CN111414548B - Object recommendation method, device, electronic equipment and medium - Google Patents
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- CN111414548B CN111414548B CN202010389816.3A CN202010389816A CN111414548B CN 111414548 B CN111414548 B CN 111414548B CN 202010389816 A CN202010389816 A CN 202010389816A CN 111414548 B CN111414548 B CN 111414548B
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- G06F16/90—Details of database functions independent of the retrieved data types
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- G06F16/90—Details of database functions independent of the retrieved data types
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Abstract
The present disclosure provides an object recommendation method performed by an electronic device, comprising: acquiring user data of a plurality of users, wherein the plurality of users comprise target users, and the user data comprise target user data of the target users; determining at least one reference user from a plurality of users based on the user data, wherein a first similarity between the user data of the reference user and the target user data is greater than a first threshold, and each reference user respectively comprises an object to be recommended; acquiring demand information of a target user; determining a target object from a plurality of objects to be recommended based on the demand information and the feature information of the objects to be recommended, wherein the second similarity of the feature information of the target object and the demand information is larger than a second threshold; and outputting the target object so as to recommend the target object to the target user. The disclosure also provides an object recommendation device, electronic equipment and a medium.
Description
Technical Field
The present disclosure relates to the field of internet technology, and more particularly, to a robot communication method and an object recommendation method, an object recommendation device, an electronic apparatus, and a medium.
Background
Currently, products to be recommended to a specific user are often determined from a plurality of products by a simple filtering or manual evaluation method.
In the process of implementing the disclosed concept, the inventor finds that at least the following problems exist in the related art: the user cannot be accurately recommended with a proper product.
Disclosure of Invention
In view of the above, the present disclosure provides an object recommendation method and an object recommendation device, an electronic apparatus, and a medium.
One aspect of the present disclosure provides an object recommendation method performed by an electronic device, including: acquiring user data of a plurality of users, wherein the plurality of users comprise target users, and the user data comprise target user data of the target users; determining at least one reference user from a plurality of users based on the user data, wherein a first similarity between the user data of the reference user and the target user data is greater than a first threshold, and each reference user respectively comprises an object to be recommended; acquiring demand information of a target user; determining a target object from a plurality of objects to be recommended based on the demand information and the feature information of the objects to be recommended, wherein the second similarity of the feature information of the target object and the demand information is larger than a second threshold; and outputting the target object so as to recommend the target object to the target user.
According to an embodiment of the present disclosure, determining at least one reference user from the plurality of users based on the user data comprises: acquiring an evaluation rule of an evaluation index; based on the user data and the evaluation rules, evaluating the evaluation index of each user in the plurality of users respectively to obtain an evaluation description of each user in the plurality of users; determining, based on the rating description of each user, a reference rating description having a first similarity to the rating description of the target user greater than the first threshold; and determining the user corresponding to the reference evaluation description as the reference user.
According to an embodiment of the present disclosure, determining a target object from a plurality of the objects to be recommended includes: acquiring object data of each object to be recommended in a plurality of objects to be recommended in a preset time period; establishing first time characteristic information of each object to be recommended based on the object data; and determining second time characteristic information of the target object based on the demand information; determining at least one first time feature information of which a second similarity between a plurality of the first time feature information and the second time feature information is greater than the second threshold; and taking the object to be recommended corresponding to the first time characteristic information as the target object.
According to an embodiment of the disclosure, the method further includes evaluating the object to be recommended based on object data of each of the objects to be recommended to obtain an evaluation result; determining the priority of the object to be recommended based on the evaluation result, and determining an excellent object according to the priority; wherein, based on the object data, establishing the first time feature information of each object to be recommended includes: and establishing first time characteristic information of each excellent object based on the object data.
According to an embodiment of the present disclosure, the object to be recommended includes a product to be recommended, and the evaluating the object to be recommended based on the object data of each of the objects to be recommended includes: and evaluating the products to be recommended based on the income data of each product to be recommended.
