CN113688324B - Bank outlet recommendation method and device - Google Patents
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- CN113688324B CN113688324B CN202111043710.9A CN202111043710A CN113688324B CN 113688324 B CN113688324 B CN 113688324B CN 202111043710 A CN202111043710 A CN 202111043710A CN 113688324 B CN113688324 B CN 113688324B
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- 230000007704 transition Effects 0.000 claims description 17
- 239000011159 matrix material Substances 0.000 claims description 16
- 238000003066 decision tree Methods 0.000 claims description 7
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
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- G—PHYSICS
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- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
Abstract
The embodiment of the application discloses a banking website recommending method and device, which can be applied to the field of artificial intelligence, the field of big data or the field of finance. The method comprises the following steps: acquiring a current target position of a target user; determining the banking outlets, the distance between the banking outlets and the target position of which meets the preset distance condition, as candidate banking outlets; determining, for each candidate banking outlet, traffic costs required to reach the candidate banking outlet from the target location; acquiring the current queuing number of the candidate banking outlets; the method comprises the steps of obtaining queuing number increase probability corresponding to a candidate bank website, wherein the queuing number increase probability is used for representing the probability of increasing the number of queuing people by a preset number after a preset time period, and the queuing number increase probability is determined according to the number of queuing people of the candidate bank website in a reference history period; and determining recommended banking sites from the candidate banking sites according to the traffic cost, the current queuing number and the queuing number increasing probability corresponding to the candidate banking sites.
Description
Technical Field
The application relates to the technical field of computers, in particular to a banking website recommending method and device.
Background
At present, before a user goes to an off-line banking website to transact related business, the user can know that the user is suitable for the off-line banking website through a website recommendation function provided by a mobile banking Application program (Application). When the mobile banking APP determines the banking website recommended to the user, the current position of the user is usually obtained, the banking website closest to the position is further queried, and the queried banking website is recommended to the user.
However, in practical application, the banking website recommended to the user determined by the method is often not a banking website capable of bringing better experience to the user, that is, the banking website recommended to the user is determined by only referring to the geographic position, and it is difficult to ensure the service experience brought by the recommended banking website to the user.
Disclosure of Invention
The embodiment of the application provides a banking website recommending method and device, which can enable the determined banking website recommended to a user to bring better service experience to the user.
In view of this, a first aspect of the present application provides a banking outlet recommendation method, the method comprising:
acquiring the current position of a target user as a target position;
according to the target position, determining a banking website with the distance between the banking website and the target position meeting the preset distance condition as a candidate banking website;
determining traffic cost required for reaching the candidate banking outlets from the target position as traffic cost corresponding to the candidate banking outlets for each candidate banking outlet; acquiring the current queuing number of the candidate banking outlets; the method comprises the steps of obtaining queuing number increase probability corresponding to the candidate bank website, wherein the queuing number increase probability is used for representing the probability of increasing the queuing number by a preset number after a preset time period, and the queuing number increase probability is determined according to the queuing number of the candidate bank website in a reference history time period;
and determining recommended banking sites from the candidate banking sites according to the traffic cost, the current queuing number and the queuing number increasing probability corresponding to each candidate banking site.
Optionally, the obtaining the probability of increasing the queuing number corresponding to the candidate banking website includes:
obtaining queuing numbers of the candidate banking outlets in all time units in the reference history period;
constructing a transition probability matrix according to queuing numbers of the candidate banking outlets in each time unit in the reference history period through a Markov chain algorithm;
based on the transition probability matrix, determining the queuing number increase probability corresponding to the candidate bank network according to the current queuing number of the candidate bank network.
Optionally, the determining, according to the traffic cost, the current queuing number, and the queuing number increase probability corresponding to each candidate banking website, a recommended banking website from each candidate banking website includes:
determining recommended banking sites from the candidate banking sites according to traffic cost, current queuing number, queuing number increase probability and business characteristics corresponding to the candidate banking sites through a decision tree regression algorithm; the business characteristics are determined according to the number of people queued by the candidate banking outlets in a reference history period.
