CN109377328B - Method and device for recommending geographical positions of merchant stores - Google Patents
Method and device for recommending geographical positions of merchant stores Download PDFInfo
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- CN109377328B CN109377328B CN201811554976.8A CN201811554976A CN109377328B CN 109377328 B CN109377328 B CN 109377328B CN 201811554976 A CN201811554976 A CN 201811554976A CN 109377328 B CN109377328 B CN 109377328B
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Abstract
The invention discloses a method and a device for recommending geographical positions of merchant stores. The method comprises the following steps: acquiring first user group characteristics of a historical consumption user group of a merchant, and extracting seed characteristics and characteristic values of the seed characteristics from the first user group characteristics; analyzing the second user group characteristics of the user groups in each target geographic area, and calculating the characteristic values of the second user group characteristics corresponding to each target geographic area; matching the seed characteristics with second user group characteristics corresponding to each target geographic area, and calculating the recommendation score of each target geographic area by using the characteristic values of the seed characteristics and the second user group characteristics which are matched with each other; and recommending the geographical position of the store to the merchant according to the recommended value of each target geographical area, wherein the recommended value reflects the matching condition of the user group in each target geographical area and the historical consumption user group of the merchant, the recommended geographical position is more objective and scientific, and the flow and the profit of the merchant can be ensured.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for recommending geographical positions of merchant stores.
Background
In the off-line business form, shop location is a very important action for a merchant, and the positioning of shops, the types of commodities and the operation effect can be influenced, the past shop location is basically based on the flow of people and the peripheral business form to select the shops, an observer is dispatched to observe the flow of people, the characteristics of the peripheral people are observed by using an questionnaire, and then the shop location is selected.
In order to solve the above problems, chinese patent application with application publication No. CN 106294516a discloses a location information providing method and apparatus, which mainly utilizes a merchant to locate itself, such as keywords "white collar" and "high frequency medium amount", to determine target user characteristics, after determining a first region (where the first region includes a plurality of second regions), determines the number of second users having the target user characteristics in each second region according to the location information of the users in the first region, and determines an index value of the target user characteristics in each second region according to the number of the second users; sorting the plurality of second regions according to the index values; the second area with the top M bits of the ranking result is used as a candidate area, the candidate area is provided for the first user, the recommended geographic position is inaccurate due to inaccurate positioning of the merchant in the determination mode, the geographic position cannot be objectively and scientifically recommended, in addition, the provided candidate area only conforms to the positioning set by the merchant, and the user traffic of the merchant cannot be guaranteed at the consumption level.
Disclosure of Invention
In view of the above, the present invention has been made to provide a method and apparatus for recommending merchant store geographical locations that overcomes or at least partially solves the above-mentioned problems.
According to one aspect of the invention, a method for recommending the geographical position of a merchant store is provided, which comprises the following steps:
acquiring first user group characteristics of a historical consumption user group of a merchant, and extracting seed characteristics and characteristic values of the seed characteristics from the first user group characteristics;
analyzing the second user group characteristics of the user groups in each target geographic area, and calculating the characteristic values of the second user group characteristics corresponding to each target geographic area;
matching the seed characteristics with second user group characteristics corresponding to each target geographic area, and calculating the recommendation score of each target geographic area by using the characteristic values of the seed characteristics and the second user group characteristics which are matched with each other;
and recommending the geographical position of the store to the merchant according to the recommendation scores of the target geographical areas.
Optionally, calculating the recommendation score of each target geographic area by using the feature values of the matched seed features and the feature values of the second user group features further comprises:
and taking the feature values of the matched seed features as weights, performing weighted calculation on the feature values of the second user group features, and determining the recommendation scores of the target geographic areas.
Optionally, the extracting the seed feature from the first user group feature and the feature value of the seed feature further includes:
calculating a characteristic value of a first user group characteristic of a historical consumption user group of a merchant;
calculating a characteristic value of a first user group characteristic of a total historical consumption user group in the industry to which the merchant belongs;
and determining the seed characteristic and the characteristic value of the seed characteristic according to the characteristic value of the first user group characteristic of the historical consumption user group of the merchant and the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs.
Optionally, determining the seed feature and the feature value of the seed feature according to the feature value of the first user group feature of the historical consumption user group of the merchant and the feature value of the first user group feature of the total historical consumption user group in the industry to which the merchant belongs further includes:
calculating the ratio of the characteristic value of the first user group characteristic of the historical consumption user group of the merchant to the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs;
and if the ratio is greater than or equal to the preset threshold, determining that the first user group characteristic corresponding to the characteristic value is the seed characteristic.
Optionally, analyzing the second user group characteristics of the user groups in the respective target geographic areas further comprises:
determining a target geographic area based on the location service information;
calculating a target geographical area corresponding to each user according to the user position information;
second user group characteristics of the user groups within the respective target geographic areas are analyzed.