Another aspect of the present disclosure provides an object recommendation apparatus, including: the first acquisition module is used for acquiring user data of a plurality of users, wherein the plurality of users comprise target users, and the user data comprise target user data of the target users; a first determining module, configured to determine at least one reference user from the plurality of users based on the user data, where a first similarity between the user data of the reference user and the target user data is greater than a first threshold, where each reference user includes an object to be recommended; the second acquisition module is used for acquiring the demand information of the target user; a second determining module, configured to determine a target object from a plurality of objects to be recommended based on the requirement information and feature information of the objects to be recommended, where a second similarity between the feature information of the target object and the requirement information is greater than a second threshold; and an output module for outputting the target object so as to recommend the target object to the target user.
According to an embodiment of the disclosure, the first determining module includes: the first acquisition submodule is used for acquiring an evaluation rule of the evaluation index; the evaluation sub-module is used for evaluating the evaluation index of each user in the plurality of users based on the user data and the evaluation rule so as to obtain the evaluation description of each user in the plurality of users; a first determination sub-module for determining, based on the rating description of each user, a reference rating description having a first similarity to the rating description of the target user greater than a first threshold; and the second determining submodule is used for determining the user corresponding to the reference evaluation description as the reference user.
According to an embodiment of the present disclosure, the second determining module includes: the second acquisition submodule is used for acquiring object data of each to-be-recommended object in a plurality of to-be-recommended objects within a preset time period; the establishing sub-module is used for establishing first time characteristic information of each object to be recommended based on the object data; and a third determining sub-module for determining second time feature information of the target object based on the demand information; a fourth determining sub-module, configured to determine at least one first time feature information that a plurality of second similarities between the first time feature information and the second time feature information are greater than the second threshold; and a fifth determining submodule, configured to take an object to be recommended corresponding to the first time feature information as the target object.
Another aspect of the present disclosure provides an electronic device comprising one or more processors; and a storage device for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed, are configured to implement a method as described above.
Another aspect of the present disclosure provides a computer program comprising computer executable instructions which when executed are for implementing a method as described above.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments thereof with reference to the accompanying drawings in which:
fig. 1 schematically illustrates an application scenario in which an object recommendation method may be applied according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of object recommendation performed by an electronic device according to an embodiment of the disclosure;
FIG. 3 schematically illustrates a flow chart of a method of determining at least one reference user from a plurality of users in accordance with an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow chart of a method of determining a target object from a plurality of objects to be recommended according to an embodiment of the disclosure;
FIG. 5 schematically illustrates a block diagram of an object recommendation apparatus according to an embodiment of the present disclosure;
FIG. 6 schematically illustrates a block diagram of a first determination module according to an embodiment of the disclosure;
FIG. 7 schematically illustrates a block diagram of a second determination module according to an embodiment of the disclosure; and
fig. 8 schematically illustrates a block diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a formulation similar to at least one of "A, B or C, etc." is used, in general such a formulation should be interpreted in accordance with the ordinary understanding of one skilled in the art (e.g. "a system with at least one of A, B or C" would include but not be limited to systems with a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Embodiments of the present disclosure provide an object recommendation method performed by an electronic device, including: acquiring user data of a plurality of users, wherein the plurality of users comprise target users, and the user data comprise target user data of the target users; determining at least one reference user from a plurality of users based on the user data, wherein a first similarity between the user data of the reference user and the target user data is greater than a first threshold, and each reference user respectively comprises an object to be recommended; acquiring demand information of a target user; determining a target object from a plurality of objects to be recommended based on the demand information and the feature information of the objects to be recommended, wherein the second similarity of the feature information of the target object and the demand information is larger than a second threshold; and outputting the target object so as to recommend the target object to the target user.
Fig. 1 schematically illustrates an application scenario in which an object recommendation method may be applied according to an embodiment of the present disclosure. It should be noted that fig. 1 illustrates only an example of an application scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments, or scenarios.
As shown in fig. 1, the application scenario may include a plurality of branches of a bank, where the plurality of branches may be distributed in different regions. For example, M branch 101 of the plurality of branches is located in new york city.
In order to meet the market demand in new york city, the M branch 101 needs to push out new financial products. However, not only a long period but also a large manpower is required for redevelopment of the financial products, and thus a new financial product suitable for popularization of the M branch 101 can be selected from the financial products of the other branches except the M branch 101, thereby recommending the new financial product to the M branch 101.