Optionally, the determining the traffic cost required for reaching the candidate banking website from the target location, as the traffic cost corresponding to the candidate banking website, includes:
determining a cost of traffic time required to reach the candidate banking outlet from the target location in a target traffic manner, and determining a cost of traffic money required to reach the candidate banking outlet from the target location in the target traffic manner; and taking the traffic time cost and the traffic money cost as traffic costs corresponding to the candidate banking outlets.
Optionally, the determining, according to the traffic cost, the current queuing number, and the queuing number increase probability corresponding to each candidate banking website, a recommended banking website from each candidate banking website includes:
determining recommendation scores corresponding to the candidate banking outlets according to traffic cost, current queuing number, queuing number increase probability and business characteristics corresponding to the candidate banking outlets; the business characteristics are determined according to the queuing number of the candidate banking outlets in a reference history period;
determining the recommended banking outlets according to the recommendation scores corresponding to the candidate banking outlets;
the method further comprises the steps of:
and displaying each candidate banking website according to the recommendation score corresponding to each candidate banking website.
A second aspect of the present application provides a banking outlet recommendation device, the device comprising:
the position acquisition module is used for acquiring the current position of the target user and taking the current position as a target position;
the website initial selection module is used for determining a banking website, the distance between the banking website and the target position of which meets the preset distance condition, according to the target position, and the banking website is used as a candidate banking website;
the reference data determining module is used for determining traffic cost required for reaching the candidate banking outlets from the target position as traffic cost corresponding to the candidate banking outlets for each candidate banking outlet; acquiring the current queuing number of the candidate banking outlets; the method comprises the steps of obtaining queuing number increase probability corresponding to the candidate bank website, wherein the queuing number increase probability is used for representing the probability of increasing the queuing number by a preset number after a preset time period, and the queuing number increase probability is determined according to the queuing number of the candidate bank website in a reference history time period;
and the website recommending module is used for determining recommended banking websites from the candidate banking websites according to the traffic cost, the current queuing number and the queuing number increasing probability corresponding to each candidate banking website.
Optionally, the reference data determining module is specifically configured to:
obtaining queuing numbers of the candidate banking outlets in all time units in the reference history period;
constructing a transition probability matrix according to queuing numbers of the candidate banking outlets in each time unit in the reference history period through a Markov chain algorithm;
based on the transition probability matrix, determining the queuing number increase probability corresponding to the candidate bank network according to the current queuing number of the candidate bank network.
Optionally, the website recommendation module is specifically configured to:
determining recommended banking sites from the candidate banking sites according to traffic cost, current queuing number, queuing number increase probability and business characteristics corresponding to the candidate banking sites through a decision tree regression algorithm; the business characteristics are determined according to the number of people queued by the candidate banking outlets in a reference history period.
Optionally, the reference data determining module is specifically configured to:
determining a cost of traffic time required to reach the candidate banking outlet from the target location in a target traffic manner, and determining a cost of traffic money required to reach the candidate banking outlet from the target location in the target traffic manner; and taking the traffic time cost and the traffic money cost as traffic costs corresponding to the candidate banking outlets.
Optionally, the website recommendation module is specifically configured to:
determining recommendation scores corresponding to the candidate banking outlets according to traffic cost, current queuing number, queuing number increase probability and business characteristics corresponding to the candidate banking outlets; the business characteristics are determined according to the queuing number of the candidate banking outlets in a reference history period;
determining the recommended banking outlets according to the recommendation scores corresponding to the candidate banking outlets;
the apparatus further comprises:
and the website display module is used for displaying each candidate banking website according to the recommendation score corresponding to each candidate banking website.
From the above technical solutions, the embodiments of the present application have the following advantages:
the embodiment of the application provides a banking website recommending method, which comprises the following steps: acquiring the current position of a target user as a target position; then, according to the target position, determining the banking website of which the distance with the target position meets the preset distance condition as a candidate banking website; further, determining, for each candidate bank node, traffic cost required for reaching the candidate bank node from a target position as traffic cost corresponding to the candidate bank node, and acquiring current queuing number of the candidate bank node and queuing number increase probability corresponding to the candidate bank node, wherein the queuing number increase probability is used for representing probability of increasing the queuing number by a preset number after a energy preset time period, and is determined according to queuing number of the candidate bank node in a reference history time period; and finally, determining recommended banking sites from the candidate banking sites according to the traffic cost, the current queuing number and the queuing number increasing probability corresponding to each candidate banking site. According to the banking website recommending method, when the banking website recommended to the user is determined, the traffic cost required by the user to go to the banking website, the current queuing number of the banking website and the increasing probability of the queuing number corresponding to the banking website are comprehensively considered, so that the determined recommending website can be ensured to be more in line with the traffic requirement of the user, the determined recommending website can be ensured not to wait for too long time when the user handles the business, and better experience can be brought to the user by the recommending website.
Drawings
Fig. 1 is a schematic flow chart of a banking website recommending method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a banking website recommendation device according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will clearly and completely describe the technical solution in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the related art, the website recommending function provided by the mobile banking APP generally directly obtains the current position of the user, and then inquires the website recommending function closest to the position to recommend the website to the user. However, in many cases, the closest banking outlet may have many people queued and it is uncertain how many queued users will be newly added on the way to the banking outlet, so it is seen that the closest outlet may not be the best choice for the user experience.
In order to solve the above problems, an embodiment of the present application provides a banking website recommendation method, in which a current location of a target user is obtained as a target location; then, according to the target position, determining the banking website of which the distance with the target position meets the preset distance condition as a candidate banking website; further, determining, for each candidate bank node, traffic cost required for reaching the candidate bank node from a target position as traffic cost corresponding to the candidate bank node, and acquiring current queuing number of the candidate bank node and queuing number increase probability corresponding to the candidate bank node, wherein the queuing number increase probability is used for representing probability of increasing the queuing number by a preset number after a energy preset time period, and is determined according to queuing number of the candidate bank node in a reference history time period; and finally, determining recommended banking sites from the candidate banking sites according to the traffic cost, the current queuing number and the queuing number increasing probability corresponding to each candidate banking site. According to the banking website recommending method, when the banking website recommended to the user is determined, the traffic cost required by the user to go to the banking website, the current queuing number of the banking website and the increasing probability of the queuing number corresponding to the banking website are comprehensively considered, so that the determined recommending website can be ensured to be more in line with the traffic requirement of the user, the determined recommending website can be ensured not to wait for too long time when the user handles the business, and better experience can be brought to the user by the recommending website.
The banking website recommending method provided by the application is described in detail through the method embodiment.
Referring to fig. 1, fig. 1 is a flow chart of a banking outlet recommendation method according to an embodiment of the present application. For convenience of description, the following embodiments are described by taking a background server whose execution subject is a mobile banking APP as an example. As shown in fig. 1, the method includes:
step 101: and acquiring the current position of the target user as the target position.
In practical application, when detecting that the target user uses the website recommendation function of the mobile phone bank APP, the server may request the target user to grant permission to obtain the location information of the target user, and obtain the location information (such as longitude and latitude) of the mobile phone carrying the mobile phone bank APP, and take the location information as the current location of the target user, that is, the target location.
Step 102: and determining the banking website with the distance between the banking website and the target position meeting the preset distance condition according to the target position, and taking the banking website as a candidate banking website.
After the server obtains the current target position of the target user, the banking website with the distance meeting the preset distance condition between the server and the target position can be determined according to the target position and used as the candidate banking website.
The server may determine N banking outlets closest to the target location after acquiring the current target location of the target user, as candidate banking outlets; alternatively, the server may also use a banking node whose distance from the target location is less than a predetermined distance threshold as a candidate banking node.
Step 103: determining traffic cost required for reaching the candidate banking outlets from the target position as traffic cost corresponding to the candidate banking outlets for each candidate banking outlet; acquiring the current queuing number of the candidate banking outlets; the method comprises the steps of obtaining the queuing number increase probability corresponding to the candidate bank website, wherein the queuing number increase probability is used for representing the probability of increasing the queuing number by a preset number after a preset time period, and the queuing number increase probability is determined according to the queuing number of the candidate bank website in a reference history time period.
