sample commodity standard images;
acquiring contour features based on the sample commodity image set to obtain a standard commodity contour feature set, wherein the standard commodity contour feature set comprises K standard commodity contour features;
acquiring contour features of the target commodity purchase record to obtain a historical commodity contour feature set;
traversing the standard commodity contour feature set based on the historical commodity contour feature set to obtain a contour design influence index and a deviation contour feature set;
presetting a profile influence retention threshold, and judging whether the profile design influence index meets the profile influence retention threshold;
if the profile design impact index meets the profile impact retention threshold, generating commodity profile trend features based on the deviated profile feature set;
the commodity contour trend feature is added to the second dimension screening feature.
4. A method as claimed in claim 3, wherein the method further comprises:
performing RGB value characteristic collection on the target commodity purchase record to obtain a historical commodity RGB value characteristic set;
Determining commodity color trend characteristics based on the historical commodity RGB value characteristic set analysis;
the merchandise color propensity feature is added to the second dimension screening feature.
5. The method of claim 1, wherein cross-border merchandise display rules are preset and browsing interface generation of the cross-border merchandise collection is performed based on the cross-border merchandise display rules, before the method further comprises:
acquiring target location information, wherein the target location information is obtained by extracting receiving address information of the historical commodity purchase record;
pre-constructing a commodity cross-border standard information base, and interactively matching the commodity cross-border standard information base based on the target location information and the target option attribute to obtain option attribute specification constraint;
generating a third dimension screening feature according to the constraint of the attribute specification of the selected product;
traversing the cross-border commodity set by taking the third dimension screening feature as a constraint to obtain a second cross-border commodity set;
and generating a browsing interface of the second cross-border commodity set based on the cross-border commodity display rule.
6. The method of claim 5, wherein the browsing interface generation of the second cross-border commodity set is based on the cross-border commodity display rules, the method further comprising:
The historical commodity browsing records of the target cross-border consumers are interacted;
splitting the historical commodity browsing records according to the target commodity purchasing records to obtain a plurality of commodity browsing record subsets;
carrying out commodity comparison trend analysis based on the commodity browsing record subsets to obtain the cross-border commodity display rule;
serializing the second cross-border commodity set based on the cross-border commodity display rule to obtain a cross-border commodity display sequence;
and interacting a target display matrix, and carrying out the cross-border commodity display sequence arrangement based on the target display matrix to generate a target browsing interface.
7. The method of claim 5, wherein the browsing interface generation of the second cross-border commodity set is based on the cross-border commodity display rules, prior to the method further comprising:
obtaining a delivery address set, wherein the delivery address set is determined according to a storage warehouse of the second cross-border commodity set;
generating a cross-border transportation cost set, wherein the cross-border transportation cost set performs cross-border path planning determination based on the target location information and the delivery address set, and the cross-border transportation cost set comprises a transportation time cost set and a transportation price cost set;
Presetting a cost weight assignment, wherein the cost weight assignment comprises a first weight of transportation time cost and a second weight of transportation price cost;
calculating to obtain a cross-border comprehensive cost information set according to the cost weight assignment and the cross-border transportation cost set;
presetting transportation cost constraint, traversing and screening the cross-border comprehensive cost information set based on the transportation cost constraint, and obtaining a third cross-border commodity set;
and generating a browsing interface based on the third cross-border commodity set.
8. A multi-dimensional cross-border commodity matching system, the system comprising:
the consumption demand acquisition unit is used for calling target input information of target cross-border consumers;
the search direction positioning module is used for determining a target selection direction, wherein the target selection direction is determined by carrying out similarity analysis on the target input information, and the target selection direction is provided with a label mark of a target selection attribute;
the historical record interaction module is used for interacting historical commodity purchase records of the target cross-border consumers, screening the historical commodity purchase records based on the target option attributes and obtaining target commodity purchase records;
The price dimension screening module is used for obtaining first dimension screening characteristics, and the first dimension screening characteristics are determined by price characteristic fluctuation analysis on the target commodity purchase records;
the feature dimension screening module is used for obtaining second dimension screening features, and the second dimension screening features are determined by carrying out design feature analysis on the target commodity purchasing record;
the primary screening alternative module is used for obtaining an alternative cross-border commodity set by adopting the target item direction matching;