Optionally, recommending the geographic location of the store to the merchant according to the recommendation score of each target geographic region further comprises:
sorting each target geographical area according to the recommended scores;
and recommending the geographical position of the store to the merchant according to the sequencing result.
Optionally, the first user group characteristic or the second user group characteristic comprises: basic features, social features, interest features, and/or consumption features.
Optionally, the characteristic value comprises: the user is the ratio.
According to another aspect of the present invention, there is provided a recommendation apparatus for a geographical location of a merchant store, comprising:
the acquisition module is suitable for acquiring first user group characteristics of a historical consumption user group of a merchant;
the extraction module is suitable for extracting seed characteristics and characteristic values of the seed characteristics from the first user group characteristics;
the analysis module is suitable for analyzing the second user group characteristics of the user groups in each target geographic area;
the first calculation module is suitable for calculating the characteristic value of the second user group characteristic corresponding to each target geographic area;
the matching module is suitable for matching the seed characteristics with the second user group characteristics corresponding to each target geographic area;
the second calculation module is suitable for calculating the recommendation score of each target geographic area by using the feature values of the matched seed features and the feature values of the second user group features;
and the recommending module is suitable for recommending the geographical position of the store to the merchant according to the recommendation scores of the target geographical areas.
Optionally, the second calculation module is further adapted to: and taking the feature values of the matched seed features as weights, performing weighted calculation on the feature values of the second user group features, and determining the recommendation scores of the target geographic areas.
Optionally, the extraction module is further adapted to: calculating a characteristic value of a first user group characteristic of a historical consumption user group of a merchant;
calculating a characteristic value of a first user group characteristic of a total historical consumption user group in the industry to which the merchant belongs;
and determining the seed characteristic and the characteristic value of the seed characteristic according to the characteristic value of the first user group characteristic of the historical consumption user group of the merchant and the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs.
Optionally, the extraction module is further adapted to: calculating the ratio of the characteristic value of the first user group characteristic of the historical consumption user group of the merchant to the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs;
and if the ratio is greater than or equal to the preset threshold, determining that the first user group characteristic corresponding to the characteristic value is the seed characteristic.
Optionally, the analysis module is further adapted to: determining a target geographic area based on the location service information;
calculating a target geographical area corresponding to each user according to the user position information;
second user group characteristics of the user groups within the respective target geographic areas are analyzed.
Optionally, the recommendation module is further adapted to: sorting each target geographical area according to the recommended scores;
and recommending the geographical position of the store to the merchant according to the sequencing result.
Optionally, the first user group characteristic or the second user group characteristic comprises: basic features, social features, interest features, and/or consumption features.
Optionally, the characteristic value comprises: the user is the ratio.
According to yet another aspect of the present invention, there is provided a computing device comprising: the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the recommendation method of the geographical position of the merchant store.
According to still another aspect of the present invention, a computer storage medium is provided, where at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform operations corresponding to the method for recommending a geographical location of a merchant store as described above.
According to the scheme provided by the invention, the first user group characteristic of the historical consumption user group of the merchant is the characteristic of the actual user consumed by the merchant, the merchant is intuitively embodied in the consumption layer, the seed characteristic is extracted from the first user group characteristic, the positioning of the merchant can be highlighted, the recommendation score of each target geographical area calculated by utilizing the characteristic value of the mutually matched seed characteristic and the characteristic value of the second user group characteristic reflects the matching condition of the user group and the historical consumption user group of the merchant in each target geographical area, the higher the recommendation score is, the more the user group in the target geographical area is matched with the historical consumption user group of the merchant, the more objective and more scientific the geographical position of the store recommended according to the recommendation score is, the merchant is more accurate in site selection, the problem of site selection misjudgment is solved, as the number of potential consumption groups is large, the user traffic of the merchant can be ensured.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 shows a flow diagram of a method for recommending merchant store geographic locations, according to one embodiment of the present invention;
FIG. 2 shows a flow diagram of a method for recommending merchant store geographic locations in accordance with another embodiment of the present invention;
FIG. 3 shows a schematic structural diagram of a recommendation device for a geographical location of a merchant store according to one embodiment of the present invention;
FIG. 4 shows a schematic structural diagram of a computing device according to one embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
FIG. 1 shows a flow diagram of a method for recommending merchant store geographic locations, according to one embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:
step S100, first user group characteristics of historical consumption user groups of merchants are obtained, and seed characteristics and characteristic values of the seed characteristics are extracted from the first user group characteristics.
In this embodiment, the historical consumption user group of the merchant refers to a user group formed by users who have consumption records in the merchant, wherein each user corresponds to a respective user characteristic, so that the process of acquiring the first user group characteristic of the historical consumption user group of the merchant can be divided into acquiring the user characteristic of each historical consumption user, and then the user characteristics of all the historical consumption users of the merchant are combined together to form the first user group characteristic. The first user group characteristics are characteristics corresponding to historical consumption users of the merchant, so that the characteristics of the merchant can be objectively reflected from a consumption level, the merchant is not positioned by the merchant, and the situation that the actually consumed user group characteristics are not consistent with the positioned consumed user group characteristics is likely to occur when the merchant positions.
When the corresponding first user group feature is obtained, the seed feature and the feature value of the seed feature may be extracted from the first user group feature. The seed features are the most obvious features of the historical consumption user group of the merchant, the features of the merchant can be further shown in the consumption level, and the extracted seed features may be different for different merchants, so that the seed features are screened from the first user group features, and the characteristics of the merchant can be reflected better. The characteristic value of the seed characteristic reflects the proportion of the users with the corresponding characteristic in the historical consumption user group of the merchant. The characteristic value of the seed characteristic can reflect the importance degree of the seed characteristic.
In this embodiment, the geographical location of the merchant store is recommended based on the first user group feature of the historical consumption user group of the merchant, so that the geographical location of the recommended merchant store can be made to have objectivity, and the seed feature is extracted from the first user group feature, so that the geographical location of the recommended merchant store can be further ensured to be a gathering place with similar user groups, and the user traffic of the merchant is ensured.
Step S101, analyzing the second user group characteristics of the user groups in each target geographic area, and calculating the characteristic value of the second user group characteristics corresponding to each target geographic area.
The target geographical area is a Geo-fence divided according to location service information, and Geo-fencing (Geo-fencing) is an application of LBS, namely, a virtual fence is used to enclose a virtual geographical boundary.
Specifically, users in each target geographic area are determined, user characteristics of each user are obtained, and the user characteristics of all the users in each target geographic area are combined together to form a second user group characteristic. After the second user group characteristics of the user groups in each target geographic area are determined, the characteristic values of the second user group characteristics corresponding to each target geographic area are calculated.
And step S102, matching the seed characteristics with second user group characteristics corresponding to each target geographic area, and calculating the recommendation score of each target geographic area by using the characteristic values of the matched seed characteristics and the characteristic values of the second user group characteristics.
The process of matching the seed features with the second user group features corresponding to each target geographic area is a process of determining whether the second user group features corresponding to each target geographic area have the seed features, for example, the seed features and the second user group features may be compared one by one, and if the second user group features corresponding to the target geographic area have the corresponding seed features, it may be determined that the seed features and the second user group features are matched with each other. By matching the seed features with the second user group features corresponding to the respective target geographic areas, it can be determined whether similar users exist in the respective target geographic areas.
After the mutually matched seed features and second user group features are determined, the recommendation score of each target geographic area needs to be calculated, and specifically, the recommendation score of each target geographic area is calculated by using the feature values of the mutually matched seed features and the feature values of the second user group features. The characteristic value of the seed characteristic is introduced in the calculation of the recommendation score of each target geographic area, so that the calculated recommendation score can reflect the matching condition of the user group and the historical consumption user group of the merchant in each target geographic area, and the higher the recommendation score is, the more the user group in the target geographic area is matched with the historical consumption user group of the merchant.
And step S103, recommending the geographical position of the store to the merchant according to the recommendation scores of the target geographical areas.
After the recommendation scores of the target geographic regions are obtained through calculation, the geographic positions of the stores can be recommended to the merchant according to the recommendation scores of the target geographic regions, and after the merchant knows the recommended geographic positions of the stores, whether a new store is set at the recommended geographic position of the stores or not can be selected according to actual needs.
According to the method provided by the above embodiment of the present invention, the first user group characteristic of the historical consumption user group of the merchant is the characteristic of the user actually consuming by the merchant, which is a visual embodiment of the merchant on the consumption level, the seed characteristic is extracted from the first user group characteristic, the positioning of the merchant can be highlighted, the recommendation score of each target geographical area calculated by using the characteristic value of the mutually matched seed characteristic and the characteristic value of the second user group characteristic reflects the matching condition of the user group and the historical consumption user group of the merchant in each target geographical area, the higher the recommendation score is, the more the user group in the target geographical area is matched with the historical consumption user group of the merchant, the geographical position of the store recommended according to the recommendation score is more objective and scientific, the merchant is more accurate in site selection, the problem of site selection misjudgment is overcome, because of a large number of potential consumption groups, therefore, the user traffic of the merchant can be guaranteed.
FIG. 2 shows a flow diagram of a method for recommending merchant store geographic locations, according to another embodiment of the present invention. As shown in fig. 2, the method comprises the steps of:
step S200, acquiring a first user group characteristic of a historical consumption user group of a merchant.
The historical consumption user group of the merchant in this embodiment refers to a user group formed by users who have consumption records in the merchant.
Specifically, historical consumption order information of a merchant is obtained, so that a historical consumption user group of the merchant is determined, and after the historical consumption user group of the merchant is determined, a first user group characteristic of the historical consumption user group of the merchant is obtained.
Each user corresponds to a respective user characteristic, so that the process of acquiring the first user group characteristic of the historical consumption user group of the merchant can be divided into the process of acquiring the user characteristic of each historical consumption user, and then the user characteristics of all the historical consumption users of the merchant are combined together to form the first user group characteristic. The first user group characteristics are characteristics corresponding to historical consumption users of the merchant, so that the characteristics of the merchant can be objectively reflected from a consumption level, the merchant is not positioned by the merchant, and the situation that the actually consumed user group characteristics are not consistent with the positioned consumed user group characteristics is likely to occur when the merchant positions.
Wherein the first user group characteristics include: basic features, social features, interest features, and/or consumption features. For example, the base features include: gender, age, marital, education, etc.; the social features include: user occupation, car, etc.; consumption characteristics: consumption level, consumption period, etc.; interest characteristics: sports, entertainment, gourmet, etc., all human-related features are understood by those skilled in the art to be within the scope of this application.
Step S201, calculating a characteristic value of a first user group characteristic of a historical consumption user group of a merchant.
After obtaining the first user group characteristic of the historical consumption user group of the merchant, calculating a characteristic value of the first user group characteristic of the historical consumption user group of the merchant, where the characteristic value may be: the user proportion is the proportion of users with corresponding characteristics in the historical consumption user group of the merchant. Specifically, the number of users corresponding to each first user group feature and the total number of users of the historical consumption user group of the merchant are counted, and a feature value of the first user group is calculated according to the number of users corresponding to the first user group feature and the total number of users of the historical consumption user group of the merchant, for example, the feature value of the first user group is the number of users corresponding to the first user group feature/the total number of users of the historical consumption user group of the merchant.
The first user group feature is a feature of a historical consumption user group of a merchant, and may include some features capable of highlighting the merchant at a consumption level, in order to enable a recommended geographic location of a store to bring more traffic to the merchant, and to be closer to a historical consumption location of the merchant, a seed feature needs to be extracted from the first user group feature, and specifically, the seed feature may be determined by the following method in steps S202 to S203:
step S202, calculating a characteristic value of a first user group characteristic of a total historical consumption user group in the industry to which the merchant belongs.
In this step, it is required to determine an industry to which the merchant belongs, for example, the merchant is a "wanxiangyuan", which may be determined that the industry to which the merchant belongs is a catering industry, then obtain total historical consumption order information of the industry to which the merchant belongs, thereby determine a total historical consumption user group in the industry to which the merchant belongs, count a total user number of the total historical consumption user group in the industry to which the merchant belongs, count a user number corresponding to a first user group feature after determining the total historical consumption user group in the industry to which the merchant belongs, calculate a feature value of the first user group feature of the total historical consumption user group in the industry to which the merchant belongs according to the user number corresponding to the first user group feature of the total historical consumption user group in the industry to which the merchant belongs and the total user number of the total historical consumption user group in the industry to which the merchant belongs, for example, the feature value of the first user group feature of the total historical consumption user group feature of the industry to which the merchant belongs is the first user group feature of the total historical consumption The number of the corresponding users/the total number of the users of the total historical consumption user group in the industry of the merchant.
Step S203, determining the seed characteristic and the characteristic value of the seed characteristic according to the characteristic value of the first user group characteristic of the historical consumption user group of the merchant and the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs.
The seed characteristic is a characteristic capable of highlighting a merchant at a consumption level, and is determined by performing difference comparison on a first user group characteristic of a historical consumption user group of the merchant and a first user group characteristic of a total historical consumption user group in the industry to which the merchant belongs.
After calculating the feature value of the first user group feature of the historical consumption user group of the merchant according to step S201 and calculating the feature value of the first user group feature of the total historical consumption user group in the industry to which the merchant belongs according to step S202, the seed feature and the feature value of the seed feature may be determined according to the feature value of the first user group feature of the historical consumption user group of the merchant and the feature value of the first user group feature of the total historical consumption user group in the industry to which the merchant belongs.
Specifically, the seed characteristics can be determined by the following method: calculating the ratio of the characteristic value of the first user group characteristic of the historical consumption user group of the merchant to the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs; and if the ratio is greater than or equal to the preset threshold, determining that the first user group characteristic corresponding to the characteristic value is the seed characteristic. The preset threshold may be flexibly set by a person skilled in the art according to actual needs, for example, the preset threshold is set to 1.5, which is only an example and does not have any limiting effect.
In the following description, with reference to the example, the first user group characteristic of the historical consumption user group of the merchant is female, the corresponding characteristic value is 80%, and the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs: the characteristic value corresponding to the female is 40%, the ratio of the characteristic value 80% of the female characteristic of the historical consumption user group of the merchant to the characteristic value 40% corresponding to the female characteristic of the total historical consumption user group in the industry to which the merchant belongs is 2, and is greater than a preset threshold value 1.5, the female characteristic is taken as the seed characteristic, wherein the characteristic value 80% is the characteristic value of the seed characteristic. By this method, at least one seed feature is obtained, where the seed feature is labeled Xi, and the feature value of the seed feature i is denoted as Ai (i stands for 1, 2, 3.. once.. so), which results in a set of seed features: x1 (eigenvalue a1), X2 (eigenvalue a2), X3 (eigenvalue A3).
Step S204, the target geographical area is determined based on the position service information.
The target geographical area is a Geo-fence divided according to location service information, and Geo-fencing (Geo-fencing) is an application of LBS, namely, a virtual fence is used to enclose a virtual geographical boundary.
Step S205, calculating the target geographical area corresponding to each user according to the user position information.
Specifically, the user location information may be determined according to an Internet Protocol (IP) address of the user, or the user's mobile phone number may be determined according to an account where the user logs in, and then the user location information may be determined by the location where the number is accessed to the mobile network. And after the user position information is determined, converting the user position information into longitude and latitude, and determining a target geographic area corresponding to the user according to the converted longitude and latitude.
Step S206, analyzing the second user group characteristics of the user groups in each target geographic area.
After the target geographic area corresponding to each user is determined, a user group in the target geographic area may be determined, user features of each user in the target geographic area are obtained, and then, the user features of all the users are combined together to form a second user group feature.
Step S207, calculating feature values of the second user group features corresponding to the target geographic areas.
After the second user group characteristics of the user groups in each target geographic area are obtained through analysis, characteristic values of the second user group characteristics corresponding to each target geographic area need to be calculated, where the characteristic values may be: the user proportion is the proportion of users with corresponding characteristics in each target geographical area. Specifically, the number of users corresponding to the second user group feature corresponding to each target geographic area and the total number of users of the user group in each target geographic area are counted, and the feature value of the second user group is calculated according to the number of users corresponding to the second user group feature corresponding to each target geographic area and the total number of users of the user group in each target geographic area, for example, the feature value of the second user group is the number of users corresponding to the second user group feature corresponding to each target geographic area/the total number of users of the user group in each target geographic area and is marked as Bi.
And step S208, matching the seed characteristics with second user group characteristics corresponding to each target geographic area.
The process of matching the seed features with the second user group features corresponding to each target geographic area is a process of determining whether the second user group features corresponding to each target geographic area have the seed features, for example, the seed features and the second user group features may be compared one by one, and if the second user group features corresponding to the target geographic area have the corresponding seed features, it may be determined that the seed features and the second user group features are matched with each other. By matching the seed features with the second user group features corresponding to the respective target geographic areas, it can be determined whether similar users exist in the respective target geographic areas.
The seed feature is matched with a second user group feature corresponding to different target geographic areas, and the matching results may not be the same, for example, the seed feature includes: x1, X2, X3, X4, X5, X6, X7, the second user group characteristic corresponding to the target geographic area 1 comprising: y1, Y2, Y4, Y6, Y8, Y9, Y10, the second user group characteristic corresponding to the target geographic area 2 comprising: y1, Y3, Y5, Y6, Y7, Y9, Y10, the second user group characteristic corresponding to the target geographic area 3 comprising: y1, Y4, Y6, Y7, Y10, Y11, Y12, matching the seed characteristics with the second user group characteristics corresponding to each target geographical area, and taking the matched seed characteristics and second user group characteristics as follows:
second user group characteristics corresponding to the target geographic area 1: y1, Y2, Y4, Y6, seed characteristics: x1, X2, X4, X6;
second user group characteristics corresponding to the target geographic area 2: y1, Y3, Y5, Y6, Y7, seed characteristics: x1, X3, X5, X6, X7;
second user group characteristics corresponding to the target geographic area 3: y1, Y4, Y6, Y7, seed characteristics: x1, X4, X6, X7; this is by way of example only and is not intended to be limiting.
Step S209, taking the feature values of the matched seed features as weights, performing weighted calculation on the feature values of the second user group features, and determining recommendation scores of the target geographic areas.
After the mutually matched seed features and the second user group features are determined, the feature values of the mutually matched seed features are used as weights, the feature values of the second user group features are subjected to weighted calculation, and the recommendation scores of the target geographic areas are determined.