Further, for each candidate banking node determined by step 102, a traffic cost (e.g., a traffic time cost, a traffic money cost, etc.) required to reach the candidate banking node from the target location where the target user is located may be determined; and the current queuing number of the candidate banking website can also be obtained; and the queuing number increase probability corresponding to the candidate bank website can be obtained, the queuing number increase probability can represent the probability of increasing the preset number of queuing numbers after the preset time period is elapsed, and the queuing number increase probability is determined according to the queuing numbers of the candidate bank website in the reference history time period.
When determining the traffic cost corresponding to the candidate bank website, the server can determine the traffic time cost required by the target traffic mode from the target position to the candidate bank website and determine the traffic money cost required by the target traffic mode from the target position to the candidate bank website; and the traffic time cost and the traffic money cost are used as traffic costs corresponding to the candidate banking outlets. For example, the server may determine the user-selected traffic pattern as a target traffic pattern, including but not limited to taking buses, driving, self-driving, and walking; then, the server can determine the time required by the target user from the target position of the target user to the candidate banking website by adopting the target traffic mode aiming at each candidate banking website as the traffic time cost corresponding to the candidate banking website; determining expenditure required by a target user from a target position where the target user is located to the candidate bank website by adopting the target traffic mode, and taking the expenditure as traffic money cost corresponding to the candidate bank website; and taking the traffic time cost and the traffic money cost corresponding to the candidate banking website as the traffic cost corresponding to the candidate banking website.
In addition, the server also needs to obtain the current queuing number of each candidate banking website for the candidate banking website.
In addition, the server also needs to determine, for each candidate banking website, an increased probability of the number of queuing people corresponding to the candidate banking website. In particular, the server needs to first obtain the number of queuing people in each time unit of the candidate banking website in the reference history period, for example, obtain the number of queuing people in each hour of each day in the past month of the candidate banking website; then, constructing a transition probability matrix according to the queuing number of the candidate banking website in each time unit in the reference history period by a Markov chain algorithm, wherein each element in the transition probability matrix is used for representing the probability of transition from the queuing number i to the queuing number j after the preset time period is passed, namely the transition probability matrix comprises a plurality of elements P ij ，P ij ＝P(X t+1 ＝j|X t =i), where i is less than or equal to n, j is less than or equal to m, m and n can be set according to the actual situation; the probability transition matrix may be expressed as follows:
furthermore, the server may determine, according to the current queuing number of the candidate banking website, a probability that the number of people of the candidate banking website reaches a preset number after a preset time period is elapsed by using the probability transition matrix. For example, in the case that the current queuing number of the candidate banking website is 1, the server may determine, according to the element P12 in the probability transition matrix, a probability that the queuing number of the candidate banking website reaches two after a preset period of time; similarly, the server can determine the probability that the number of people in line in the candidate banking website reaches three, four, … …, m, etc.
Optionally, when the embodiment of the present application obtains the data for measuring candidate banking sites, some sensitive user data may be obtained, such as personal information of the user, bank account information, and the like, so as to avoid leakage of sensitive information caused by introducing the information in a process of determining recommended sites, the embodiment of the present application may further perform desensitization processing on the data after obtaining the data, so as to reject the sensitive user data from the obtained data.
Step 104: and determining recommended banking sites from the candidate banking sites according to the traffic cost, the current queuing number and the queuing number increasing probability corresponding to each candidate banking site.
After determining the traffic cost required by the target user to reach each candidate bank node, the current queuing number of each candidate bank node and the queuing number increase probability of each candidate bank node, the server can determine recommended bank nodes which can be recommended to the target user from each candidate bank node according to the traffic time cost and the traffic money cost required by the target user to reach each candidate bank node, the current queuing number of each candidate bank node and the queuing number increase probability of each candidate bank node.
In specific implementation, the server may use a decision tree CART (classification and regression tree) regression algorithm to determine the recommended bank node from the candidate bank nodes according to the traffic time cost and the traffic money cost corresponding to each candidate bank node, the current queuing number, the queuing number increase probability, and the business characteristics of the candidate bank node (i.e., determined according to the queuing number of each candidate bank node in each time unit in the reference history period).
