CN111143714A - Recommendation sorting method and system, electronic device and readable storage medium - Google Patents
Recommendation sorting method and system, electronic device and readable storage medium Download PDFInfo
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Abstract
The invention discloses a recommendation sequencing method, a recommendation sequencing system, electronic equipment and a readable storage medium, wherein the method comprises the following steps: acquiring customer characteristic information, wherein the customer characteristic information is used for representing basic information of a customer; acquiring business circle characteristic information, wherein the business circle characteristic information is used for representing basic information of a business circle; acquiring relationship characteristic information between business circles, wherein the relationship characteristic information between the business circles is used for representing association information between the business circles; and generating recommendation sequencing information according to the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts. The method comprises the steps of firstly, obtaining customer characteristic information, business district characteristic information and relation characteristic information among business districts; and then, the multiple information is combined, the multiple information is integrated to generate recommendation sequencing information, so that the recommendation sequencing information is more complete, the matching relationship between the client and the business district is better mined by introducing multiple characteristics, and the accuracy of the recommendation sequencing information is improved.
Description
Technical Field
The invention relates to the technical field of intelligent recommendation, in particular to a recommendation sorting method, a recommendation sorting system, electronic equipment and a readable storage medium.
Background
With the rapid development of internet technology, Location-Based services (LBS) can conveniently acquire various information around the Location where a user is located, so that people's life is more and more convenient.
The recommendation function under the current LBS house renting scene is divided into a business district searching person and a house source searching person, a client with corresponding requirements is matched according to the basic attribute of the business district/house source to obtain a recommendation list, and then a salesperson can recommend corresponding sales information to the client. However, the sorting characteristics of the recommendation list are single, and only the positions/prices of the trade circle, the positions/prices of the house sources and the house states (area, exclusive guard, etc.), and the new and old degrees of the customers make the sorting strategy not perfect enough, and the accurate recommendation list cannot be provided for the customers.
Disclosure of Invention
In view of this, embodiments of the present invention provide a recommendation sorting method, a recommendation sorting system, an electronic device, and a readable storage medium, so as to solve the problem in the prior art that a recommendation list of a business turn finder is not accurate enough.
Therefore, the embodiment of the invention provides the following technical scheme:
according to a first aspect, an embodiment of the present invention provides a recommendation ranking method, including: acquiring customer characteristic information, wherein the customer characteristic information is used for representing basic information of a customer; acquiring business circle characteristic information, wherein the business circle characteristic information is used for representing basic information of a business circle; acquiring relationship characteristic information between business circles, wherein the relationship characteristic information between the business circles is used for representing association information between the business circles; and generating recommendation sequencing information according to the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts.
Optionally, generating recommendation ranking information according to the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts includes: grading and grading the customer characteristic information, the business circle characteristic information and the relation characteristic information among the business circles respectively to obtain grading results of the customers and the business circles; and sorting the grading results to obtain recommended sorting information.
Optionally, before the step of generating recommendation ranking information according to the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts, the method further includes: and acquiring preset weight information of the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts.
Optionally, before the step of generating recommendation ranking information according to the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts, the method further includes: and acquiring cold start rule information.
Optionally, before the step of generating recommendation ranking information according to the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts, the method further includes: and acquiring service tendency information.
Optionally, before the step of generating recommendation ranking information according to the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts, the method further includes: acquiring cold start weight corresponding to the cold start rule information; and/or acquiring the service tendency weight corresponding to the service tendency information.
Optionally, the client characteristic information includes: the system comprises client business district information, client preference information, client official network access information and client new and old degree information;
and/or the presence of a gas in the gas,
the business district feature information includes: business index information of a business district, traffic index information of the business district, standard price information of the business district, dynamic sales information of the business district, house source inventory information of the business district, high-storage-age house source proportion information in the business district and heat information of the business district;
and/or the presence of a gas in the gas,
the relationship characteristic information between the business circles comprises: the system comprises commuting distance information between business circles, traffic information between business circles, peripheral facility information and house source price information of the business circles.
According to a second aspect, an embodiment of the present invention provides a recommendation ranking system, including: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring client characteristic information which is used for representing basic information of a client; the second acquisition module is used for acquiring the characteristic information of the business district, and the characteristic information of the business district is used for representing the basic information of the business district; the third acquisition module is used for acquiring the relationship characteristic information between business circles, and the relationship characteristic information between the business circles is used for representing the association information between the business circles; and the processing module is used for generating recommendation sequencing information according to the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts.