In connection with the example illustrated in step S208, a recommendation score1 ═ sum (B1 a1+ B2 a2+ B4 a4+ B6 a6) for the target geographic region 1 may be determined according to step S209
Determine recommendation score2 ═ sum (B1 a1+ B3 A3+ B5 a5+ B6 a6+ B7 a7) for target geographic region 2
Determine the recommendation score, score3, (B1 a1+ B4 a4+ B6 a6+ B7 a7) for the target geographic region 3
In the step, the characteristic value of the seed characteristic is introduced in the calculation of the recommendation score of each target geographic area, so that the calculated recommendation score can reflect the matching condition of the user group and the historical consumption user group of the merchant in each target geographic area, and the higher the recommendation score is, the more the user group and the historical consumption user group of the merchant in the target geographic area are matched.
And step S210, sequencing each target geographic area according to the recommended scores.
The recommendation score is a composite score for the target geographic region, so a higher recommendation score indicates a greater match between the group of users in the target geographic region and the merchant's group of historical consuming users. When the geographical position of the store is recommended according to the recommendation score, the recommendation can be performed according to the recommendation score, specifically, the target geographical areas can be sorted according to the sequence of the recommendation scores from large to small, and the sorting result of the target geographical areas is obtained.
And step S211, recommending the geographical position of the store to the merchant according to the sequencing result.
After the target geographic areas are ranked according to the recommended scores to obtain ranking results, the geographic positions of the stores can be recommended to the merchants according to the ranking results, for example, the first target geographic area ranked in the first place can be recommended to the merchants, or the third target geographic area ranked in the first place can be recommended to the merchants, where no specific limitation is made, and after knowing the recommended geographic position of the store, the merchants can select whether to set up a new store at the recommended geographic position of the store according to actual needs.
The method for recommending the geographical position of the store of the merchant provided by the invention not only can be used for recommending the geographical position of a newly opened store to the merchant in the city with the opened store, but also can be used for recommending the geographical position of a newly opened store to the merchant in other cities.
According to the method provided by the embodiment of the invention, the characteristic value of the first user group characteristic of the historical consumption user group of the merchant is compared with the characteristic value of the first user characteristic of the total historical consumption user group in the industry to which the merchant belongs to determine the seed characteristic and the characteristic value of the seed characteristic, the seed characteristic is matched with the second user group characteristic corresponding to each target geographic area, the characteristic value of the matched seed characteristic is taken as the weight, the characteristic value of the second user group characteristic is weighted and calculated, the recommendation score of each target geographic area is determined, the recommendation score reflects the matching condition of the user group in each target geographic area and the historical consumption user group of the merchant, the higher the recommendation score is, the more the user group in the target geographic area is matched with the historical consumption user group of the merchant, the geographic position of the store recommended according to the recommendation score is more objective, The method is more scientific, so that the merchant can select the address more accurately, the problem of address selection misjudgment is solved, and the user flow of the merchant can be ensured due to the fact that a plurality of potential consumer groups exist.
Fig. 3 is a schematic structural diagram of a recommendation device for a geographical location of a merchant store according to one embodiment of the present invention. As shown in fig. 3, the apparatus includes: the system comprises an acquisition module 300, an extraction module 310, an analysis module 320, a first calculation module 330, a matching module 340, a second calculation module 350 and a recommendation module 360.
An obtaining module 300 adapted to obtain a first user group characteristic of a historical consumption user group of a merchant;
an extracting module 310, adapted to extract seed features and feature values of the seed features from the first user group features;
an analysis module 320 adapted to analyze a second user group characteristic of the user group within each of the target geographic areas;
the first calculating module 330 is adapted to calculate feature values of the second user group features corresponding to the respective target geographic areas;
a matching module 340 adapted to match the seed features with second user group features corresponding to each target geographic area;
the second calculating module 350 is adapted to calculate the recommendation score of each target geographic area by using the feature values of the matched seed features and the feature values of the second user group features;
and the recommending module 360 is suitable for recommending the geographical position of the store to the merchant according to the recommendation scores of the target geographical areas.
Optionally, the second calculation module 350 is further adapted to: and taking the feature values of the matched seed features as weights, performing weighted calculation on the feature values of the second user group features, and determining the recommendation scores of the target geographic areas.
Optionally, the extraction module 310 is further adapted to: calculating a characteristic value of a first user group characteristic of a historical consumption user group of a merchant;
calculating a characteristic value of a first user group characteristic of a total historical consumption user group in the industry to which the merchant belongs;
and determining the seed characteristic and the characteristic value of the seed characteristic according to the characteristic value of the first user group characteristic of the historical consumption user group of the merchant and the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs.
Optionally, the extraction module 310 is further adapted to: calculating the ratio of the characteristic value of the first user group characteristic of the historical consumption user group of the merchant to the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs;
and if the ratio is greater than or equal to the preset threshold, determining that the first user group characteristic corresponding to the characteristic value is the seed characteristic.
Optionally, the analysis module 320 is further adapted to: determining a target geographic area based on the location service information;
calculating a target geographical area corresponding to each user according to the user position information;
second user group characteristics of the user groups within the respective target geographic areas are analyzed.
