CN112116178A - Ordering method and device for offline stores - Google Patents
Ordering method and device for offline stores Download PDFInfo
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- CN112116178A CN112116178A CN201910532001.3A CN201910532001A CN112116178A CN 112116178 A CN112116178 A CN 112116178A CN 201910532001 A CN201910532001 A CN 201910532001A CN 112116178 A CN112116178 A CN 112116178A
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- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- 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
Abstract
The embodiment of the invention discloses a method and a device for ordering offline stores, and relates to the technical field of internet. The method comprises the following steps: acquiring an operation intention of a user for going out of a store based on an online operation of the user; screening at least one alternative offline store matched with the operation intention from all offline stores based on the comprehensive service capability index of all offline stores; constructing a formula model for calculating a matching value of the at least one alternative offline store and the operation intention; and respectively calculating matching values of the at least one alternative offline store and the operation intention based on the formula model, and sequencing the at least one alternative offline store according to the matching values. According to the method for sequencing the offline stores, the matching degree between the offline stores recommended to the user and the operation intention of the user is improved, and the purchase conversion rate of the offline stores is further improved.
Description
Technical Field
The invention relates to the technical field of internet, in particular to a method and a device for ordering offline stores.
Background
With the diversification of consumer demands, e-commerce is moving from pure online sales to online and offline integrated sales. Online and offline sales will be deeply integrated in the future, and an online and offline integrated sales model will lead to a new business model in the future. In an online environment, when a user has a certain specific requirement, a suitable offline store is often found through network search, and whether the offline store can provide a good or service corresponding to the specific requirement is actually examined. Therefore, it is crucial to order all offline stores to preferentially recommend good-quality match line offline stores to the user.
In the related art, the sorting mode of the off-line stores is mostly more traditional, that is, the sorting result of the off-line stores is formed according to the heat or sales of the off-line stores and the manual intervention. Recommending a certain number of offline stores ranked in the top to the user. Obviously, the traditional sorting mode does not consider the operation intention of the user for searching for a suitable offline store through network search, and meanwhile, the service capacity of the offline store cannot be quantized, so that the matching degree of the offline store recommended to the user and the operation intention of the user is reduced, and the purchase conversion rate of the offline store is further reduced.
Disclosure of Invention
In order to overcome the problems that a traditional sorting mode in the related art does not consider that a user searches for a suitable operation intention of an offline store through network search, meanwhile, the service capacity of the offline store cannot be quantized, and the matching degree of the offline store recommended to the user and the operation intention of the user is reduced, the embodiment of the invention provides a sorting method and a sorting device of the offline store.
According to an aspect of the present invention, there is provided a method for ordering offline stores, including:
acquiring an operation intention of a user for going out of a store based on an online operation of the user;
screening at least one alternative offline store matched with the operation intention from all offline stores based on the comprehensive service capability index of all offline stores;
constructing a formula model for calculating a matching value of the at least one alternative offline store and the operation intention; and
and respectively calculating the matching values of the at least one alternative offline store and the operation intention based on the formula model, and sequencing the at least one alternative offline store according to the matching values.
Preferably, the method for ordering offline stores further includes:
obtaining a basic information feature vector of the user according to the historical online operation of the user, wherein the basic information feature vector comprises: purchasing power, regional selection preferences for off-line stores, user personality preferences.
Preferably, the in-line operation comprises: search for keywords and/or purchase physical products.
Preferably, the obtaining of the operation intention of the user to the offline store based on the online operation of the user includes:
and performing semantic analysis on the keywords and/or the entity products to obtain the service types of the services provided by the offline stores expected by the user.
Preferably, before the step of screening out at least one alternative offline store matching the operation intention from all offline stores based on the comprehensive service capability index of all offline stores, the method further comprises:
and acquiring the comprehensive service capability index of all the off-line stores.
Preferably, the plurality of service capability indicators of the offline store include: user feedback index, off-line store service quality index and off-line store hardware parameter index
The acquiring of the comprehensive service capability index of all the off-line stores comprises:
respectively calculating a plurality of service capacity indexes of each offline store;
and respectively converting the plurality of service capacity indexes of each offline store into the comprehensive service capacity index which accords with the life cognition.