The present disclosure provides an object recommendation method, which may be applied to the above scenario, and which may accurately select financial products suitable for popularization of M branches 101 from a plurality of branches.
Fig. 2 schematically illustrates a flowchart of object recommendation performed by an electronic device according to an embodiment of the disclosure.
As shown in fig. 2, the method includes operations S201 to S205.
In operation S201, user data of a plurality of users including a target user is acquired, the user data including target user data of the target user.
For example, in the scenario described above with reference to fig. 1, the plurality of users may be a plurality of branches of a bank and the target user may be an M branch. The user data may include administrative environments, geographic humanities, business scales, etc. for each branch.
For another example, in the context of a shopping website recommending items to a target user, the user may be a member of a shopping website, and the user data may include age, gender, occupation, geographic location, and the like.
In operation S202, at least one reference user is determined from a plurality of users based on the user data, wherein a first similarity of the user data of the reference user to the target user data is greater than a first threshold, each reference user including an object to be recommended.
For example, user data of other users than the target user among the plurality of users may be compared with the target user data, so that a user whose first similarity between the user data of the other users and the target user data is greater than a first threshold value is used as a reference user. For example, the value of the first threshold may be set by the person skilled in the art. For example, the first threshold may be set to 0.8, etc.
For example, in the scenario shown in fig. 1, the operation environment information of the other branches except the M branches is compared with the operation environment information of the M branches, and the like, so as to determine the first similarity between the operation environment of the other branches and the operation environment of the M branches. The operation environment information may include, for example, a supervision environment, a geographical humanity, an operation scale, and the like. Next, a branch having a first similarity greater than a first threshold among other branches may be used as a reference user.
According to an embodiment of the present disclosure, the object to be recommended may be, for example, a financial product that has been pushed out, an existing commodity, or the like.
In operation S203, demand information of the target user is acquired.
According to embodiments of the present disclosure, the demand information of the target user may include, for example, a description of characteristics that the target user has for the target object. The demand information may be, for example, a description of the profitability of the financial product by the M branches, a limitation of buying or selling the financial product, or the like. For another example, in the context of a shopping website recommending items to a target user, the demand information may be a description of the target user's use, size, etc. of the items.
According to embodiments of the present disclosure, for example, a target user may input demand information in a user interaction interface, so that an electronic device performing the method collects the demand information from the user interaction interface.
In operation S204, a target object is determined from the plurality of objects to be recommended based on the requirement information and the feature information of the objects to be recommended, wherein a second similarity of the feature information of the target object and the requirement information is greater than a second threshold. For example, the second threshold may take on a value of 0.9. The second threshold may be set by one skilled in the art himself.
According to an embodiment of the present disclosure, the feature information of the object to be recommended may be a description of features possessed by the object to be recommended. For example, if the object to be recommended is a financial product that has been promoted by other branches, the feature information of the object to be recommended may be a profit curve of the financial product that changes with time. For another example, if the object to be recommended is a table on a shopping website, the feature information of the object to be recommended may be a color, a size, or the like of the table.
According to the embodiment of the disclosure, for example, the feature information of the object to be recommended may be compared with the requirement information, so as to determine the second similarity between the feature information of the plurality of objects to be recommended and the requirement information. And determining the object to be recommended, of which the second similarity of the feature information and the requirement information is greater than a second threshold value, from the plurality of objects to be recommended.
In operation S205, the target object is output so as to recommend the target object to the target user. The target object may be displayed on a display screen, for example.
According to the embodiment of the disclosure, the object recommending method can determine the reference user similar to the characteristics of the target user from a plurality of users, so that the target object meeting the requirements of the target user is screened out from the objects to be recommended included in the reference user, and the accuracy of recommending the target object to the target user is improved.
Fig. 3 schematically illustrates a flow chart of a method of determining at least one reference user from a plurality of users according to an embodiment of the disclosure.
As shown in fig. 3, the method may include operations S212 to S242.
In operation S212, an evaluation rule of an evaluation index is acquired.
According to the embodiment of the present disclosure, the evaluation rule of the evaluation index may be formulated by a person skilled in the art according to the actual situation, for example, and may be stored in the storage unit of the electronic device, so that the electronic device performing the object recommendation method may read the evaluation rule of the evaluation index from the storage unit.
According to the embodiments of the present disclosure, different evaluation rules may be set for different evaluation indexes. For example, the evaluation rule for the evaluation index "business scale" may be that the total assets are large-scale of more than 200 gigabytes, the total resources are medium-scale of 200 gigabytes to 100 gigabytes, and the total assets are small-scale of less than 100 gigabytes. For example, the evaluation rule corresponding to the evaluation index "geographic humanity" may be that the local average deposit is more than 10 ten thousand yuan, the local average deposit is good between 10 ten thousand yuan and 3 ten thousand yuan, the local average deposit is qualified between 3 ten thousand yuan and 1 ten thousand yuan, and the local average deposit is not qualified between 1 ten thousand yuan. The evaluation rule may further include, for example, a mapping table that may specify that the score for large-scale correspondence may be 10, the score for medium-scale correspondence may be 5, the score for small-scale correspondence may be 1, and the score for excellent correspondence may be 10, the score for good correspondence may be 8, the score for passing may be 5, and the score for failing may be 1.
In operation S222, the evaluation index of each of the plurality of users is evaluated based on the user data and the evaluation rule, respectively, to obtain an evaluation description of each of the plurality of users.
According to an embodiment of the present disclosure, the evaluation index of each branch is evaluated, for example, according to an evaluation rule. For example, the evaluation index may include a supervision environment, a geographical personality, and an operation scale, and the evaluation description of the M branches according to the evaluation rule may be that the operation scale is a medium scale, the geographical personality is excellent, and the supervision environment is strict, for example.
In operation S232, a reference rating description having a first similarity to the rating description of the target user greater than a first threshold is determined based on the rating description of each user.
According to the embodiments of the present disclosure, for example, the electronic device may calculate a score of each user from the evaluation description and the mapping table of the evaluation rule, thereby determining the similarity with the evaluation description of the target user from the score of each user.
For example, a difference between the score of each user and the score of the target user may be calculated, where the difference is taken as a first similarity, and if the difference is greater than a first threshold, the first similarity is greater than the first threshold. Or the score of each evaluation index of the user may be compared with the score of the evaluation index of the target user, and if the difference between the score of each evaluation index and the score of the evaluation index of the target user is smaller than the set value, it may be determined that the first similarity between the evaluation description of the user and the evaluation description of the target user is greater than the first threshold.
In operation S242, the user corresponding to the reference rating description is determined as the reference user. That is, a user whose first similarity to the evaluation description of the target user is greater than the first threshold value is determined as the reference user.
FIG. 4 schematically illustrates a flow chart of a method of determining a target object from a plurality of objects to be recommended according to an embodiment of the disclosure.
As shown in fig. 4, the method may include operations S214 to S254.
In operation S214, object data of each of the plurality of objects to be recommended in the predetermined period of time is acquired.
For example, the financial product A1 of the branch a in 1 year may acquire weekly profit data, and the financial product A1 may be sold with the object data such as the share.
In operation S224, first time feature information of each object to be recommended is established based on the object data.
According to embodiments of the present disclosure, for example, a time-dependent profit profile of the financial product A1 for 1 year and a time-dependent share of the financial product A1 may be constructed from the weekly profit data of the financial product A1.
According to embodiments of the present disclosure, a time-series model of a financial product may be established, for example, from object data, the first time-characteristic information of the financial product being characterized by the time-series model.
According to an embodiment of the present disclosure, the object recommendation method may further include, before operation S224: the object data of each object to be recommended is evaluated to obtain an evaluation result, and the priority of the object to be recommended is determined based on the evaluation result, and the excellent object is determined according to the priority, so that the first time characteristic information of each excellent object may be established based on the object data in operation S224.
According to an embodiment of the present disclosure, based on the object data of each object to be recommended, evaluating the object to be recommended may include: and evaluating the products to be recommended based on the income data of each product to be recommended.