Optionally, the processing module includes: the first processing unit is used for grading and grading the customer characteristic information, the business circle characteristic information and the relation characteristic information among the business circles respectively to obtain grading results of the customers and the business circles; and the second processing unit is used for sequencing the scoring results to obtain recommended sequencing information.
Optionally, the method further comprises: and the fourth acquisition module is used for acquiring the customer characteristic information, the business district characteristic information and preset weight information of the relation characteristic information among the business districts.
Optionally, the method further comprises: and the fifth acquisition module is used for acquiring the cold start rule information.
Optionally, the method further comprises: and the sixth acquisition module is used for acquiring the service tendency information.
Optionally, the method further comprises: a seventh obtaining module, configured to obtain a cold start weight corresponding to the cold start rule information; and/or the eighth obtaining module is configured to obtain the service tendency weight corresponding to the service tendency information.
Optionally, the client characteristic information includes: the system comprises client business district information, client preference information, client official network access information and client new and old degree information;
and/or the presence of a gas in the gas,
the business district feature information includes: business index information of a business district, traffic index information of the business district, standard price information of the business district, dynamic sales information of the business district, house source inventory information of the business district, high-storage-age house source proportion information in the business district and heat information of the business district;
and/or the presence of a gas in the gas,
the relationship characteristic information between the business circles comprises: the system comprises commuting distance information between business circles, traffic information between business circles, peripheral facility information and house source price information of the business circles.
According to a third aspect, an embodiment of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the recommendation ranking method of any of the above first aspects.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause a computer to execute the recommendation ranking method according to any one of the first aspect above.
The technical scheme of the embodiment of the invention has the following advantages:
the embodiment of the invention provides a recommendation sequencing method, a recommendation sequencing system, electronic equipment and a readable storage medium, wherein the method comprises the following steps: acquiring customer characteristic information, wherein the customer characteristic information is used for representing basic information of a customer; acquiring business circle characteristic information, wherein the business circle characteristic information is used for representing basic information of a business circle; acquiring relationship characteristic information between business circles, wherein the relationship characteristic information between the business circles is used for representing association information between the business circles; and generating recommendation sequencing information according to the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts. The method comprises the steps of firstly, obtaining customer characteristic information, business district characteristic information and relation characteristic information among business districts; and then, the multiple information is combined, the multiple information is integrated to generate recommendation sequencing information, so that the recommendation sequencing information is more complete, the matching relationship between the client and the business district is better mined by introducing multiple characteristics, and the accuracy of the recommendation sequencing information is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of a recommendation ranking method according to an embodiment of the present invention;
FIG. 2 is a flowchart of another specific example of a recommendation ranking method according to an embodiment of the present invention;
FIG. 3 is a flowchart of another specific example of a recommendation ranking method according to an embodiment of the present invention;
FIG. 4 is a flowchart of another specific example of a recommendation ranking method according to an embodiment of the present invention;
FIG. 5 is a flowchart of another specific example of a recommendation ranking method according to an embodiment of the present invention;
FIG. 6 is a flowchart of another specific example of a recommendation ranking method according to an embodiment of the present invention;
FIG. 7 is a block diagram of one specific example of a recommendation ranking system in accordance with an embodiment of the present invention;
fig. 8 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In a recommended ordering scheme under the current LBS house renting scene, ordering characteristics for ordering strategies are a business circle position/price, a house source position/price and a house state (area, exclusive guard and the like), and the new and old degrees of customers; the ranking strategy is to score the business circle grade, price difference, house state and new and old labels of the clients, the used characteristics are less, the scoring strategy is not perfect, and the recommendation list is not accurate enough. Therefore, in the embodiment, the specific characteristics and the actual characteristics of the business circle for finding people under the LBS renting scene are combined, various characteristics are added, comprehensive sequencing scoring is carried out based on the various characteristics, the optimal matching between the business circle and the client is mined, and a more accurate recommendation list is obtained.
Based on this, the embodiment of the present invention provides a recommendation ranking method, which is applied in LBS renting scenarios, as shown in fig. 1, the method may include steps S1-S4.
Step S1: and acquiring customer characteristic information, wherein the customer characteristic information is used for representing basic information of a customer.
As an exemplary embodiment, the customer characteristic information includes: customer business district information, customer preference information, customer official website access information and customer new and old degree information. In other exemplary embodiments, the client characteristic information may further include professional information of the client, age information of the client, and the like, and this embodiment is only illustrative and not limited thereto.
Specifically, the customer business district information includes the customer's resident business district information, and intention business district information. According to the description of the embodiment, the customer business district information may also include other information, and the embodiment is only described schematically and is not limited thereto.
The customer preference information includes a preferred price level. According to the description of the embodiment, the customer preference information may also include other information, and the embodiment is only described schematically and is not limited thereto.
The client official website access information comprises the official website activity degree of the client, the number of days of the latest active time from the current day and whether the client is an official website high-activity user. According to the description of the embodiment, the customer official website access information may also include other information, and the embodiment is only described schematically and is not limited thereto.
The client new and old degree information comprises an old client and a new client, and can be determined according to the time when the client joins the official website. According to the description of the embodiment, a person skilled in the art may also determine the new and old degree information of the client by using other manners in the prior art, and the embodiment is only described schematically and is not limited thereto.
Step S2: and acquiring the characteristic information of the business circle, wherein the characteristic information of the business circle is used for representing the basic information of the business circle.
As an exemplary embodiment, the business district feature information includes: business index information of a business district, traffic index information of the business district, standard price information of the business district, dynamic sales information of the business district, house source inventory information of the business district, high-storage-age house source proportion information in the business district and heat information of the business district. In other exemplary embodiments, the business district feature information may further include a competition index of the business district, a population gathering index of the business district, and the like, and this embodiment is only illustrative and not limited thereto.
Specifically, the business turn traffic index information may include a traffic congestion index or a traffic operation index, which is a conceptual index value comprehensively reflecting the smoothness or congestion of the road network. The business district popularity information indicates whether the business district belongs to a section with high popularity in the current city.
Step S3: and obtaining the relation characteristic information between the business circles, wherein the relation characteristic information between the business circles is used for representing the association information between the business circles.
As an exemplary embodiment, the relationship characteristic information between business circles includes: the system comprises commuting distance information between business circles, traffic information between business circles, peripheral facility information and house source price information of the business circles.
Specifically, the commuting distance information between the business circles comprises the spatial linear distance between the business circles. The traffic information between the business circles includes the traffic manner (e.g., the number of times and time of subway transfer, etc.) between the business circles. The peripheral facility information includes various facilities such as transportation, medical treatment, education, living facilities, and the like. The commercial housing source price information includes a price difference between the standard price and the lowest price.
Step S4: and generating recommendation sequencing information according to the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts.
As an exemplary embodiment, the recommendation ranking information may be obtained according to the customer characteristic information, the business district characteristic information, the relationship characteristic information between business districts, and their respective weights. Those skilled in the art should understand that the generation of the recommendation ranking information according to the various information and the weight occupied by each information is not used to limit the embodiment.
In this embodiment, the recommendation sorting information may be a recommendation list, such as a business turn-customer sequence, which is only used as an example and not limited thereto.
Through the steps, the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts are obtained firstly; and then, the multiple information is combined, the multiple information is integrated to generate recommendation sequencing information, so that the recommendation sequencing information is more complete, the matching relationship between the client and the business district is better mined by introducing multiple characteristics, and the accuracy of the recommendation sequencing information is improved.
As an exemplary embodiment, the step S4 of generating recommendation ranking information according to the customer characteristic information, the business district characteristic information and the relationship characteristic information between business districts includes steps S41-S42, as shown in fig. 2.
Step S41: and respectively grading and grading the customer characteristic information, the business circle characteristic information and the relation characteristic information between the business circles to obtain grading results of the customers and the business circles.
As an exemplary embodiment, each feature information includes a plurality of features, each feature in each feature information is graded, different grades correspond to different scores according to a preset scoring standard, and then the scores corresponding to the features are added to obtain a scoring result of the customer and the business district, which is only taken as an example and not limited thereto. Specifically, the preset scoring criteria may be set based on empirical values, such as a customer's new or old degree of grading into: 1 point is obtained by 1 day of the accumulated login days of the user; the cumulative login day 2 day score was 0.8; the cumulative login day number is more than or equal to 3 days and the score is 0.7; as another example, the user activity is graded as: the active user score is 1 score and the inactive user score is 0 score.
Step S42: and sorting the grading results to obtain recommended sorting information.