Preferably, the constructing a formula model for calculating the matching value of the at least one alternative offline store and the operation intention comprises:
and constructing a formula model for calculating a matching value of the at least one alternative offline store and the operation intention by taking the operation intention and the basic information feature vector as weights.
According to another aspect of the present invention, there is provided a sorting apparatus for offline stores, comprising:
a first acquisition unit configured to perform an online operation based on a user, and acquire an operation intention of the user to an offline store;
a screening unit configured to perform screening of at least one alternative offline store matching the operation intention from all offline stores based on a composite service capability index of the offline stores;
a construction unit configured to perform construction of a formula model for calculating a matching value of the at least one alternative offline store with the operational intention; and
a sorting unit configured to perform calculating matching values of the at least one alternative offline store and the operational intention respectively based on the formula model, and sorting the at least one alternative offline store according to the matching values.
Preferably, the sorting apparatus further includes:
a second obtaining unit configured to perform an on-line operation according to the history of the user, and obtain a basic information feature vector of the user, where the basic information feature vector includes: purchasing power, regional selection preferences for off-line stores, user personality preferences.
Preferably, the in-line operation comprises: search for keywords and/or purchase physical products.
Preferably, the obtaining of the operation intention of the user to the offline store based on the online operation of the user includes:
and performing semantic analysis on the keywords and/or the entity products to obtain the service types of the services provided by the offline stores expected by the user.
Preferably, the sorting apparatus further includes:
and the third acquisition unit is configured to acquire the comprehensive service capability index of all the offline stores.
Preferably, the plurality of service capability indicators of the offline store include: user feedback index, off-line store service quality index and off-line store hardware parameter index
The acquiring of the comprehensive service capability index of all the off-line stores comprises:
respectively calculating a plurality of service capacity indexes of each offline store;
and respectively converting the plurality of service capacity indexes of each offline store into the comprehensive service capacity index which accords with the life cognition.
Preferably, the constructing a formula model for calculating the matching value of the at least one alternative offline store and the operation intention comprises:
and constructing a formula model for calculating a matching value of the at least one alternative offline store and the operation intention by taking the operation intention and the basic information feature vector as weights.
According to still another aspect of the present invention, there is provided a sort control apparatus for an offline store, including: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to perform the above-described off-line store ordering method.
According to yet another aspect of the present invention, there is provided a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, which when executed, implement the method for ordering offline stores as described above.
According to yet another aspect of the present invention, there is provided a computer program product comprising a computer program product, the computer program comprising program instructions which, when executed by a mobile terminal, cause the mobile terminal to perform the steps of the above-mentioned method of ordering of offline stores.
One embodiment of the present invention includes at least the following advantages or benefits:
1) and acquiring the operation intention of the user to the off-line store based on the on-line operation of searching keywords and/or purchasing entity products and the like of the user. And screening out at least one alternative offline store which is matched with the operation intention of the user to the offline store from all offline stores based on the comprehensive service capability index of all offline stores. All offline stores are screened by combining the operation intentions of the user to the offline stores, and at least one alternative offline store matched with the operation intentions of the user to the offline stores is obtained, so that the matching degree of the offline stores recommended to the user and the operation intentions of the user is improved, and the purchase conversion rate of the offline stores is further improved.
2) And acquiring comprehensive service capability indexes of all off-line stores. Specifically, the method comprises the steps of calculating main data of off-line stores through an algorithm deployed in advance to obtain a plurality of service capacity indexes of each off-line store. And respectively converting the plurality of service capability indexes of each offline store into a comprehensive service capability index conforming to the cognition of the life. Optionally, the sum of the multiple service capability indicators of each offline store is logarithmized, and the value of the obtained comprehensive service capability indicator is subject to positive distribution. And screening at least one alternative offline store matched with the operation intention from all offline stores based on the comprehensive service capability index of all offline stores. And constructing a formula model for calculating a matching value of at least one alternative offline store and the operation intention by taking the operation intention of the offline store and the basic information characteristic vector of the user as weights. And respectively calculating the matching value of the at least one alternative offline store and the operation intention based on the formula model, and sequencing the at least one alternative offline store according to the matching value. The comprehensive service capability index of each offline store is quantified, so that the actual service capability of the offline store is accurately reflected, the matching degree of the offline store recommended to the user and the operation intention of the user is improved, and the purchase conversion rate of the offline store is improved.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:
fig. 1 shows a flow diagram of a ranking method of offline stores according to an embodiment of the present invention.