For example, the object to be recommended may include a financial product A1, a financial product B3, a financial product C5. And evaluating the financial products A1, B3 and C5 according to the profit data of the financial products A1, B3 and C5. If the profit of the three financial products is that the financial product A1 > the financial product B3 > the financial product C5, the priority of the object to be recommended may be that the financial product A1 > the financial product B3 > the financial product C5, so that it is determined that the excellent object may be that the financial product A1 and the financial product B3. The first time characteristic information of the financial product A1 may be established according to the object data of the financial product A1 and the first time characteristic information of the financial product B3 may be established according to the object data of the financial product B3 in operation S224.
In operation S234, second time characteristic information of the target object is determined based on the demand information.
According to an embodiment of the present disclosure, for example, a curve for the profitability of the target object and a curve for the sales share may be constructed according to the demand information.
In operation S244, at least one first time feature information having a second similarity of the plurality of first time feature information to the second time feature information greater than a second threshold is determined.
According to an embodiment of the present disclosure, for example, regression analysis may be performed on the first time feature information and the second time feature information, and the second similarity may be determined according to a result of the regression analysis.
In operation S254, the object to be recommended corresponding to the first time feature information is taken as the target object.
According to the embodiment of the disclosure, the object recommendation method does not need to manually evaluate the object to be recommended, so that the product analysis timeliness is rapidly reduced, and the labor cost is reduced. The method has wide application range, breaks the regional limitation of the product, can be applied to the product recommendation multiplexing between overseas branches and overseas branches of the national banks, between overseas branches and internal branches, between the internal branches and the internal branches, and can be used for the national groups of other industries, the regional industry of the country and the feasibility analysis of different products in the same region.
Fig. 5 schematically illustrates a block diagram of an object recommendation apparatus 500 according to an embodiment of the present disclosure.
As shown in fig. 5, the object recommendation apparatus 500 includes a first acquisition module 510, a first determination module 520, a second acquisition module 530, a second determination module 540, and an output module 550.
The first obtaining module 510 may, for example, perform operation S201 described above with reference to fig. 2, for obtaining user data of a plurality of users, where the plurality of users includes a target user, and the user data includes target user data of the target user.
The first determining module 520 may, for example, perform operation S202 described above with reference to fig. 2, for determining at least one reference user from the plurality of users based on the user data, wherein a first similarity of the user data of the reference user and the target user data is greater than a first threshold, wherein each of the reference users includes an object to be recommended.
The second obtaining module 530 may, for example, perform operation S203 described above with reference to fig. 2, for obtaining the requirement information of the target user.
The second determining module 540 may, for example, perform operation S204 described above with reference to fig. 2, configured to determine a target object from a plurality of objects to be recommended based on the requirement information and the feature information of the objects to be recommended, where a second similarity between the feature information of the target object and the requirement information is greater than a second threshold.
The output module 550 may perform, for example, operation S205 described above with reference to fig. 2, for outputting the target object so as to recommend the target object to the target user.
Fig. 6 schematically illustrates a block diagram of the first determination module 520, according to an embodiment of the disclosure.
As shown in fig. 6, the first determining module 520 includes a first acquiring sub-module 521, an evaluating sub-module 522, a first determining sub-module 523, and a second determining sub-module 524.
The first obtaining sub-module 521 may, for example, perform operation S212 described above with reference to fig. 3 for obtaining the evaluation rule of the evaluation index.
The evaluation sub-module 522 may, for example, perform operation S222 described above with reference to fig. 3, configured to evaluate the evaluation index of each of the plurality of users based on the user data and the evaluation rule, respectively, to obtain an evaluation description of each of the plurality of users.
The first determining submodule 523 may, for example, perform operation S232 described above with reference to fig. 3 for determining, based on the rating description of each user, a reference rating description having a first similarity to the rating description of the target user greater than a first threshold.
The second determination submodule 524 may perform, for example, operation S242 described above with reference to fig. 3, for determining the user corresponding to the reference evaluation description as the reference user.
Fig. 7 schematically illustrates a block diagram of a second determination module 540 according to an embodiment of the disclosure.
As shown in fig. 7, the second determining module 540 includes a second acquiring sub-module 541, a building sub-module 542, a third determining sub-module 543, a fourth determining sub-module 544, and a fifth determining sub-module 545.
The second obtaining sub-module 541 may, for example, perform operation S214 described above with reference to fig. 4, configured to obtain object data of each of the plurality of objects to be recommended in a predetermined period of time.