As an exemplary embodiment, the ranking is performed according to the score of the scoring result, and a recommendation ranking list is obtained after the ranking, and the recommendation ranking list can be used as recommendation ranking information.
Through the steps, the customer characteristic information, the business circle characteristic information and the relation characteristic information among the business circles are graded and graded respectively, and the recommendation sequencing information is generated according to grading results of the grading and grading, so that the recommendation sequencing information better meets the customer requirements.
As an exemplary embodiment, before the step of generating recommendation ranking information according to the customer characteristic information, the business district characteristic information and the relationship characteristic information between business districts in step S4, as shown in fig. 3, a step S5 is further included.
Step S5: and acquiring preset weight information of the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts.
Specifically, each of the plurality of types of feature information corresponds to a weight value, and the weight values constitute preset weight information. The preset weight information may be preset according to requirements, for example, the preset weight value of the customer characteristic information is a, the preset weight value of the business district characteristic information is b, the preset weight value of the relation characteristic information between the business districts is c, the values of a, b and c are all numerical values greater than 0 and smaller than 1, and the sum of a + b + c is 1. The present embodiment is described only schematically, and is not limited thereto.
In this case, in step S4, the recommended ranking information is generated according to the preset weight information, the customer characteristic information, the business district characteristic information, and the relationship characteristic information between the business districts of the acquired characteristic information.
Through the steps, the weight of each characteristic information is introduced, the proportion of each characteristic information is reasonably set according to the requirement, the importance degree of each characteristic information can be flexibly adjusted, and the optimized recommendation ranking information is obtained.
As an exemplary embodiment, before the step of generating recommendation ranking information according to the customer characteristic information, the business district characteristic information and the relationship characteristic information between business districts in step S4, as shown in fig. 4, a step S6 is further included.
Step S6: and acquiring cold start rule information.
Specifically, the cold start rule information includes employing a rule-based recommendation policy; according to the scoring data of the client demand, through similar neighbor query, the user group most similar to the interest preference of the current client is found, and the most interesting recommendation list is provided for the current client according to the preference information of the user groups.
At this time, step S4 is to generate recommendation ranking information according to the cold start rule information, the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts.
By adding cold start rule information, the sequencing strategy is further perfected, and more accurate recommended sequencing information is obtained.
As an exemplary embodiment, before the step of generating recommendation ranking information according to the customer characteristic information, the business district characteristic information and the relationship characteristic information between the business districts in step S4, as shown in fig. 5, a step S7 is further included.
Step S7: and acquiring service tendency information.
Specifically, the business tendency information mainly includes that the weights of certain business circles are increased according to stock removal, drainage and the like.
At this time, step S4 is to generate recommendation ranking information according to the business tendency information, the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts.
Through the steps, the service tendency information is added, the sequencing strategy is further perfected, and the sequencing information is recommended more accurately.
As an exemplary embodiment, before the step of generating recommendation ranking information according to the customer characteristic information, the business district characteristic information and the relationship characteristic information between business districts in step S4, as shown in fig. 6, steps S8-S9 are further included.
Step S8: and acquiring cold start weight corresponding to the cold start rule information. Specifically, the cold start weight may be preset, and may be reasonably set according to actual needs, for example, and is not limited to 0.5. And if the proportion of the cold start rule information needs to be increased, the cold start weight is increased.
Step S9: and acquiring the service tendency weight corresponding to the service tendency information. Specifically, the service tendency weight may be preset, and may be reasonably set according to actual needs, for example, 0.3, which is only taken as an example and not limited thereto. And if the proportion of the service tendency information needs to be increased, the service tendency weight is increased.
In this case, it is necessary to obtain preset weight information of the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts, a cold start weight corresponding to the cold start rule information, and a business tendency weight corresponding to the business tendency information, respectively. Specifically, each of the plurality of pieces of information corresponds to a weight value, and the weight values constitute weight information. The weight information may be preset according to a requirement, for example, a preset weight value of the customer characteristic information is a, a preset weight value of the business district characteristic information is b, a preset weight value of the relation characteristic information between the business districts is c, a cold start weight value of the cold start rule information is d, a business tendency weight value of the business tendency information is e, values of a, b, c, d and e are numerical values greater than 0 and smaller than 1, and the sum of a + b + c + d + e is 1. The present embodiment is described only schematically, and is not limited thereto.
Step S4 is to generate recommendation ranking information according to preset weight information, cold start weight, business tendency weight, cold start rule information, business tendency information, customer feature information, business district feature information, relationship feature information between business districts, cold start rule information, and business tendency information.
Specifically, scoring is carried out according to the customer characteristic information, the business circle characteristic information, the relation characteristic information among business circles, the cold start rule information, the business tendency information and the weight information corresponding to each characteristic information, and recommendation list information is determined according to a scoring result.
A detailed description will be given below with a specific example, listing the scoring calculation manner based on the above information.
Through the steps, the ranking characteristics in the prior art are subjected to characteristic expansion, a more complete scoring strategy is formed by integrating various characteristic factors, the optimal matching of the business circles and the clients is realized, the optimized business circle-client recommended ranking information is obtained, and the accuracy of the recommended ranking information is improved.
The embodiment also provides a recommendation sorting system, which is used for implementing the above embodiments and preferred embodiments, and the description of the system already made is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
The present embodiment further provides a recommendation ranking system, as shown in fig. 7, including: a first acquisition module 71, a second acquisition module 72, a third acquisition module 73 and a processing module 74.
A first obtaining module 71, configured to obtain client characteristic information, where the client characteristic information is used to represent basic information of a client; the details are described with reference to step S1.
The second obtaining module 72 is configured to obtain business district feature information, where the business district feature information is used to represent basic information of a business district; the details are described with reference to step S2.
The third obtaining module 73 is configured to obtain relationship characteristic information between business circles, where the relationship characteristic information between business circles is used to represent association information between business circles; the details are described with reference to step S3.
The processing module 74 is configured to generate recommendation ranking information according to the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts; the details are described with reference to step S4.
As an exemplary embodiment, the processing module includes: the first processing unit is configured to respectively grade and grade the customer characteristic information, the business circle characteristic information, and the relationship characteristic information between the business circles to obtain grading results of the customers and the business circles, and the detailed contents refer to step S41; and a second processing unit, configured to rank the scoring results to obtain recommendation ranking information, where the detailed content refers to step S42.
As an exemplary embodiment, the recommendation ranking system further comprises: the fourth acquisition module is used for acquiring the preset weight information of the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts; the details are described with reference to step S5.
As an exemplary embodiment, the recommendation ranking system further comprises: the fifth acquisition module is used for acquiring cold start rule information; the details are described with reference to step S6.
As an exemplary embodiment, the recommendation ranking system further comprises: a sixth obtaining module, configured to obtain service tendency information; the details are described with reference to step S7.
As an exemplary embodiment, the recommendation ranking system further comprises: a seventh obtaining module, configured to obtain a cold start weight corresponding to the cold start rule information, where the detailed content refers to that in step S8; and/or, an eighth obtaining module, configured to obtain a service tendency weight corresponding to the service tendency information, where the details are described with reference to step S9.
As an exemplary embodiment, the customer characteristic information includes: the system comprises client business district information, client preference information, client official network access information and client new and old degree information;
and/or the presence of a gas in the gas,
the business district characteristic information comprises: business index information of a business district, traffic index information of the business district, standard price information of the business district, dynamic sales information of the business district, house source inventory information of the business district, high-storage-age house source proportion information in the business district and heat information of the business district;
and/or the presence of a gas in the gas,
the relationship characteristic information between the business circles comprises: the system comprises commuting distance information between business circles, traffic information between business circles, peripheral facility information and house source price information of the business circles.
The recommendation ranking system in this embodiment is presented in the form of functional units, where a unit refers to an ASIC circuit, a processor and memory executing one or more software or fixed programs, and/or other devices that may provide the above functionality.
Further functional descriptions of the modules are the same as those of the corresponding embodiments, and are not repeated herein.
An embodiment of the present invention further provides an electronic device, as shown in fig. 8, the electronic device includes one or more processors 81 and a memory 82, where one processor 81 is taken as an example in fig. 8.
The controller may further include: an input device 83 and an output device 84.
The processor 81, the memory 82, the input device 83 and the output device 84 may be connected by a bus or other means, and fig. 8 illustrates the connection by a bus as an example.
The memory 82, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the recommendation ranking method in the embodiments of the present application. The processor 81 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 82, i.e. implements the recommended ranking method of the above-described method embodiments.
The memory 82 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of a processing device operated by the server, and the like. Further, the memory 82 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 82 may optionally include memory located remotely from the processor 81, which may be connected to a network connection device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 83 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the processing device of the server. The output device 84 may include a display device such as a display screen.
One or more modules are stored in the memory 82 and, when executed by the one or more processors 81, perform the recommendation ranking method as shown in fig. 1-6.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by instructing relevant hardware through a computer program, and the executed program may be stored in a computer-readable storage medium, and when executed, may include the processes of the above embodiments of the recommended sorting method. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A recommendation ranking method, comprising:
acquiring customer characteristic information, wherein the customer characteristic information is used for representing basic information of a customer;
acquiring business circle characteristic information, wherein the business circle characteristic information is used for representing basic information of a business circle;
acquiring relationship characteristic information between business circles, wherein the relationship characteristic information between the business circles is used for representing association information between the business circles;
and generating recommendation sequencing information according to the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts.
2. The recommendation ranking method according to claim 1, wherein generating recommendation ranking information based on the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts includes:
grading and grading the customer characteristic information, the business circle characteristic information and the relation characteristic information among the business circles respectively to obtain grading results of the customers and the business circles;
and sorting the grading results to obtain recommended sorting information.
3. The recommendation ranking method according to claim 1, wherein, before the step of generating recommendation ranking information based on the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts, further comprising:
and acquiring preset weight information of the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts.
4. The recommendation ranking method according to claim 1, wherein, before the step of generating recommendation ranking information based on the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts, further comprising:
and acquiring cold start rule information.
5. The recommendation ranking method according to claim 1, wherein, before the step of generating recommendation ranking information based on the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts, further comprising:
and acquiring service tendency information.
6. The recommendation ranking method according to claim 4 or 5, wherein, before the step of generating recommendation ranking information according to the customer characteristic information, the business district characteristic information, and the relationship characteristic information between business districts, further comprising:
acquiring cold start weight corresponding to the cold start rule information;
and/or the presence of a gas in the gas,
and acquiring the service tendency weight corresponding to the service tendency information.
7. The recommendation ranking method according to any one of claims 1-6,
the client characteristic information includes: the system comprises client business district information, client preference information, client official network access information and client new and old degree information;
and/or the presence of a gas in the gas,
the business district feature information includes: business index information of a business district, traffic index information of the business district, standard price information of the business district, dynamic sales information of the business district, house source inventory information of the business district, high-storage-age house source proportion information in the business district and heat information of the business district;
and/or the presence of a gas in the gas,
the relationship characteristic information between the business circles comprises: the system comprises commuting distance information between business circles, traffic information between business circles, peripheral facility information and house source price information of the business circles.
8. A recommendation ranking system, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring client characteristic information which is used for representing basic information of a client;
the second acquisition module is used for acquiring the characteristic information of the business district, and the characteristic information of the business district is used for representing the basic information of the business district;
the third acquisition module is used for acquiring the relationship characteristic information between business circles, and the relationship characteristic information between the business circles is used for representing the association information between the business circles;
and the processing module is used for generating recommendation sequencing information according to the customer characteristic information, the business district characteristic information and the relation characteristic information among the business districts.
9. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the recommendation ranking method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a computer to perform the recommendation ranking method of any one of claims 1-7.
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| CN201911359446.2A CN111143714A (en) | 2019-12-25 | 2019-12-25 | Recommendation sorting method and system, electronic device and readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109902713A (en) * | 2019-01-17 | 2019-06-18 | 平安城市建设科技(深圳)有限公司 | Building recommended method, equipment, storage medium and device based on data analysis |
| CN109949123A (en) * | 2019-02-12 | 2019-06-28 | 平安科技（深圳）有限公司 | Source of houses recommended method, device, computer equipment and computer readable storage medium |
| CN110162711A (en) * | 2019-05-28 | 2019-08-23 | 湖北大学 | A kind of resource intelligent recommended method and system based on internet startup disk method |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109902713A (en) * | 2019-01-17 | 2019-06-18 | 平安城市建设科技(深圳)有限公司 | Building recommended method, equipment, storage medium and device based on data analysis |
| CN109949123A (en) * | 2019-02-12 | 2019-06-28 | 平安科技（深圳）有限公司 | Source of houses recommended method, device, computer equipment and computer readable storage medium |
| CN110162711A (en) * | 2019-05-28 | 2019-08-23 | 湖北大学 | A kind of resource intelligent recommended method and system based on internet startup disk method |
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Application publication date: 20200512 |