FIG. 2 shows a flow diagram of a method of ordering offline stores of one embodiment of the present invention.
Fig. 3 shows a flow diagram of a ranking method of offline stores of one embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a sorting apparatus of an offline store according to an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a sorting apparatus of an offline store according to an embodiment of the present invention.
Fig. 6 is a schematic structural diagram showing a ranking control apparatus of an offline store according to an embodiment of the present invention.
Detailed Description
The present invention will be described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details. Well-known methods, procedures, and procedures have not been described in detail so as not to obscure the present invention. The figures are not necessarily drawn to scale.
Fig. 1 is a flowchart illustrating a sorting method of offline stores according to an embodiment of the present invention. The method specifically comprises the following steps:
in step S110, an operation intention of the user on an offline store is acquired based on an online operation of the user.
In this step, based on the on-line operation of the user, the operation intention of the user on the offline store is acquired. An online operation by a user, comprising: search for keywords and/or purchase physical products. The service type of the service provided by the offline store expected by the user can be obtained by performing semantic analysis on the keywords and/or the attributes of the entity products. It can be understood that the type of service that the user desires the service provided by the offline store is the user's operational intention of the offline store. For example, if a user purchases an automobile part online through a network, semantic analysis can be performed on information such as the brand, model, price, and the like of the purchased automobile part to obtain the service type of the service that the user desires to provide for the user by the offline store. In addition, the user searches keywords such as 'car with thirty-ten-thousand-yuan price' on the internet, and the semantic analysis can be performed on the keywords such as 'car with thirty-thousand-yuan price', so that the service type of the service which the user expects the corresponding offline store to provide for the user can be obtained. The service types here are, for example, repair service, maintenance service, purchase service, and conversion service.
Optionally, semantic analysis is performed on the keywords and/or the attributes of the entity products to obtain the location information of the offline store where the user desires to provide the service. The location information of the offline store, at which the user desires to provide the service, may be used as a parameter of a formula model for calculating a matching value of the at least one alternative offline store with the operational intention.
In step S120, at least one alternative offline store matching the operation intention is screened out from all offline stores based on the integrated service capability index of the offline stores.
In this step, at least one alternative offline store that matches the user's operational intent on the offline store is screened from all offline stores based on the aggregate service capability index of all offline stores. The comprehensive service capability index of the offline store not only represents the quantification of the quality of goods, service and the like provided by the store, but also represents the acceptance and trust of most users for the offline store.
The comprehensive service capability index can be calculated according to a plurality of service capability indexes of the offline store. Wherein, a plurality of service ability indexes of the off-line store comprise: and the user feeds back indexes, the off-line store service quality index and the off-line store hardware parameter index. For example, the user feedback index indicates the satisfaction degree of the user on the service provided by the offline store, and may be obtained by the scoring system according to the average score by filling the relevant scores such as the product quality, the service quality, the delivery speed and the like for the current consumption after the user finishes consuming the online offline store every time. The offline store service quality index represents the service capability of the offline store in terms of response time, reliability, safety and the like of the offline store for providing services. The off-line store hardware parameter index represents the service capability of the off-line store in terms of the perfection of various hardware facilities for providing services for the off-line store.
For example, a user searches for keywords such as "car with a thirty-ten-thousand-dollar price" on the internet, and performs semantic analysis on the keywords such as "car with a thirty-thousand-dollar price" to obtain a purchase service that the user expects a corresponding off-line store to provide for the user, that is, the user expects to purchase a car with a thirty-thousand-dollar price at the corresponding store. And screening out at least one alternative offline store for selling automobiles with the price of about thirty-ten-thousand yuan from all offline stores based on the comprehensive service capacity index of all offline stores.
In step S130, a formula model for calculating a matching value of the at least one alternative offline store and the operational intention is constructed.
In the step, a formula model for calculating the matching value of the at least one alternative offline store and the operation intention of the user to the offline store is constructed. The operation intention of the user to the offline store can be used as a weight, and a formula model for calculating the matching value of the at least one alternative offline store and the operation intention is constructed.
In step S140, based on the formula model, matching values of the at least one alternative offline store and the operation intention are respectively calculated, and the at least one alternative offline store is sorted according to the matching values.
In this step, matching values of the at least one alternative offline store and the operational intention are respectively calculated based on the formula model, and the at least one alternative offline store is sorted by the matching values. According to the requirement, a certain number of alternative offline stores ranked in the front can be displayed to the user, and the user selects one alternative offline store from the displayed offline stores for consumption.
According to the embodiment of the invention, the operation intention of the user on the off-line store is obtained based on the on-line operation of searching keywords and/or purchasing entity products and the like of the user. And screening out at least one alternative offline store which is matched with the operation intention of the user to the offline store from all offline stores based on the comprehensive service capability index of all offline stores. All offline stores are screened by combining the operation intentions of the user to the offline stores, and at least one alternative offline store matched with the operation intentions of the user to the offline stores is obtained, so that the matching degree of the offline stores recommended to the user and the operation intentions of the user is improved, and the purchase conversion rate of the offline stores is further improved.
Optionally, the operation intention of the user to the offline store is used as a weight, and a formula model for calculating a matching value of the at least one alternative offline store and the operation intention is constructed. And respectively calculating the matching value of the at least one alternative offline store and the operation intention based on the formula model, and sequencing the at least one alternative offline store according to the matching value. And displaying a certain number of alternative offline stores ranked at the top to the user, and selecting one alternative offline store from the displayed offline stores by the user for consumption. The operation intentions are used as the weight of a formula model for calculating the matching value of at least one alternative offline store and the operation intentions, so that the matching degree of the offline stores recommended to the user and the operation intentions of the user is further improved, and the purchase conversion rate of the offline stores is further improved.
FIG. 2 shows a flow diagram of a method of ordering offline stores of one embodiment of the present invention. Specifically, the process of screening the offline stores matched with the operation intention of the user from all the offline stores according to the online operation of the user and the comprehensive service capability indexes of all the offline stores is shown, the operation intention of the user and the basic information feature vector are used as weight parameters, the matching degree of at least one screened alternative offline store and the operation intention of the user is calculated, and at least one alternative offline store is ranked according to the matching degree to obtain a ranking result. And calculating the main data of the off-line stores by using an algorithm deployed in advance to obtain service capability indexes such as user feedback indexes, off-line store service quality indexes and off-line store hardware parameter indexes of each off-line store.
Fig. 3 is a flow chart illustrating a method of ordering offline stores according to an embodiment of the present invention. The present embodiment is a more sophisticated method of ordering off-line stores than the previous embodiments. The method for ordering offline stores in this embodiment is described in detail below with reference to fig. 2, and specifically includes the following steps:
in step S310, an operation intention of a user to an offline store is acquired based on an online operation of the user.
In this step, this step is identical to step S110 in fig. 1, and is not described here again.
In step S320, a basic information feature vector of the user is obtained according to the historical online operation of the user.
In this step, the basic information feature vector of the user is acquired according to the historical online operation of the user. The basic information feature vector of the user comprises: purchasing power, regional selection preferences for off-line stores, user personality preferences. The semantic analysis can be performed on the historical information of the search keywords and/or the purchased entity products of the user to obtain the basic information feature vector of the user.
In step S330, a comprehensive service capability index of all the offline stores is obtained.
In this step, the composite service capability index of all the offline stores is obtained. The plurality of service capability indicators for the offline store include: user feedback index, off-line store service quality index and off-line store hardware parameter index
Acquiring comprehensive service capability indexes of all off-line stores, comprising the following steps:
and respectively calculating a plurality of service capacity indexes of each offline store. As shown in fig. 2, the offline store master data is calculated by using a pre-deployed algorithm, so as to obtain service capability indexes such as a user feedback index, an offline store service quality index, and an offline store hardware parameter index of each offline store. For example, by using an algorithm deployed in advance, the main data of the offline store a is calculated to obtain a user feedback index a, an offline store service quality index b and an offline store hardware parameter index c of the offline store a.
However, the service capability indexes such as the user feedback index of each offline store, the offline store service quality index, and the offline store hardware parameter index obtained by calculating the main data of the service stores through an algorithm deployed in advance are absolute data, and the situation of deviating from the actual demand may occur. For example, three alternative offline stores selling automobiles at a price of about thirty ten thousand dollars are screened from all offline stores based on the comprehensive service capability index of all offline stores. Respectively, a candidate offline store 1, a candidate offline store 2, and a candidate offline store 3. The alternative online store 1 sells cars of about one million yuan, the alternative online store 2 sells cars of about one million yuan, and the alternative online store 3 sells cars of about thirty-five million yuan. The algorithm for calculating the offline store master data assumes that the degree of matching between the alternative offline store 1 and the operation intention of the user is higher than the degree of matching between the alternative offline store 2 and the operation intention of the user. In fact, the prices of the alternative offline store 1 and the alternative offline store 2 for selling automobiles are greatly deviated from the daily cognition and have no reference value.
And respectively converting the plurality of service capability indexes of each offline store into a comprehensive service capability index conforming to the cognition of the life. For example, the calculation formula of the comprehensive service capability index I of the offline store a is as follows:
I＝log(a+b+c) (1)
wherein, a is a user feedback index, b is an off-line store service quality index, and c is an off-line store hardware parameter index.
The logarithm of one random variable follows a positive distribution. And taking the logarithm of the sum of the multiple service capability indexes of each offline store, and obtaining the value of the comprehensive service capability index which is subject to positive distribution. That is, the value of the obtained integrated service capability index is relatively linear near the sum of the plurality of service capability indexes conforming to the biological recognition, and the value of the obtained integrated service capability index tends to zero near the sum of the plurality of service capability indexes not conforming to the biological recognition. It can be understood that there are many conversion formulas for converting the multiple service capability indicators of each offline store into the comprehensive service capability indicator conforming to the life recognition, and a more appropriate conversion formula can be selected according to different data of the multiple service capability indicators of each offline store. The formula (1) is not to be taken as limiting the embodiments of the present application.
In step S340, at least one alternative offline store matching the operation intention is screened out from all offline stores based on the comprehensive service capability index of all offline stores.
This step is identical to step S120 in fig. 1, and will not be described here.
In step S350, a formula model for calculating a matching value of the at least one alternative offline store and the operational intention is constructed.
In the step, a formula model for calculating the matching value of the at least one alternative offline store and the operation intention of the user to the offline store is constructed. The operation intention of the user to the offline store and the basic information feature vector are used as weights, and a formula model for calculating the matching value of the at least one alternative offline store and the operation intention is constructed. The weights here may be set in advance. For example, the formula model M for calculating the matching value of the at least one alternative offline store and the operational intention is:
M＝(m+n)I (2)
wherein m is the weight of the operation intention of the user to the offline store, n is the sum of the weights of all elements in the basic information characteristic vector, and I is the comprehensive service capability index of the alternative offline store.
For example, the service type of the service that the corresponding offline store desires to provide for is purchase service by the user 1. The basic information feature vector of the user 1 comprises: the purchasing power is high, and the offline store in a region with a short distance is preferred to be selected. The weight values of the repair service, the maintenance service, the purchase service, and the retrofit service set in advance are 3, 2, 4, and 1, respectively. The right of higher purchasing power and general purchasing power set in advanceThe weights are 5 and 3, respectively, and the weights of the offline store preferred for selecting closer zones and the offline store preferred for selecting farther zones are 7 and 4, respectively. The comprehensive service capability indexes of the alternative offline store 1, the alternative offline store 2 and the alternative offline store 3 are I respectively1、I2And I3. Through the formula (2), the matching values of the operation intention of the user 1 and the alternative offline store 1, the alternative offline store 2 and the alternative offline store 3 are respectively (4+5+7) I1，(4+5+7)I2And (4+5+7) I3。
In step S360, based on the formula model, matching values of the at least one alternative offline store and the operation intention are respectively calculated, and the at least one alternative offline store is sorted according to the matching values.