Optionally, the recommendation module 360 is further adapted to: sorting each target geographical area according to the recommended scores;
and recommending the geographical position of the store to the merchant according to the sequencing result.
Optionally, the first user group characteristic or the second user group characteristic comprises: basic features, social features, interest features, and/or consumption features.
Optionally, the characteristic value comprises: the user is the ratio.
According to the device provided by the above embodiment of the present invention, the first user group characteristic of the historical consumption user group of the merchant is the characteristic of the user actually consuming by the merchant, which is a visual embodiment of the merchant on the consumption level, the seed characteristic is extracted from the first user group characteristic, the positioning of the merchant can be highlighted, the recommendation score of each target geographical area calculated by using the characteristic value of the mutually matched seed characteristic and the characteristic value of the second user group characteristic reflects the matching condition of the user group and the historical consumption user group of the merchant in each target geographical area, the higher the recommendation score is, the more the user group in the target geographical area is matched with the historical consumption user group of the merchant, the geographical position of the store recommended according to the recommendation score is more objective and scientific, the merchant is more accurate in site selection, the problem of site selection misjudgment is overcome, because of a large number of potential consumption groups, therefore, the user traffic of the merchant can be guaranteed.
The embodiment of the application also provides a nonvolatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the method for recommending the geographical position of the merchant store in any method embodiment.
Fig. 4 is a schematic structural diagram of a computing device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.
As shown in fig. 4, the computing device may include: a processor (processor)402, a Communications Interface 404, a memory 406, and a Communications bus 408.
Wherein:
the processor 402, communication interface 404, and memory 406 communicate with each other via a communication bus 408.
A communication interface 404 for communicating with network elements of other devices, such as clients or other servers.
The processor 402 is configured to execute the program 410, and may specifically execute the relevant steps in the above-described embodiment of the method for recommending a geographical location of a merchant store.
In particular, program 410 may include program code comprising computer operating instructions.
The processor 402 may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 406 for storing a program 410. Memory 406 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 410 may be specifically configured to cause the processor 402 to execute the method for recommending the geographical location of the merchant store in any of the above-described method embodiments. For specific implementation of each step in the program 410, reference may be made to corresponding steps and corresponding descriptions in units in the above-mentioned recommended embodiment of the geographical location of the merchant store, which are not described herein again. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described devices and modules may refer to the corresponding process descriptions in the foregoing method embodiments, and are not described herein again.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of the merchant store geographical location recommendation device in accordance with embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (16)
1. A recommendation method for geographical locations of merchant stores comprises the following steps:
acquiring first user group characteristics of a historical consumption user group of a merchant, and extracting seed characteristics and characteristic values of the seed characteristics from the first user group characteristics; the characteristic value of the seed characteristic reflects the proportion of users with corresponding characteristics in a historical consumption user group of a merchant;
analyzing the second user group characteristics of the user groups in each target geographic area, and calculating the characteristic values of the second user group characteristics corresponding to each target geographic area; the characteristic value of the second user group characteristic is the proportion of users with the characteristic in the user group in the target geographic area;
matching the seed characteristics with second user group characteristics corresponding to each target geographic area, and calculating the recommendation score of each target geographic area by using the characteristic values of the seed characteristics and the second user group characteristics which are matched with each other;
recommending the geographical position of the store to the merchant according to the recommendation score of each target geographical area;
wherein the extracting of the seed feature from the first user group feature and the feature value of the seed feature further comprises:
calculating a characteristic value of a first user group characteristic of a historical consumption user group of a merchant;
calculating a characteristic value of a first user group characteristic of a total historical consumption user group in the industry to which the merchant belongs;
and determining the seed characteristic and the characteristic value of the seed characteristic according to the characteristic value of the first user group characteristic of the historical consumption user group of the merchant and the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs.
2. The method of claim 1, wherein the calculating the recommendation score for each target geographic area using the feature values of the mutually matched seed features and the feature values of the second user group features further comprises:
and taking the feature values of the matched seed features as weights, performing weighted calculation on the feature values of the second user group features, and determining the recommendation scores of the target geographic areas.
3. The method of claim 1 or 2, wherein the determining a seed feature and a feature value of the seed feature according to a feature value of a first user group feature of a historical consuming user group of a merchant and a feature value of a first user group feature of an overall historical consuming user group within an industry to which the merchant belongs further comprises:
calculating the ratio of the characteristic value of the first user group characteristic of the historical consumption user group of the merchant to the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs;
and if the ratio is greater than or equal to a preset threshold value, determining that the first user group characteristic corresponding to the characteristic value is a seed characteristic.
4. The method of claim 1 or 2, wherein said analyzing a second user group characteristic of the user group within each target geographic area further comprises:
determining a target geographic area based on the location service information;
calculating a target geographical area corresponding to each user according to the user position information;
second user group characteristics of the user groups within the respective target geographic areas are analyzed.