Specifically, the server can regard traffic time cost, traffic money cost, queuing number increase probability and banking website business characteristics as reference indexes; and dividing the data under the reference index into a plurality of sections according to the data distribution of each candidate bank website under the reference index aiming at each reference index, wherein the data belonging to the same section can be regarded as the data of the same class. Furthermore, the server may calculate, for each reference index, a corresponding information gain, where the information gain is calculated in the following manner:
1) The empirical entropy H (D) is calculated for each reference index:
wherein D represents the number of all data under the reference index, C k Representing the data quantity of the kth candidate banking outlet under the reference index.
2) Calculating the empirical condition entropy H (D|A) of the reference index A:
wherein D is i Represents the data quantity of the ith class under the reference index A, D ik For D i And C k Is a union of (a) and (b).
3) Calculating the information gain G (D, a) of the reference index:
G(D,A)＝H(D)-H(D|A)
the corresponding reference index with the maximum information gain can be used as the root node of the decision tree; further, the root node of the next layer is selected from the rest of the reference indexes by combining the data under the reference indexes; and the like until the recommended banking outlets are determined according to the root node of the last layer.
In the process of determining the recommended website, the server can also determine the recommended score corresponding to each candidate banking website according to the traffic time cost and the traffic money cost corresponding to each candidate banking website, the current queuing number, the queuing number increasing probability and the business characteristics of the candidate banking website; selecting a plurality of candidate banking outlets with the corresponding recommendation scores ranked at the front from the candidate banking outlets as recommended banking outlets; and displaying each candidate banking website to the user according to the recommendation score corresponding to each recommended banking website.
In the banking website recommending method provided by the embodiment of the application, the current position of the target user is firstly obtained as the target position; then, according to the target position, determining the banking website of which the distance with the target position meets the preset distance condition as a candidate banking website; further, determining, for each candidate bank node, traffic cost required for reaching the candidate bank node from a target position as traffic cost corresponding to the candidate bank node, and acquiring current queuing number of the candidate bank node and queuing number increase probability corresponding to the candidate bank node, wherein the queuing number increase probability is used for representing probability of increasing the queuing number by a preset number after a energy preset time period, and is determined according to queuing number of the candidate bank node in a reference history time period; and finally, determining recommended banking sites from the candidate banking sites according to the traffic cost, the current queuing number and the queuing number increasing probability corresponding to each candidate banking site. According to the banking website recommending method, when the banking website recommended to the user is determined, the traffic cost required by the user to go to the banking website, the current queuing number of the banking website and the increasing probability of the queuing number corresponding to the banking website are comprehensively considered, so that the determined recommending website can be ensured to be more in line with the traffic requirement of the user, the determined recommending website can be ensured not to wait for too long time when the user handles the business, and better experience can be brought to the user by the recommending website.
The embodiment of the application also provides a banking website recommending device. Referring to fig. 2, fig. 2 is a schematic structural diagram of a banking outlet recommendation device provided in an embodiment of the present application, and as shown in fig. 2, the banking outlet recommendation device includes:
a location obtaining module 201, configured to obtain a location where a target user is currently located as a target location;
the website initial selection module 202 is configured to determine, according to the target position, a banking website whose distance from the target position meets a preset distance condition, as a candidate banking website;
a reference data determining module 203, configured to determine, for each candidate banking website, a traffic cost required for reaching the candidate banking website from the target location, as a traffic cost corresponding to the candidate banking website; acquiring the current queuing number of the candidate banking outlets; the method comprises the steps of obtaining queuing number increase probability corresponding to the candidate bank website, wherein the queuing number increase probability is used for representing the probability of increasing the queuing number by a preset number after a preset time period, and the queuing number increase probability is determined according to the queuing number of the candidate bank website in a reference history time period;
the website recommending module 204 is configured to determine a recommended website from the candidate banking websites according to the traffic cost, the current queuing number, and the queuing number increase probability corresponding to each candidate banking website.
Optionally, the reference data determining module 203 is specifically configured to:
obtaining queuing numbers of the candidate banking outlets in all time units in the reference history period;
constructing a transition probability matrix according to queuing numbers of the candidate banking outlets in each time unit in the reference history period through a Markov chain algorithm;
based on the transition probability matrix, determining the queuing number increase probability corresponding to the candidate bank network according to the current queuing number of the candidate bank network.