This step is identical to step S140 in fig. 1, and will not be described herein.
According to the embodiment of the invention, the comprehensive service capability index of all off-line stores is obtained. Specifically, the method comprises the steps of calculating main data of off-line stores through an algorithm deployed in advance to obtain a plurality of service capacity indexes of each off-line store. And respectively converting the plurality of service capability indexes of each offline store into a comprehensive service capability index conforming to the cognition of the life. Optionally, the sum of the multiple service capability indicators of each offline store is logarithmized, and the value of the obtained comprehensive service capability indicator is subject to positive distribution. And screening at least one alternative offline store matched with the operation intention from all offline stores based on the comprehensive service capability index of all offline stores. And constructing a formula model for calculating a matching value of at least one alternative offline store and the operation intention by taking the operation intention of the offline store and the basic information characteristic vector of the user as weights. And respectively calculating the matching value of the at least one alternative offline store and the operation intention based on the formula model, and sequencing the at least one alternative offline store according to the matching value. The comprehensive service capability index of each offline store is quantified, so that the actual service capability of the offline store is accurately reflected, the matching degree of the offline store recommended to the user and the operation intention of the user is improved, and the purchase conversion rate of the offline store is improved.
Preferably, the sum of a plurality of service capability indexes of each offline store is logarithmized, the value of the obtained comprehensive service capability index is subjected to positive-too distribution, the obtained comprehensive service capability index accords with biological cognition, and offline stores corresponding to the comprehensive service capability index which does not accord with the biological cognition in all the offline stores are screened out, so that the matching degree of the offline stores recommended to the user and the operation intention of the user is improved, and the purchase conversion rate of the offline stores is improved. Meanwhile, the number of the alternative offline stores is reduced, the calculation amount of the matching value of the at least one alternative offline store and the operation intention calculated based on the formula model is further reduced, and the sequencing efficiency of the offline stores is improved.
Fig. 4 is a schematic structural diagram of a sorting apparatus of an offline store according to an embodiment of the present invention. As shown in fig. 4, the sorting apparatus for offline stores includes: a first obtaining unit 410, a screening unit 420, a building unit 430 and a sorting unit 440.
A first obtaining unit 410 configured to perform an online operation based on a user, and obtain an operation intention of the user to an offline store.
And the screening unit 420 is configured to perform screening of at least one alternative offline store matched with the operation intention from all the offline stores based on the comprehensive service capability index of all the offline stores.
A building unit 430 configured to perform building a formula model for calculating a matching value of the at least one alternative offline store with the operational intent.
A sorting unit 440 configured to perform calculating matching values of the at least one alternative offline store and the operational intention, respectively, based on the formula model, and sorting the at least one alternative offline store according to the matching values.
In the embodiment of the application, the ranking device of the offline store acquires the operation intention of the user on the offline store based on the online operation of the user. An online operation by a user, comprising: search for keywords and/or purchase physical products. And screening out at least one alternative offline store which is matched with the operation intention of the user to the offline store from all offline stores based on the comprehensive service capability index of all offline stores. And constructing a formula model for calculating the matching value of the at least one alternative offline store and the operation intention of the user to the offline store. And respectively calculating the matching value of the at least one alternative offline store and the operation intention based on the formula model, and sequencing the at least one alternative offline store according to the matching value.
Fig. 5 is a schematic structural diagram of a sorting apparatus of an offline store according to an embodiment of the present invention. As shown in fig. 5, the sorting apparatus for offline stores includes: a first obtaining unit 510, a second obtaining unit 520, a third obtaining unit 530, a screening unit 540, a constructing unit 550, and a sorting unit 560.
A first obtaining unit 510 configured to perform an online operation based on a user, and obtain an operation intention of the user to an offline store.