5. The method of claim 1 or 2, wherein the recommending a merchant a geographic location of a store according to the recommendation score for each target geographic region further comprises:
sorting each target geographic area according to the recommended scores;
and recommending the geographical position of the store to the merchant according to the sequencing result.
6. The method according to claim 1 or 2, wherein the first or second user group characteristic comprises: basic features, social features, interest features, and/or consumption features.
7. The method of claim 1 or 2, wherein the feature values comprise: the user is the ratio.
8. A recommendation device for a geographic location of a merchant store, comprising:
the acquisition module is suitable for acquiring first user group characteristics of a historical consumption user group of a merchant;
the extraction module is suitable for extracting seed features and feature values of the seed features from the first user group features;
the analysis module is suitable for analyzing the second user group characteristics of the user groups in each target geographic area;
the first calculation module is suitable for calculating the characteristic value of the second user group characteristic corresponding to each target geographic area;
the matching module is suitable for matching the seed characteristics with the second user group characteristics corresponding to each target geographic area;
the second calculation module is suitable for calculating the recommendation score of each target geographic area by using the feature values of the matched seed features and the feature values of the second user group features;
the recommending module is suitable for recommending the geographical position of the store to the merchant according to the recommending scores of the target geographical areas;
the extraction module is further adapted to: calculating a characteristic value of a first user group characteristic of a historical consumption user group of a merchant;
calculating a characteristic value of a first user group characteristic of a total historical consumption user group in the industry to which the merchant belongs;
and determining the seed characteristic and the characteristic value of the seed characteristic according to the characteristic value of the first user group characteristic of the historical consumption user group of the merchant and the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs.
9. The apparatus of claim 8, wherein the second computing module is further adapted to:
and taking the feature values of the matched seed features as weights, performing weighted calculation on the feature values of the second user group features, and determining the recommendation scores of the target geographic areas.
10. The apparatus of claim 8 or 9, wherein the extraction module is further adapted to:
calculating the ratio of the characteristic value of the first user group characteristic of the historical consumption user group of the merchant to the characteristic value of the first user group characteristic of the total historical consumption user group in the industry to which the merchant belongs;
and if the ratio is greater than or equal to a preset threshold value, determining that the first user group characteristic corresponding to the characteristic value is a seed characteristic.
11. The apparatus of claim 8 or 9, wherein the analysis module is further adapted to: determining a target geographic area based on the location service information;
calculating a target geographical area corresponding to each user according to the user position information;
second user group characteristics of the user groups within the respective target geographic areas are analyzed.
12. The apparatus of claim 8 or 9, wherein the recommendation module is further adapted to: sorting each target geographic area according to the recommended scores;
and recommending the geographical position of the store to the merchant according to the sequencing result.
13. The apparatus of claim 8 or 9, wherein the first or second user group characteristic comprises: basic features, social features, interest features, and/or consumption features.
14. The apparatus of claim 8 or 9, wherein the feature value comprises: the user is the ratio.
15. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the method for recommending merchant store geographic locations of any of claims 1-7.
16. A computer storage medium having stored therein at least one executable instruction that causes a processor to perform operations corresponding to the method of recommending merchant store geographic locations of any of claims 1-7.
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| CN111985985B (en) * | 2019-05-22 | 2024-04-30 | 口口相传(北京)网络技术有限公司 | Store information extraction method and device, storage medium and electronic device |
| CN112116179A (en) * | 2019-06-19 | 2020-12-22 | 北京三快在线科技有限公司 | Address analysis method and device, storage medium and electronic device |
| CN110634028B (en) * | 2019-09-18 | 2022-08-19 | 创优数字科技(广东)有限公司 | Commodity structure configuration method and system |
| CN111274500A (en) * | 2020-01-15 | 2020-06-12 | 平安医疗健康管理股份有限公司 | Position information recommendation method and device, computer equipment and storage medium |
| CN111488527B (en) * | 2020-04-17 | 2021-02-02 | 滴图(北京)科技有限公司 | Position recommendation method and device, electronic equipment and computer-readable storage medium |
| CN112308603A (en) * | 2020-10-13 | 2021-02-02 | 深圳坤湛科技有限公司 | Similarity expansion-based rapid store site selection method and device and storage medium |
| CN112215658B (en) * | 2020-10-15 | 2024-08-20 | 平安国际智慧城市科技股份有限公司 | Big data-based address selection method, device, computer equipment and storage medium |
| CN113129113B (en) * | 2021-05-13 | 2024-10-22 | 上海寻梦信息技术有限公司 | Store matching method, system, equipment and storage medium based on geofence |
| CN113706222B (en) * | 2021-09-18 | 2023-12-22 | 口碑(上海)信息技术有限公司 | Store site selection method and device |
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