Optionally, the website recommendation module 204 is specifically configured to:
determining recommended banking sites from the candidate banking sites according to traffic cost, current queuing number, queuing number increase probability and business characteristics corresponding to the candidate banking sites through a decision tree regression algorithm; the business characteristics are determined according to the number of people queued by the candidate banking outlets in a reference history period.
Optionally, the reference data determining module 203 is specifically configured to:
determining a cost of traffic time required to reach the candidate banking outlet from the target location in a target traffic manner, and determining a cost of traffic money required to reach the candidate banking outlet from the target location in the target traffic manner; and taking the traffic time cost and the traffic money cost as traffic costs corresponding to the candidate banking outlets.
Optionally, the website recommendation module 204 is specifically configured to:
determining recommendation scores corresponding to the candidate banking outlets according to traffic cost, current queuing number, queuing number increase probability and business characteristics corresponding to the candidate banking outlets; the business characteristics are determined according to the queuing number of the candidate banking outlets in a reference history period;
determining the recommended banking outlets according to the recommendation scores corresponding to the candidate banking outlets;
the apparatus further comprises:
and the website display module is used for displaying each candidate banking website according to the recommendation score corresponding to each candidate banking website.
When the banking website recommending device determines the banking website recommended to the user, traffic cost required by the user to go to the banking website, the current queuing number of the banking website and the increasing probability of the queuing number corresponding to the banking website are comprehensively considered, so that the determined recommending website can be ensured to be more in line with the traffic requirement of the user, the determined recommending website can be ensured not to wait for too long time when the user handles the business, and better experience can be brought to the user by the recommending website.
It should be noted that the banking website recommendation method and device provided by the invention can be used in the artificial intelligence field, the big data field or the financial field. The foregoing is merely an example, and the application fields of the banking website recommendation method and the banking website recommendation device provided by the invention are not limited.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc. various media for storing computer program.
It should be understood that in this application, "at least one" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A banking outlet recommendation method, the method comprising:
acquiring the current position of a target user as a target position;
according to the target position, determining a banking website with the distance between the banking website and the target position meeting the preset distance condition as a candidate banking website;
determining traffic cost required for reaching the candidate banking outlets from the target position as traffic cost corresponding to the candidate banking outlets for each candidate banking outlet; acquiring the current queuing number of the candidate banking outlets; the method comprises the steps of obtaining queuing number increase probability corresponding to the candidate bank website, wherein the queuing number increase probability is used for representing the probability of increasing the queuing number by a preset number after a preset time period, and the queuing number increase probability is determined according to the queuing number of the candidate bank website in a reference history time period;
and determining recommended banking sites from the candidate banking sites according to the traffic cost, the current queuing number and the queuing number increasing probability corresponding to each candidate banking site.
2. The method of claim 1, wherein the obtaining the increased probability of the number of people in line corresponding to the candidate banking outlets comprises:
obtaining queuing numbers of the candidate banking outlets in all time units in the reference history period;
constructing a transition probability matrix according to queuing numbers of the candidate banking outlets in each time unit in the reference history period through a Markov chain algorithm;
based on the transition probability matrix, determining the queuing number increase probability corresponding to the candidate bank network according to the current queuing number of the candidate bank network.
3. The method of claim 1, wherein said determining recommended banking outlets from each of said candidate banking outlets based on respective corresponding traffic costs, current queuing numbers, and increased probability of queuing numbers, comprises:
determining recommended banking sites from the candidate banking sites according to traffic cost, current queuing number, queuing number increase probability and business characteristics corresponding to the candidate banking sites through a decision tree regression algorithm; the business characteristics are determined according to the number of people queued by the candidate banking outlets in a reference history period.
4. The method of claim 1, wherein the determining the cost of traffic required to reach the candidate banking outlet from the target location as the corresponding cost of traffic for the candidate banking outlet comprises:
determining a cost of traffic time required to reach the candidate banking outlet from the target location in a target traffic manner, and determining a cost of traffic money required to reach the candidate banking outlet from the target location in the target traffic manner; and taking the traffic time cost and the traffic money cost as traffic costs corresponding to the candidate banking outlets.