Optionally, the inline operation comprises: search for keywords and/or purchase physical products. And performing semantic analysis on the keywords and/or the entity products to obtain the service types of the services provided by the offline stores expected by the user.
A second obtaining unit 520, configured to perform an online operation according to the history of the user, and obtain a basic information feature vector of the user, where the basic information feature vector includes: purchasing power, regional selection preferences for off-line stores, user personality preferences.
A third obtaining unit 530 configured to perform obtaining the comprehensive service capability index of all the offline stores.
Optionally, the plurality of service capability indicators of the offline store include: user feedback index, off-line store service quality index and off-line store hardware parameter index
The acquiring of the comprehensive service capability index of all the off-line stores comprises:
respectively calculating a plurality of service capacity indexes of each offline store;
and respectively converting the plurality of service capacity indexes of each offline store into the comprehensive service capacity index which accords with the life cognition.
A screening unit 540 configured to perform screening of at least one alternative offline store from all offline stores that matches the operational intent based on the composite service capability indicator of all offline stores.
A building unit 550 configured to perform building a formula model for calculating a matching value of the at least one alternative offline store with the operational intention.
Optionally, the operation intention and the basic information feature vector are used as weights, and a formula model for calculating a matching value of the at least one alternative offline store and the operation intention is constructed.
A sorting unit 560 configured to perform calculating matching values of the at least one alternative offline store and the operational intention, respectively, based on the formula model, and sorting the at least one alternative offline store by the matching values.
In the embodiment of the application, the ranking device of the offline store acquires the operation intention of the user on the offline store based on the online operation of the user. An online operation by a user, comprising: search for keywords and/or purchase physical products. And acquiring comprehensive service capability indexes of all off-line stores. Specifically, the method comprises the steps of calculating main data of off-line stores through an algorithm deployed in advance to obtain a plurality of service capacity indexes of each off-line store. And respectively converting the plurality of service capability indexes of each offline store into a comprehensive service capability index conforming to the cognition of the life. Optionally, the sum of the multiple service capability indicators of each offline store is logarithmized, and the value of the obtained comprehensive service capability indicator is subject to positive distribution. And screening at least one alternative offline store matched with the operation intention from all offline stores based on the comprehensive service capability index of all offline stores. And constructing a formula model for calculating the matching value of the at least one alternative offline store and the operation intention of the user to the offline store. And respectively calculating the matching value of the at least one alternative offline store and the operation intention based on the formula model, and sequencing the at least one alternative offline store according to the matching value.
Fig. 6 is a block diagram of a ranking control apparatus of an offline store according to an embodiment of the present invention. The apparatus shown in fig. 6 is only an example and should not limit the functionality and scope of use of embodiments of the present invention in any way.
Referring to fig. 6, the apparatus includes a processor 610, a memory 620, and an input-output device 630 connected by a bus. The memory 620 includes a Read Only Memory (ROM) and a Random Access Memory (RAM), various computer instructions and data required to perform system functions are stored in the memory 620, and the processor 610 reads the various computer instructions from the memory 620 to perform various appropriate actions and processes. An input/output device including an input portion of a keyboard, a mouse, and the like; an output section including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section including a hard disk and the like; and a communication section including a network interface card such as a LAN card, a modem, or the like. The memory 620 also stores the following computer instructions to perform the operations specified by the offline store ordering method of an embodiment of the present invention: acquiring an operation intention of a user for going out of a store based on an online operation of the user; screening at least one alternative offline store matched with the operation intention from all offline stores based on the comprehensive service capability index of all offline stores; constructing a formula model for calculating a matching value of the at least one alternative offline store and the operation intention; and respectively calculating matching values of the at least one alternative offline store and the operation intention based on the formula model, and sequencing the at least one alternative offline store according to the matching values.
Accordingly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that, when executed, implement the operations specified by the offline store ordering method.
Correspondingly, the embodiment of the invention also provides a computer program product, which comprises a computer program product, wherein the computer program comprises program instructions, and when the program instructions are executed by the mobile terminal, the mobile terminal is enabled to execute the steps of the sorting method for the offline store.