the multi-dimensional matching module is used for traversing the alternative cross-border commodity set by taking the first dimension screening feature and the second dimension screening feature as constraints to obtain a cross-border commodity set;
the display module is used for presetting cross-border commodity display rules and generating a browsing interface of the cross-border commodity set based on the cross-border commodity display rules.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311038416.8A CN117035934A (en) | 2023-08-17 | 2023-08-17 | Multi-dimensional cross-border commodity matching method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311038416.8A CN117035934A (en) | 2023-08-17 | 2023-08-17 | Multi-dimensional cross-border commodity matching method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117035934A true CN117035934A (en) | 2023-11-10 |
Family
ID=88635102
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311038416.8A Pending CN117035934A (en) | 2023-08-17 | 2023-08-17 | Multi-dimensional cross-border commodity matching method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117035934A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090030829A1 (en) * | 2007-07-25 | 2009-01-29 | Neosaej Corp. | Seller automated engine architecture and methodology for optimized pricing strategies in automated real-time iterative reverse auctions over the internet and the like for the purchase and sale of goods and services |
| US20130080289A1 (en) * | 2011-09-28 | 2013-03-28 | Rupessh Ranen Roy | Retail shopping |
| JP2019215717A (en) * | 2018-06-13 | 2019-12-19 | 潤一 野元 | Matching system, matching method, and computer program |
| CN114782142A (en) * | 2022-05-09 | 2022-07-22 | 广州欢聚时代信息科技有限公司 | Commodity information matching method and device, equipment, medium and product thereof |
| CN115203514A (en) * | 2022-07-26 | 2022-10-18 | 广州欢聚时代信息科技有限公司 | Commodity query redirection method and device, equipment, medium and product thereof |
| CN115935068A (en) * | 2022-12-12 | 2023-04-07 | 杭州洋驼网络科技有限公司 | Commodity recommendation method and device for Internet platform |
| CN116342226A (en) * | 2023-04-12 | 2023-06-27 | 郝荣恺 | Commodity recommendation method and system based on big data |
| CN116595390A (en) * | 2023-04-24 | 2023-08-15 | 阿里巴巴（中国）有限公司 | Commodity information processing method and electronic equipment |
-
2023
- 2023-08-17 CN CN202311038416.8A patent/CN117035934A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090030829A1 (en) * | 2007-07-25 | 2009-01-29 | Neosaej Corp. | Seller automated engine architecture and methodology for optimized pricing strategies in automated real-time iterative reverse auctions over the internet and the like for the purchase and sale of goods and services |
| US20130080289A1 (en) * | 2011-09-28 | 2013-03-28 | Rupessh Ranen Roy | Retail shopping |
| JP2019215717A (en) * | 2018-06-13 | 2019-12-19 | 潤一 野元 | Matching system, matching method, and computer program |
| CN114782142A (en) * | 2022-05-09 | 2022-07-22 | 广州欢聚时代信息科技有限公司 | Commodity information matching method and device, equipment, medium and product thereof |
| CN115203514A (en) * | 2022-07-26 | 2022-10-18 | 广州欢聚时代信息科技有限公司 | Commodity query redirection method and device, equipment, medium and product thereof |
| CN115935068A (en) * | 2022-12-12 | 2023-04-07 | 杭州洋驼网络科技有限公司 | Commodity recommendation method and device for Internet platform |
| CN116342226A (en) * | 2023-04-12 | 2023-06-27 | 郝荣恺 | Commodity recommendation method and system based on big data |
| CN116595390A (en) * | 2023-04-24 | 2023-08-15 | 阿里巴巴（中国）有限公司 | Commodity information processing method and electronic equipment |
Non-Patent Citations (1)
| Title |
|---|
| 王茜;喻继军;: "基于价格消费习惯的商品多样性推荐", 系统工程, no. 03 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11238617B2 (en) | Normalization of color from a digital image into a universal digital color system for specification and matching | |
| CN108629665B (en) | Personalized commodity recommendation method and system | |
| US9436704B2 (en) | System for normalizing, codifying and categorizing color-based product and data based on a universal digital color language | |
| US9607404B2 (en) | System for normalizing, codifying and categorizing color-based product and data based on a universal digital color system | |
| US9087357B2 (en) | System for normalizing, codifying and categorizing color-based product and data based on a universal digital color language | |
| US8407104B2 (en) | Catalog based price search | |
| EP2765534A2 (en) | System and method for normalization and codificaton of colors for dynamic analysis | |
| Wang et al. | A strategy-oriented operation module for recommender systems in E-commerce | |
| US20090198593A1 (en) | Method and apparatus for comparing entities | |
| US20130249934A1 (en) | Color-based identification, searching and matching enhancement of supply chain and inventory management systems | |
| CN111667337A (en) | Commodity evaluation ordering method and system | |
| US10832305B1 (en) | System and method for image processing and searching for classification in a product database | |
| CN114997956B (en) | Mother and infant product intelligent recommendation system based on big data | |
| Gangopadhyay | An image-based system for electronic retailing | |
| Chou et al. | The RFM Model Analysis for VIP Customer: A case study of golf clothing brand | |
| CN117035934A (en) | Multi-dimensional cross-border commodity matching method and system | |
| CN115131108A (en) | E-commerce commodity screening system | |
| US20220067976A1 (en) | Normalized nesting cube and mapping system for machine learning to color coordinate products, patterns and objects on a homogenized ecommerce platform | |
| JPH02170269A (en) | Commodity information analyzing method | |
| Han et al. | Price suggestion for online second-hand items | |
| Aryafar et al. | Exploring user behaviour on etsy through dominant colors | |
| Zhao et al. | Research on the impact of product attributes on online reputation of goods based on the EML | |
| Malik et al. | Applying data mining for clustering shoppers based on store loyalty | |
| Dang et al. | E-commerce Website based on Graphics and Image Processing Technology (IPT) | |
| CN117196700A (en) | Product release optimization method and system of cross-border e-commerce platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |