CN108182608B - Electronic device, product recommendation method, and computer-readable storage medium - Google Patents
Electronic device, product recommendation method, and computer-readable storage medium Download PDFInfo
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- CN108182608B CN108182608B CN201810091244.3A CN201810091244A CN108182608B CN 108182608 B CN108182608 B CN 108182608B CN 201810091244 A CN201810091244 A CN 201810091244A CN 108182608 B CN108182608 B CN 108182608B
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Abstract
The invention discloses an electronic device, a product recommendation method and a computer-readable storage medium, wherein the product recommendation method comprises the following steps: acquiring a member score corresponding to the member, and determining a score floating interval; screening products with the product scores in the score floating interval; acquiring the willingness investment amount range of the member, determining the amount limit range corresponding to the member, and comparing the amount limit range and the amount limit range; if part or all of the range of the willful investment amount is within the amount limit range, determining the amount intersection range of the willful investment amount and the amount limit range, and extracting the product of which the total issued amount is within the amount intersection range from the screened products; if the wished investment sum range is out of the sum limit range, extracting products of which the total issuing sum is within the sum limit range from the screened products; pushing the extracted product to the member. The technical scheme of the invention ensures that the products pushed by the transaction platform to the client are more targeted.
Description
Technical Field
The present invention relates to the field of online trading platforms, and in particular, to an electronic device, a product recommendation method, and a computer-readable storage medium.
Background
At present, for a trading platform between enterprises and institutions, all products on shelves on the trading platform are generally displayed to platform members (enterprise clients) according to the time sequence, and the products are summarized and treated without distinction regardless of the scale, size, nature and the like of the enterprises. The enterprise customers have different requirements on product investment or financing due to enterprise properties, financing or investment purposes, enterprise safety rating, enterprise scale, enterprise asset conditions and the like, the indiscriminate product pushing mode has no pertinence, most of the pushed products are usually completely unaffiliated with the enterprise customers, so that the customers are difficult to quickly find interested/intentional products, and the efficiency is very low.
Disclosure of Invention
The invention provides an electronic device, a product recommendation method and a computer-readable storage medium, aiming at enabling a product pushed by a trading platform to be more targeted and enabling a customer to select a proper product more efficiently.
In order to achieve the above object, the electronic device provided in the present invention includes a memory and a processor, the memory stores a product recommendation system operable on the processor, and the product recommendation system, when executed by the processor, implements the following steps:
a1, after detecting that a member logs in the system, acquiring a member score corresponding to the member, and determining a score floating interval taking the acquired member score as a center;
b1, screening products with the product scores in the score floating interval from a product database;
c1, acquiring the willingness investment amount range of the member, and determining the amount limit range corresponding to the member score of the member according to the mapping relation between the predetermined member score and the amount limit range;
d1, comparing the acquired willingness investment amount range with the amount limit range;
e1, if the willingness investment amount range is partially or completely located in the amount limited range, determining an amount intersection range of the willingness investment amount range and the amount limited range, and extracting products with total issuing amount of the products located in the amount intersection range from the screened products;
f1, if the wished investment sum range is out of the sum limit range, extracting the product with the total issuing amount of the product within the sum limit range from the screened products;
g1, pushing the extracted product to the member in a preset manner.
Preferably, the processor is further configured to execute the product recommendation system to implement the steps of:
after a newly registered member is detected, acquiring information of each preset attribute item from registration information of the member;
based on the acquired information of each preset attribute item, determining the score of each preset attribute item of the member by searching a score table corresponding to each preset attribute item;
and carrying out weighted summation on the scores of the preset attribute items of the member according to the weights corresponding to the preset attribute items so as to obtain the member score of the member, and storing the obtained member score and the member in a correlated manner.
Preferably, the processor is further configured to execute the product recommendation system to implement the steps of:
after receiving a product shelf loading request, acquiring information of each preset attribute item from product information of the product;
based on the acquired information of each preset attribute item, the score of each preset attribute item of the product is obtained by searching a score table corresponding to each preset attribute item;
and carrying out weighted summation on the scores of the preset attribute items of the member according to the predetermined weights corresponding to the preset attribute items to obtain the product score of the product, and storing the obtained product score in association with the product.
Preferably, the step G1 is replaced by:
if the extracted product quantity is less than or equal to a first preset quantity, expanding the score floating interval according to a preset rule, and executing the steps B1-F1 again based on the expanded score floating interval;
if the extracted product quantity is larger than or equal to a second preset quantity, reducing the score floating interval according to a preset rule, and executing the steps B1 to F1 again based on the reduced score floating interval;
and if the number of the extracted products is larger than the first preset number and smaller than the second preset number, pushing the extracted products to the member in a preset mode.
The invention also provides a product recommendation method, which comprises the following steps:
a2, after detecting that a member logs in the system, acquiring a member score corresponding to the member, and determining a score floating interval taking the acquired member score as a center;
b2, screening products with the product scores in the score floating interval from a product database;
c2, acquiring the willingness investment amount range of the member, and determining the amount limit range corresponding to the member score of the member according to the mapping relation between the predetermined member score and the amount limit range;
d2, comparing the acquired willingness investment amount range with the amount limit range;
e2, if the willingness investment amount range is partially or completely located in the amount limited range, determining an amount intersection range of the willingness investment amount range and the amount limited range, and extracting products with total issuing amount of the products located in the amount intersection range from the screened products;
f2, if the wished investment sum range is out of the sum limit range, extracting the product with the total issuing amount of the product within the sum limit range from the screened products;
g2, pushing the extracted product to the member in a preset manner.
Preferably, the product recommendation method further comprises:
after a newly registered member is detected, acquiring information of each preset attribute item from registration information of the member;
based on the acquired information of each preset attribute item, determining the score of each preset attribute item of the member by searching a score table corresponding to each preset attribute item;
and carrying out weighted summation on the scores of the preset attribute items of the member according to the weights corresponding to the preset attribute items so as to obtain the member score of the member, and storing the obtained member score and the member in a correlated manner.
Preferably, the product recommendation method further comprises:
after receiving a product shelf loading request, acquiring information of each preset attribute item from product information of the product;
based on the acquired information of each preset attribute item, the score of each preset attribute item of the product is obtained by searching a score table corresponding to each preset attribute item;
and carrying out weighted summation on the scores of the preset attribute items of the member according to the predetermined weights corresponding to the preset attribute items to obtain the product score of the product, and storing the obtained product score in association with the product.
Preferably, the step G2 is replaced by:
if the extracted product quantity is less than or equal to a first preset quantity, expanding the score floating interval according to a preset rule, and executing the steps B2-F2 again based on the expanded score floating interval;
if the extracted product quantity is larger than or equal to a second preset quantity, reducing the score floating interval according to a preset rule, and executing the steps B2 to F2 again based on the reduced score floating interval;
and if the number of the extracted products is larger than the first preset number and smaller than the second preset number, pushing the extracted products to the member in a preset mode.
The present invention also contemplates a computer-readable storage medium storing a product recommendation system executable by at least one processor to cause the at least one processor to perform the steps of:
a3, after detecting that a member logs in the system, acquiring a member score corresponding to the member, and determining a score floating interval taking the acquired member score as a center;
b3, screening products with the product scores in the score floating interval from a product database;
c3, acquiring the willingness investment amount range of the member, and determining the amount limit range corresponding to the member score of the member according to the mapping relation between the predetermined member score and the amount limit range;
d3, comparing the acquired willingness investment amount range with the amount limit range;
e3, if the willingness investment amount range is partially or completely located in the amount limited range, determining an amount intersection range of the willingness investment amount range and the amount limited range, and extracting products with total issuing amount of the products located in the amount intersection range from the screened products;
f3, if the wished investment sum range is out of the sum limit range, extracting the product with the total issuing amount of the product within the sum limit range from the screened products;
g3, pushing the extracted product to the member in a preset manner.
Preferably, the step G3 is replaced by:
if the extracted product quantity is less than or equal to a first preset quantity, expanding the score floating interval according to a preset rule, and executing the steps B3-F3 again based on the expanded score floating interval;
if the extracted product quantity is larger than or equal to a second preset quantity, reducing the score floating interval according to a preset rule, and executing the steps B3 to F3 again based on the reduced score floating interval;
and if the number of the extracted products is larger than the first preset number and smaller than the second preset number, pushing the extracted products to the member in a preset mode.
According to the technical scheme, the member scores of all members and the product scores of all products on shelves are calculated in advance, after the members log in a system, a score floating interval is determined according to the member scores of the members, and products with the product scores within the score floating range are screened from product data; determining the total issuing amount range of the products suitable for the member by comparing the willful investment amount range of the member with the amount limit range corresponding to the member score, further extracting the products with the total issuing amount meeting the requirement from the screened products according to the determined total issuing amount range of the products suitable for the member, and pushing the extracted products to the member; therefore, products pushed to the members are more targeted, less in amount and more accurate, the products are products which can be matched with the members, the members can quickly determine whether interesting products exist, and the product selection is more efficient.
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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a flowchart illustrating a product recommendation method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a second embodiment of a product recommendation method according to the present invention;
FIG. 3 is a flowchart illustrating a product recommendation method according to a third embodiment of the present invention;
FIG. 4 is a schematic diagram of an operating environment of an embodiment of a product recommendation system of the present invention;
FIG. 5 is a block diagram of a program of an embodiment of the product recommendation system of the present invention;
FIG. 6 is a block diagram of a product recommendation system according to a second embodiment of the present invention;
FIG. 7 is a block diagram of a product recommendation system according to a third embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, fig. 1 is a flowchart illustrating a product recommendation method according to an embodiment of the present invention.
In this embodiment, the product recommendation method includes:
step S10, after detecting that the member logs in the system, acquiring the member score corresponding to the member, and determining a score floating interval taking the acquired member score as the center;
the system comprises a system database, a system database and a plurality of value floating intervals, wherein the value floating intervals are used for storing pre-calculated member values corresponding to all members, when the condition that a member logs in the system is detected, namely the member logs in the system, the system acquires the member value corresponding to the logged-in member, the value floating intervals taking the acquired member value as the center are determined according to the acquired member value, and the range of the value floating intervals can be preset. For example, the floating interval of the score is from 30% lower to 30% higher than the obtained membership score, i.e., if the obtained membership score is X, the floating interval of the score is 0.7X to 1.3X.
Step S20, screening products with the product scores in the score floating interval from a product database;
the product database of the system also stores the pre-calculated product scores corresponding to all products on the shelf, and after the score floating interval is determined, the system screens the products with the product scores in the score floating interval from the product database, namely finds out the products matched with the member scores.
Step S30, acquiring the willingness investment amount range of the member, and determining the amount limit range corresponding to the member score of the member according to the mapping relation between the predetermined member score and the amount limit range;
the member information of the member comprises a willingness investment amount range (for example, 400-500 ten thousand) filled by the member, namely the amount range which the member wants to invest, and the willingness investment amount range of the member is obtained from the member information of the member; the system has a mapping table of preset member score and money limit range, and the money limit range (for example, 400 to 600 ten thousand) corresponding to the member score can be known by inquiring the mapping table.
Step S40, comparing the acquired willingness investment amount range with the amount limit range;
step S50, if the willingness investment amount range is partially or totally located in the amount limited range, determining the amount intersection range of the willingness investment amount range and the amount limited range, and extracting products with the total issuing amount of the products located in the amount intersection range from the screened products;
for example, 1, if the willingness investment amount range of the member is 600 to 1000 ten thousand, and the amount limit range of the member is 500 to 800 ten thousand, the willingness investment amount range is partially within the amount limit range; 2. the willingness investment amount range of the member is 600-1000 ten thousand, the amount limit range of the member is 500-1100 ten thousand, and the willingness investment amount range is totally positioned in the amount limit range; and when part or all of the range of the willful investment amount is within the amount limit range, determining the amount intersection range of the willful investment amount and the amount limit range, and further extracting products with the total issuing amount of the product scores within the determined amount intersection range from the products screened by the score floating interval.
Step S60, if the wished investment sum range is outside the sum limit range, extracting the product with the total sum of the issued products within the sum limit range from the screened products;
for example, if the member has an intended investment amount range of 1500 to 2000 ten thousand and the member has a limited amount range of 1000 to 1400 ten thousand, the intended investment amount range is not within the limited amount range and exceeds the limited amount range, and the intended investment amount range exceeds the strength of the member's enterprise or company, then the limited amount range is used as an extraction condition to further extract products whose product scores are distributed in the limited amount range from among the products screened through the score floating interval
And step S70, pushing the extracted products to the member in a preset mode.
After the products are extracted, the system pushes the screened products to the member in a preset mode. For example, the screened products are displayed through a popup interface, the screened products are prompted through a system message, or the screened products are displayed in turn on a preset product display interface, and the like.
According to the technical scheme, the method comprises the steps of calculating the member scores of all members and the product scores of all products on shelves in advance, determining a score floating interval according to the member scores of the members after the members log in a system, and screening products of which the product scores are within the score floating range in product data; determining the total issuing amount range of the products suitable for the member by comparing the willful investment amount range of the member with the amount limit range corresponding to the member score, further extracting the products with the total issuing amount meeting the requirement from the screened products according to the determined total issuing amount range of the products suitable for the member, and pushing the extracted products to the member; therefore, products pushed to the members are more targeted, less in amount and more accurate, the products are products which can be matched with the members, the members can quickly determine whether interesting products exist, and the product selection is more efficient.
As shown in fig. 2, fig. 2 is a flowchart illustrating a second embodiment of the product recommendation method of the present invention. The embodiment is based on an embodiment, and in this embodiment, the product recommendation method further includes:
step S101, after a newly registered member is detected, acquiring information of each preset attribute item from registration information of the member;
when each enterprise registers a system member, related information of the enterprise needs to be filled in, wherein the related information comprises a plurality of preset attribute items which need to be filled in: whether the system is a group parent company, a group to which the system belongs, whether the system is a listed company, listing time, a stock code, registered capital, real income capital, stockholder background, establishment time, employee number, the working age of core employees, industry category, enterprise nature, government support degree, the area, a registered place, an operating range, a main business composition and external rating, and after the system passes the verification of the relevant information of the enterprise (namely the registered member succeeds), the relevant information is recorded in a member database. After the system detects a newly registered member (i.e. after the new member is successfully registered), the system obtains information of each preset attribute item in the registration information of the new member from the member database, for example, the information of each preset attribute item in the registration information of the new member is: the company is a group parent company, belongs to a certain group, is a listed company, is listed on a certain day of a certain month in a certain year, has a stock code of 5000 ten thousand, and has an actual income capital of 4000 ten thousand … ….
Step S102, based on the acquired information of each preset attribute item, determining the score of each preset attribute item of the member by searching a score table corresponding to each preset attribute item;
and presetting a scoring table of each preset attribute item of the member in the system, and respectively searching the corresponding scoring table according to the acquired information of each preset attribute item of the newly registered member so as to determine the score of each preset attribute item. For example, the attribute "whether it is a corporate parent" is scored as: "yes" for score 10 and "no" for score 3; the rating of the attribute term "registered capital" is: 3 points of ' less than 500 ten thousand ', ' 5 points of 500-5000 ten thousand ', ' more than 5000 ten thousand ' and ' 10 points; and so on.
Step S103, carrying out weighted summation on the scores of the preset attribute items of the member according to the weights corresponding to the preset attribute items to obtain the member score of the member, and storing the obtained member score and the member in a related manner.
The system is provided with the weight of each preset attribute item of the system, after the score of each preset attribute item of the member is determined, the score of each preset attribute item of the member is weighted and summed according to the preset weight of each preset attribute item, the member score of the member is obtained through calculation, and the calculated member score is associated with the member and stored.
In addition, in this embodiment, the product recommendation method further includes: updating the attribute information of all the members at regular time, re-determining the member score of the member with the changed attribute information according to the calculation mode of the member score, and replacing the newly determined member score with the previously stored member score.
In this embodiment, the product recommendation method further includes:
after receiving a product shelf loading request, acquiring information of each preset attribute item from product information of the product;
when each product is on shelf, the related information of the product needs to be filled, wherein the related information comprises a plurality of preset attribute items: the system is characterized by comprising product classification, product name, product type, total amount issued, information of interest date, expiration date, expected income, repayment mode, interest rate mode and information of a publisher, wherein after the related information of the product is approved, the product is put on shelf, and the related information is recorded in a product database. After the system detects a newly-shelved product, the system obtains information of each preset attribute item of the new product from the product database, for example, the information of each preset attribute item of the newly-shelved product is as follows: medical devices, instruments, total 2000 ten thousand … … issued.
Based on the acquired information of each preset attribute item, the score of each preset attribute item of the product is obtained by searching a score table corresponding to each preset attribute item;
and according to the acquired information of each preset attribute item of the new products on shelves, respectively searching the corresponding scoring table to determine the score of each preset attribute item.
And carrying out weighted summation on the scores of the preset attribute items of the member according to the predetermined weights corresponding to the preset attribute items to obtain the product score of the product, and storing the obtained product score in association with the product.
The system is provided with the weight of each preset attribute item of the product, after the score of each preset attribute item of the product on the shelf is determined, the scores of the preset attribute items of the product are weighted and summed according to the preset weights of the preset attribute items, the product score of the product is obtained through calculation, and the calculated product score is associated with the product and is stored.
As shown in fig. 3, fig. 3 is a schematic flow chart of a product recommendation method according to three embodiments of the present invention. The present embodiment is based on any one of the above embodiments, and the product recommendation method of the present embodiment replaces step S70 with:
step S71, if the extracted product quantity is less than or equal to a first preset quantity, expanding the score floating interval according to a preset rule, and executing steps S20 to S60 again based on the expanded score floating interval;
the system is preset with a rule for modifying the size of a score floating interval, if the number of the extracted products with the scores in the score floating interval is less than or equal to a first preset number (for example, 10), that is, the extracted products are too few or the products which are not selected to be accordant are not selected, if the products which are selected in the way are directly pushed to members, the number of the products selected by the members is too few, and the products which are interested by the users may not be in the small number of the selected products; therefore, the system expands the score floating interval according to the preset rule, so as to increase the number of the products with the product scores in the expanded score floating interval, and then re-executes the steps S20 to S60; if the number of newly extracted products is still too small, step S71 is executed again. Preferably, in this embodiment, the preset rule includes: each expansion process expands at a fixed rate (e.g., 10% of the membership score).
Step S72, if the extracted product quantity is larger than or equal to a second preset quantity, reducing the score floating interval according to a preset rule, and executing steps S20 to S60 again based on the reduced score floating interval;
if the number of the extracted products in the score floating interval is greater than or equal to a second number (for example, 30), that is, the number of the extracted products is too large, if the extracted products are directly pushed to the member, the efficiency of selecting the products by the member is still affected due to the fact that the number of the pushed products is too large. Therefore, the system narrows down the floating interval according to the preset rule, so as to reduce the number of products with the product score within the narrowed floating interval, and then re-executes steps S20 to S60; if the number of newly extracted products is still too large, step S72 is executed again. Preferably, in this embodiment, the preset rule includes: each reduction process reduces at a fixed rate (e.g., 10% of the membership score).
Step S73, if the number of extracted products is greater than the first preset number and less than the second preset number, pushing the extracted products to the member in a preset manner.
If the number of the extracted products is within the range of the first preset number and the second preset number, namely the number of the extracted products is very suitable, not many, not too few, the system directly pushes the extracted products to the member in a preset mode.
In the embodiment, when the number of extracted products is too large or too small, the products meeting the requirements are extracted again after the score floating interval is correspondingly adjusted, so that the number of the finally extracted products is more suitable, and the number of the finally extracted products is not too large or too small.
In addition, the invention also provides a product recommendation system.
Please refer to fig. 4, which is a schematic diagram of an operating environment of the product recommendation system 10 according to an embodiment of the present invention.
In the present embodiment, the product recommendation system 10 is installed and operated in the electronic device 1. The electronic device 1 may be a desktop computer, a notebook, a palm computer, a server, or other computing equipment. The electronic device 1 may include, but is not limited to, a memory 11, a processor 12, and a display 13. Fig. 4 only shows the electronic device 1 with components 11-13, but it is to be understood that not all of the shown components are required to be implemented, and that more or fewer components may alternatively be implemented.
The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a hard disk or a memory of the electronic device 1. The memory 11 may also be an external storage device of the electronic apparatus 1 in other embodiments, such as a plug-in hard disk provided on the electronic apparatus 1, a Smart Memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic apparatus 1. The memory 11 is used for storing application software installed in the electronic device 1 and various data, such as program codes of the product recommendation system 10. The memory 11 may also be used to temporarily store data that has been output or is to be output.
The processor 12 may be, in some embodiments, a Central Processing Unit (CPU), microprocessor or other data Processing chip for executing program codes stored in the memory 11 or Processing data, such as executing the product recommendation system 10.
The display 13 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 13 is used for displaying information processed in the electronic apparatus 1 and for displaying a visualized user interface, such as a service customization interface or the like. The components 11-13 of the electronic device 1 communicate with each other via a system bus.
Please refer to fig. 5, which is a block diagram of a product recommendation system 10 according to an embodiment of the present invention. In this embodiment, the product recommendation system 10 can be divided into one or more modules, and the one or more modules are stored in the memory 11 and executed by one or more processors (in this embodiment, the processor 12) to complete the present invention. For example, in fig. 5, the product recommendation system 10 may be partitioned into a first determination module 101, a filtering module 102, a second determination module 103, a comparison module 104, an extraction module 105, and a first push module 106. The module referred to in the present invention refers to a series of computer program instruction segments capable of performing specific functions, and is more suitable than a program for describing the execution process of the product recommendation system 10 in the electronic device 1, wherein:
the first determining module 101 is configured to, after detecting that a member logs in a system, acquire a member score corresponding to the member, and determine a score floating interval with the acquired member score as a center;
the system comprises a system database, a system database and a plurality of value floating intervals, wherein the value floating intervals are used for storing pre-calculated member values corresponding to all members, when the condition that a member logs in the system is detected, namely the member logs in the system, the system acquires the member value corresponding to the logged-in member, the value floating intervals taking the acquired member value as the center are determined according to the acquired member value, and the range of the value floating intervals can be preset. For example, the floating interval of the score is from 30% lower to 30% higher than the obtained membership score, i.e., if the obtained membership score is X, the floating interval of the score is 0.7X to 1.3X.
The screening module 102 is used for screening the products with the product scores in the score floating interval from the product database;
the product database of the system also stores the pre-calculated product scores corresponding to all products on the shelf, and after the score floating interval is determined, the system screens the products with the product scores in the score floating interval from the product database, namely finds out the products matched with the member scores.
The second determining module 103 is configured to obtain a willingness investment amount range of the member, and determine an amount limit range corresponding to the member score of the member according to a mapping relationship between the predetermined member score and the amount limit range;
the member information of the member comprises a willingness investment amount range (for example, 400-500 ten thousand) filled by the member, namely the amount range which the member wants to invest, and the willingness investment amount range of the member is obtained from the member information of the member; the system has a mapping table of preset member score and money limit range, and the money limit range (for example, 400 to 600 ten thousand) corresponding to the member score can be known by inquiring the mapping table.
A comparison module 104, configured to compare the obtained willingness investment amount range with the amount limit range;
the extraction module 105 is configured to determine an amount intersection range between the willingness investment amount range and the amount limit range when part or all of the willingness investment amount range is within the amount limit range, and extract a product with an overall product issuance amount within the amount intersection range from the screened products;
for example, 1, if the willingness investment amount range of the member is 600 to 1000 ten thousand, and the amount limit range of the member is 500 to 800 ten thousand, the willingness investment amount range is partially within the amount limit range; 2. the willingness investment amount range of the member is 600-1000 ten thousand, the amount limit range of the member is 500-1100 ten thousand, and the willingness investment amount range is totally positioned in the amount limit range; and when part or all of the range of the willful investment amount is within the amount limit range, determining the amount intersection range of the willful investment amount and the amount limit range, and further extracting products with the total issuing amount of the product scores within the determined amount intersection range from the products screened by the score floating interval.
The extraction module 105 is further configured to extract a product with an issuance sum within the amount limit range from the screened products when the willingness investment amount range is outside the amount limit range;
for example, if the member has an intended investment amount range of 1500 to 2000 ten thousand and the member has a limited amount range of 1000 to 1400 ten thousand, the intended investment amount range is not within the limited amount range and exceeds the limited amount range, and the intended investment amount range exceeds the strength of the member's enterprise or company, then the limited amount range is used as an extraction condition to further extract products whose product scores are distributed in the limited amount range from among the products screened through the score floating interval
And the first pushing module 106 is used for pushing the extracted product to the member in a preset mode.
After the products are extracted, the system pushes the screened products to the member in a preset mode. For example, the screened products are displayed through a popup interface, the screened products are prompted through a system message, or the screened products are displayed in turn on a preset product display interface, and the like.
According to the technical scheme, the method comprises the steps of calculating the member scores of all members and the product scores of all products on shelves in advance, determining a score floating interval according to the member scores of the members after the members log in a system, and screening products of which the product scores are within the score floating range in product data; determining the total issuing amount range of the products suitable for the member by comparing the willful investment amount range of the member with the amount limit range corresponding to the member score, further extracting the products with the total issuing amount meeting the requirement from the screened products according to the determined total issuing amount range of the products suitable for the member, and pushing the extracted products to the member; therefore, products pushed to the members are more targeted, less in amount and more accurate, the products are products which can be matched with the members, the members can quickly determine whether interesting products exist, and the product selection is more efficient.
Referring to fig. 6, fig. 6 is a block diagram of a program of a product recommendation system according to a second embodiment of the present invention. In this embodiment, the product recommendation system further includes:
a first obtaining module 107, configured to obtain information of each preset attribute item from registration information of a newly registered member after the member is detected;
when each enterprise registers a system member, related information of the enterprise needs to be filled in, wherein the related information comprises a plurality of preset attribute items which need to be filled in: whether the system is a group parent company, a group to which the system belongs, whether the system is a listed company, listing time, a stock code, registered capital, real income capital, stockholder background, establishment time, employee number, the working age of core employees, industry category, enterprise nature, government support degree, the area, a registered place, an operating range, a main business composition and external rating, and after the system passes the verification of the relevant information of the enterprise (namely the registered member succeeds), the relevant information is recorded in a member database. After the system detects a newly registered member (i.e. after the new member is successfully registered), the system obtains information of each preset attribute item in the registration information of the new member from the member database, for example, the information of each preset attribute item in the registration information of the new member is: the company is a group parent company, belongs to a certain group, is a listed company, is listed on a certain day of a certain month in a certain year, has a stock code of 5000 ten thousand, and has an actual income capital of 4000 ten thousand … ….
A first score determining module 108, configured to determine, based on the obtained information of each preset attribute item, a score of each preset attribute item of the member by looking up a score table corresponding to each preset attribute item;
and presetting a scoring table of each preset attribute item of the member in the system, and respectively searching the corresponding scoring table according to the acquired information of each preset attribute item of the newly registered member so as to determine the score of each preset attribute item. For example, the attribute "whether it is a corporate parent" is scored as: "yes" for score 10 and "no" for score 3; the rating of the attribute term "registered capital" is: 3 points of ' less than 500 ten thousand ', ' 5 points of 500-5000 ten thousand ', ' more than 5000 ten thousand ' and ' 10 points; and so on.
The first calculating module 109 is configured to perform weighted summation on the scores of the preset attribute items of the member according to weights corresponding to the preset attribute items, so as to obtain a member score of the member, and store the obtained member score in association with the member.
The system is provided with the weight of each preset attribute item of the system, after the score of each preset attribute item of the member is determined, the score of each preset attribute item of the member is weighted and summed according to the preset weight of each preset attribute item, the member score of the member is obtained through calculation, and the calculated member score is associated with the member and stored.
In addition, in this embodiment, the product recommendation system further includes: and the updating module is used for updating the attribute information of all the members at regular time, re-determining the member score of the member with the changed attribute information according to the calculation mode of the member score, and replacing the newly determined member score with the previously stored member score.
In this embodiment, the product recommendation system further includes:
the second acquisition module is used for acquiring the information of each preset attribute item from the product information of the product after receiving the product shelving request;
when each product is on shelf, the related information of the product needs to be filled, wherein the related information comprises a plurality of preset attribute items: the system is characterized by comprising product classification, product name, product type, total amount issued, information of interest date, expiration date, expected income, repayment mode, interest rate mode and information of a publisher, wherein after the related information of the product is approved, the product is put on shelf, and the related information is recorded in a product database. After the system detects a newly-shelved product, the system obtains information of each preset attribute item of the new product from the product database, for example, the information of each preset attribute item of the newly-shelved product is as follows: medical devices, instruments, total 2000 ten thousand … … issued.
The second score determining module is used for obtaining the score of each preset attribute item of the product by searching a score table corresponding to each preset attribute item based on the acquired information of each preset attribute item;
and according to the acquired information of each preset attribute item of the new products on shelves, respectively searching the corresponding scoring table to determine the score of each preset attribute item.
And the second calculation module is used for weighting and summing the scores of the preset attribute items of the member according to the predetermined weights corresponding to the preset attribute items to obtain the product score of the product, and storing the obtained product score in association with the product.
The system is provided with the weight of each preset attribute item of the product, after the score of each preset attribute item of the product on the shelf is determined, the scores of the preset attribute items of the product are weighted and summed according to the preset weights of the preset attribute items, the product score of the product is obtained through calculation, and the calculated product score is associated with the product and is stored.
Fig. 7 is a block diagram of a program of a product recommendation system according to a third embodiment of the present invention. The product recommendation system of this embodiment replaces the first pushing module 106 with a second pushing module 107, and the second pushing module 110 includes
The expansion submodule 111 is configured to expand the score floating interval according to a preset rule when the number of the extracted products is smaller than or equal to a first preset number, and re-execute the screening module 102, the second determining module 103, the comparing module 104, and the extracting module 105 based on the expanded score floating interval;
the system is preset with a rule for modifying the size of a score floating interval, if the number of the extracted products with the scores in the score floating interval is less than or equal to a first preset number (for example, 10), that is, the extracted products are too few or the products which are not selected to be accordant are not selected, if the products which are selected in the way are directly pushed to members, the number of the products selected by the members is too few, and the products which are interested by the users may not be in the small number of the selected products; therefore, the system expands the score floating interval according to a preset rule, so that the number of products with the product scores in the expanded score floating interval is increased, and then the screening module 102, the second determining module 103, the comparing module 104 and the extracting module 105 are executed again; if the number of re-extracted products is still too small, the expand sub-module 111 is executed again. Preferably, in this embodiment, the preset rule includes: each expansion process expands at a fixed rate (e.g., 10% of the membership score).
A narrowing sub-module 112, configured to narrow the score floating interval according to a preset rule if the number of the extracted products is greater than or equal to a second preset number, and re-execute the screening module 102, the second determining module 103, the comparing module 104, and the extracting module 105 based on the narrowed score floating interval;
if the number of the extracted products in the score floating interval is greater than or equal to a second number (for example, 30), that is, the number of the extracted products is too large, if the extracted products are directly pushed to the member, the efficiency of selecting the products by the member is still affected due to the fact that the number of the pushed products is too large. Therefore, the system reduces the score floating interval according to a preset rule, so that the number of products with the product scores in the reduced score floating interval is reduced, and then the screening module 102, the second determining module 103, the comparing module 104 and the extracting module 105 are executed again; if the number of re-extracted products is still too large, the shrink sub-module 112 is executed again. Preferably, in this embodiment, the preset rule includes: each reduction process reduces at a fixed rate (e.g., 10% of the membership score).
The pushing sub-module 113 is configured to, when the number of the extracted products is greater than the first preset number and smaller than the second preset number, push the extracted products to the member in a preset manner.
If the number of the extracted products is within the range of the first preset number and the second preset number, namely the number of the extracted products is very suitable, not many, not too few, the system directly pushes the extracted products to the member in a preset mode.
In the embodiment, when the number of extracted products is too large or too small, the products meeting the requirements are extracted again after the score floating interval is correspondingly adjusted, so that the number of the finally extracted products is more suitable, and the number of the finally extracted products is not too large or too small.
Further, the present invention also provides a computer-readable storage medium storing a product recommendation system, which is executable by at least one processor to cause the at least one processor to execute the product recommendation method in any of the above embodiments.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (7)
1. An electronic device comprising a memory and a processor, the memory having stored thereon a product recommendation system operable on the processor, the product recommendation system when executed by the processor implementing the steps of:
a1, after detecting that a member logs in the system, acquiring a member score corresponding to the member, and determining a score floating interval taking the acquired member score as a center;
b1, screening products with the product scores in the score floating interval from a product database;
c1, acquiring the willingness investment amount range filled in the member information by the member, and determining the amount limit range corresponding to the member score of the member according to the mapping relation between the predetermined member score and the amount limit range; d1, comparing the acquired willingness investment amount range with the amount limit range;
e1, if the willingness investment amount range is partially or completely located in the amount limited range, determining an amount intersection range of the willingness investment amount range and the amount limited range, and extracting products with total issuing amount of the products located in the amount intersection range from the screened products;
f1, if the wished investment sum range is out of the sum limit range, extracting the product with the total issuing amount of the product within the sum limit range from the screened products;
if the extracted product quantity is less than or equal to a first preset quantity, expanding the score floating interval according to a preset rule, and executing the steps B1-F1 again based on the expanded score floating interval;
if the extracted product quantity is larger than or equal to a second preset quantity, reducing the score floating interval according to a preset rule, and executing the steps B1 to F1 again based on the reduced score floating interval;
and if the number of the extracted products is larger than the first preset number and smaller than the second preset number, pushing the extracted products to the member in a preset mode.
2. The electronic device of claim 1, wherein the processor is further configured to execute the product recommendation system to:
after a newly registered member is detected, acquiring information of each preset attribute item from registration information of the member;
based on the acquired information of each preset attribute item, determining the score of each preset attribute item of the member by searching a score table corresponding to each preset attribute item;
and carrying out weighted summation on the scores of the preset attribute items of the member according to the weights corresponding to the preset attribute items so as to obtain the member score of the member, and storing the obtained member score and the member in a correlated manner.
3. The electronic device of claim 1, wherein the processor is further configured to execute the product recommendation system to:
after receiving a product shelf loading request, acquiring information of each preset attribute item from product information of the product;
based on the acquired information of each preset attribute item, the score of each preset attribute item of the product is obtained by searching a score table corresponding to each preset attribute item;
and carrying out weighted summation on the scores of the preset attribute items of the member according to the predetermined weights corresponding to the preset attribute items to obtain the product score of the product, and storing the obtained product score in association with the product.
4. A product recommendation method, characterized in that the product recommendation method comprises the steps of:
a2, after detecting that a member logs in the system, acquiring a member score corresponding to the member, and determining a score floating interval taking the acquired member score as a center;
b2, screening products with the product scores in the score floating interval from a product database;
c2, acquiring the willingness investment amount range filled in the member information by the member, and determining the amount limit range corresponding to the member score of the member according to the mapping relation between the predetermined member score and the amount limit range;
d2, comparing the acquired willingness investment amount range with the amount limit range;
e2, if the willingness investment amount range is partially or completely located in the amount limited range, determining an amount intersection range of the willingness investment amount range and the amount limited range, and extracting products with total issuing amount of the products located in the amount intersection range from the screened products;
f2, if the wished investment sum range is out of the sum limit range, extracting the product with the total issuing amount of the product within the sum limit range from the screened products;
g2, pushing the extracted products to the member in a preset mode;
if the extracted product quantity is less than or equal to a first preset quantity, expanding the score floating interval according to a preset rule, and executing the steps B1-F1 again based on the expanded score floating interval;
if the extracted product quantity is larger than or equal to a second preset quantity, reducing the score floating interval according to a preset rule, and executing the steps B1 to F1 again based on the reduced score floating interval;
and if the number of the extracted products is larger than the first preset number and smaller than the second preset number, pushing the extracted products to the member in a preset mode.
5. The product recommendation method of claim 4, further comprising:
after a newly registered member is detected, acquiring information of each preset attribute item from registration information of the member;
based on the acquired information of each preset attribute item, determining the score of each preset attribute item of the member by searching a score table corresponding to each preset attribute item;
and carrying out weighted summation on the scores of the preset attribute items of the member according to the weights corresponding to the preset attribute items so as to obtain the member score of the member, and storing the obtained member score and the member in a correlated manner.
6. The product recommendation method of claim 4, further comprising:
after receiving a product shelf loading request, acquiring information of each preset attribute item from product information of the product;
based on the acquired information of each preset attribute item, the score of each preset attribute item of the product is obtained by searching a score table corresponding to each preset attribute item;
and carrying out weighted summation on the scores of the preset attribute items of the member according to the predetermined weights corresponding to the preset attribute items to obtain the product score of the product, and storing the obtained product score in association with the product.
7. A computer-readable storage medium having stored thereon a product recommendation system executable by at least one processor to cause the at least one processor to perform the steps of:
a3, after detecting that a member logs in the system, acquiring a member score corresponding to the member, and determining a score floating interval taking the acquired member score as a center;
b3, screening products with the product scores in the score floating interval from a product database;
c3, acquiring the willingness investment amount range filled in the member information by the member, and determining the amount limit range corresponding to the member score of the member according to the mapping relation between the predetermined member score and the amount limit range;
d3, comparing the acquired willingness investment amount range with the amount limit range;
e3, if the willingness investment amount range is partially or completely located in the amount limited range, determining an amount intersection range of the willingness investment amount range and the amount limited range, and extracting products with total issuing amount of the products located in the amount intersection range from the screened products;
f3, if the wished investment sum range is out of the sum limit range, extracting the product with the total issuing amount of the product within the sum limit range from the screened products;
if the extracted product quantity is less than or equal to a first preset quantity, expanding the score floating interval according to a preset rule, and executing the steps B3-F3 again based on the expanded score floating interval;
if the extracted product quantity is larger than or equal to a second preset quantity, reducing the score floating interval according to a preset rule, and executing the steps B3 to F3 again based on the reduced score floating interval;
and if the number of the extracted products is larger than the first preset number and smaller than the second preset number, pushing the extracted products to the member in a preset mode.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015201090A (en) * | 2014-04-09 | 2015-11-12 | 凸版印刷株式会社 | Commodity recommendation device, commodity recommendation method, and program |
| CN105260480A (en) * | 2015-11-09 | 2016-01-20 | 魅族科技（中国）有限公司 | Information recommendation method and terminal |
| CN106779809A (en) * | 2016-11-25 | 2017-05-31 | 增立智造信息科技有限公司 | A kind of pricing information optimum organization method and system of big data platform |
| CN107077687A (en) * | 2014-11-13 | 2017-08-18 | 数字炼金术（澳大利亚）企业有限公司 | Obtain the data relevant with consumer, the processing data and the output that the consumer's quotation being electronically generated is provided |
| CN107341173A (en) * | 2017-05-16 | 2017-11-10 | 阿里巴巴集团控股有限公司 | A kind of information processing method and device |
| CN107526800A (en) * | 2017-08-20 | 2017-12-29 | 平安科技（深圳）有限公司 | Device, method and the computer-readable recording medium of information recommendation |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170206554A1 (en) * | 2012-10-18 | 2017-07-20 | Mack Craft | Centralized system of aggregated data sources and methods of use |
-
2018
- 2018-01-30 CN CN201810091244.3A patent/CN108182608B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015201090A (en) * | 2014-04-09 | 2015-11-12 | 凸版印刷株式会社 | Commodity recommendation device, commodity recommendation method, and program |
| CN107077687A (en) * | 2014-11-13 | 2017-08-18 | 数字炼金术（澳大利亚）企业有限公司 | Obtain the data relevant with consumer, the processing data and the output that the consumer's quotation being electronically generated is provided |
| CN105260480A (en) * | 2015-11-09 | 2016-01-20 | 魅族科技（中国）有限公司 | Information recommendation method and terminal |
| CN106779809A (en) * | 2016-11-25 | 2017-05-31 | 增立智造信息科技有限公司 | A kind of pricing information optimum organization method and system of big data platform |
| CN107341173A (en) * | 2017-05-16 | 2017-11-10 | 阿里巴巴集团控股有限公司 | A kind of information processing method and device |
| CN107526800A (en) * | 2017-08-20 | 2017-12-29 | 平安科技（深圳）有限公司 | Device, method and the computer-readable recording medium of information recommendation |
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|---|---|
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