The flowcharts and block diagrams in the figures and block diagrams illustrate the possible architectures, functions, and operations of the systems, methods, and apparatuses according to the embodiments of the present invention, and may represent a module, a program segment, or merely a code segment, which is an executable instruction for implementing a specified logical function. It should also be noted that the executable instructions that implement the specified logical functions may be recombined to create new modules and program segments. The blocks of the drawings, and the order of the blocks, are thus provided to better illustrate the processes and steps of the embodiments and should not be taken as limiting the invention itself.
The above description is only a few embodiments of the present invention, and is not intended to limit the present invention, and various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for ordering off-line stores, comprising:
acquiring an operation intention of a user for going out of a store based on an online operation of the user;
screening at least one alternative offline store matched with the operation intention from all offline stores based on the comprehensive service capability index of all offline stores;
constructing a formula model for calculating a matching value of the at least one alternative offline store and the operation intention; and
and respectively calculating the matching values of the at least one alternative offline store and the operation intention based on the formula model, and sequencing the at least one alternative offline store according to the matching values.
2. The method of ordering offline store according to claim 1, further comprising:
obtaining a basic information feature vector of the user according to the historical online operation of the user, wherein the basic information feature vector comprises: purchasing power, regional selection preferences for off-line stores, user personality preferences.
3. The method of ordering of off-line stores of claim 1, wherein the on-line operations comprise: search keywords and/or purchase physical products, then
The obtaining of the operation intention of the user to the offline store based on the online operation of the user comprises:
and performing semantic analysis on the keywords and/or the entity products to obtain the service types of the services provided by the offline stores expected by the user.
4. The method for ordering offline stores according to claim 3, wherein before the step of screening out at least one alternative offline store matching the operation intent from all offline stores based on the comprehensive service capability index of all offline stores, the method further comprises:
acquiring comprehensive service capability indexes of all the offline stores,
the plurality of service capability indicators for the offline store include: user feedback index, off-line store service quality index and off-line store hardware parameter index
The acquiring of the comprehensive service capability index of all the off-line stores comprises:
respectively calculating a plurality of service capacity indexes of each offline store;
and respectively converting the plurality of service capacity indexes of each offline store into the comprehensive service capacity index which accords with the life cognition.
5. The method for ordering offline stores according to claim 2, wherein said constructing a formula model for calculating the matching value of the at least one alternative offline store with the operational intent comprises:
and constructing a formula model for calculating a matching value of the at least one alternative offline store and the operation intention by taking the operation intention and the basic information feature vector as weights.
6. An off-line store sequencing device, comprising:
a first acquisition unit configured to perform an online operation based on a user, and acquire an operation intention of the user to an offline store;
a screening unit configured to perform screening of at least one alternative offline store matching the operation intention from all offline stores based on a composite service capability index of the offline stores;
a construction unit configured to perform construction of a formula model for calculating a matching value of the at least one alternative offline store with the operational intention; and
a sorting unit configured to perform calculating matching values of the at least one alternative offline store and the operational intention respectively based on the formula model, and sorting the at least one alternative offline store according to the matching values.
7. The ordering apparatus for offline store according to claim 6, further comprising:
a second obtaining unit configured to perform an on-line operation according to the history of the user, and obtain a basic information feature vector of the user, where the basic information feature vector includes: purchasing power, regional selection preferences for off-line stores, user personality preferences.
8. The ordering apparatus for offline store according to claim 7, further comprising:
and the third acquisition unit is configured to acquire the comprehensive service capability index of all the offline stores.
9. An off-line store sequencing control device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to perform the method of ordering of off-line stores of any of claims 1 to 5 above.
10. A computer-readable storage medium storing computer instructions which, when executed, implement the method of ordering offline stores of any one of claims 1 to 5.
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| CN201910532001.3A CN112116178A (en) | 2019-06-19 | 2019-06-19 | Ordering method and device for offline stores |
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| CN201910532001.3A CN112116178A (en) | 2019-06-19 | 2019-06-19 | Ordering method and device for offline stores |
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