5. The method of claim 1, wherein said determining recommended banking outlets from each of said candidate banking outlets based on respective corresponding traffic costs, current queuing numbers, and increased probability of queuing numbers, comprises:
determining recommendation scores corresponding to the candidate banking outlets according to traffic cost, current queuing number, queuing number increase probability and business characteristics corresponding to the candidate banking outlets; the business characteristics are determined according to the queuing number of the candidate banking outlets in a reference history period;
determining the recommended banking outlets according to the recommendation scores corresponding to the candidate banking outlets;
the method further comprises the steps of:
and displaying each candidate banking website according to the recommendation score corresponding to each candidate banking website.
6. A banking outlet recommendation device, the device comprising:
the position acquisition module is used for acquiring the current position of the target user and taking the current position as a target position;
the website initial selection module is used for determining a banking website, the distance between the banking website and the target position of which meets the preset distance condition, according to the target position, and the banking website is used as a candidate banking website;
the reference data determining module is used for determining traffic cost required for reaching the candidate banking outlets from the target position as traffic cost corresponding to the candidate banking outlets for each candidate banking outlet; acquiring the current queuing number of the candidate banking outlets; the method comprises the steps of obtaining queuing number increase probability corresponding to the candidate bank website, wherein the queuing number increase probability is used for representing the probability of increasing the queuing number by a preset number after a preset time period, and the queuing number increase probability is determined according to the queuing number of the candidate bank website in a reference history time period;
and the website recommending module is used for determining recommended banking websites from the candidate banking websites according to the traffic cost, the current queuing number and the queuing number increasing probability corresponding to each candidate banking website.
7. The apparatus of claim 6, wherein the reference data determination module is specifically configured to:
obtaining queuing numbers of the candidate banking outlets in all time units in the reference history period;
constructing a transition probability matrix according to queuing numbers of the candidate banking outlets in each time unit in the reference history period through a Markov chain algorithm;
based on the transition probability matrix, determining the queuing number increase probability corresponding to the candidate bank network according to the current queuing number of the candidate bank network.
8. The apparatus of claim 6, wherein the website recommendation module is specifically configured to:
determining recommended banking sites from the candidate banking sites according to traffic cost, current queuing number, queuing number increase probability and business characteristics corresponding to the candidate banking sites through a decision tree regression algorithm; the business characteristics are determined according to the number of people queued by the candidate banking outlets in a reference history period.
9. The apparatus of claim 6, wherein the reference data determination module is specifically configured to:
determining a cost of traffic time required to reach the candidate banking outlet from the target location in a target traffic manner, and determining a cost of traffic money required to reach the candidate banking outlet from the target location in the target traffic manner; and taking the traffic time cost and the traffic money cost as traffic costs corresponding to the candidate banking outlets.
10. The apparatus of claim 6, wherein the website recommendation module is specifically configured to:
determining recommendation scores corresponding to the candidate banking outlets according to traffic cost, current queuing number, queuing number increase probability and business characteristics corresponding to the candidate banking outlets; the business characteristics are determined according to the queuing number of the candidate banking outlets in a reference history period;
determining the recommended banking outlets according to the recommendation scores corresponding to the candidate banking outlets;
the apparatus further comprises:
and the website display module is used for displaying each candidate banking website according to the recommendation score corresponding to each candidate banking website.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104951979A (en) * | 2015-06-06 | 2015-09-30 | 浙江维融电子科技股份有限公司 | Bank branch recommendation method |
| CN111639271A (en) * | 2020-06-04 | 2020-09-08 | 中国银行股份有限公司 | Network information pushing method and device |
| CN112116116A (en) * | 2020-09-29 | 2020-12-22 | 中国银行股份有限公司 | Bank outlet recommendation method and device |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104951979A (en) * | 2015-06-06 | 2015-09-30 | 浙江维融电子科技股份有限公司 | Bank branch recommendation method |
| CN111639271A (en) * | 2020-06-04 | 2020-09-08 | 中国银行股份有限公司 | Network information pushing method and device |
| CN112116116A (en) * | 2020-09-29 | 2020-12-22 | 中国银行股份有限公司 | Bank outlet recommendation method and device |
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