The establishing sub-module 542 may, for example, perform operation S224 described above with reference to fig. 4, configured to establish the first time feature information of each of the objects to be recommended based on the object data.
The third determining sub-module 543 may perform, for example, operation S234 described above with reference to fig. 4, for determining second time characteristic information of the target object based on the requirement information.
The fourth determining submodule 544 may, for example, perform operation S244 described above with reference to fig. 4 for determining at least one first time feature information in which a plurality of second similarities of the first time feature information and the second time feature information are greater than the second threshold.
The fifth determining sub-module 545 may, for example, perform operation S254 described above with reference to fig. 4, and is configured to take the object to be recommended corresponding to the first time feature information as the target object.
According to an embodiment of the present disclosure, the object to be recommended includes a product to be recommended, and evaluating the object to be recommended based on object data of each object to be recommended includes: and evaluating the products to be recommended based on the income data of each product to be recommended.
Any number of modules, sub-modules, units, sub-units, or at least some of the functionality of any number of the sub-units according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented as split into multiple modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or in any other reasonable manner of hardware or firmware that integrates or encapsulates the circuit, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be at least partially implemented as computer program modules, which when executed, may perform the corresponding functions.
For example, any of the first acquisition module 510, the first determination module 520, the second acquisition module 530, the second determination module 540, and the output module 550 may be combined in one module to be implemented, or any one of the modules may be split into a plurality of modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the first acquisition module 510, the first determination module 520, the second acquisition module 530, the second determination module 540, and the output module 550 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or as hardware or firmware in any other reasonable manner of integrating or packaging the circuitry, or as any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, at least one of the first acquisition module 510, the first determination module 520, the second acquisition module 530, the second determination module 540, and the output module 550 may be at least partially implemented as a computer program module, which when executed, may perform the corresponding functions.
Fig. 8 schematically illustrates a block diagram of an electronic device according to an embodiment of the disclosure. The electronic device shown in fig. 8 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 8, a computer electronic device 800 according to an embodiment of the present disclosure includes a processor 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. The processor 801 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 801 may also include on-board memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing the different actions of the method flows according to embodiments of the disclosure.
In the RAM 803, various programs and data required for the operation of the electronic device 800 are stored. The processor 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. The processor 801 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 802 and/or the RAM 803. Note that the program may be stored in one or more memories other than the ROM 802 and the RAM 803. The processor 801 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, the electronic device 800 may also include an input/output (I/O) interface 805, the input/output (I/O) interface 805 also being connected to the bus 804. The electronic device 800 may also include one or more of the following components connected to the I/O interface 805: an input portion 806 including a keyboard, mouse, etc.; an output portion 807 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 808 including a hard disk or the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. The drive 810 is also connected to the I/O interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as needed so that a computer program read out therefrom is mounted into the storage section 808 as needed.
According to embodiments of the present disclosure, the method flow according to embodiments of the present disclosure may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809, and/or installed from the removable media 811. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 801. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 802 and/or RAM 803 and/or one or more memories other than ROM 802 and RAM 803 described above.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be combined in various combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.
Claims (8)
1. An object recommendation method performed by an electronic device, comprising:
acquiring user data of a plurality of users, wherein the plurality of users comprise target users, and the user data comprise target user data of the target users;
determining at least one reference user from the plurality of users based on the user data, wherein a first similarity of the user data of the reference user and the target user data is greater than a first threshold, and each reference user respectively comprises an object to be recommended;
acquiring demand information of the target user;
determining a target object from a plurality of objects to be recommended based on the demand information and the feature information of the objects to be recommended, wherein the second similarity between the feature information of the target object and the demand information is larger than a second threshold; and
outputting the target object so as to recommend the target object to the target user;
wherein the determining the target object from the plurality of objects to be recommended includes:
acquiring object data of each object to be recommended in a plurality of objects to be recommended in a preset time period;
establishing first time characteristic information of each object to be recommended based on the object data;
determining second time feature information of the target object based on the demand information;
determining at least one first time feature information of which a second similarity between a plurality of the first time feature information and the second time feature information is greater than the second threshold; and
taking the object to be recommended corresponding to the first time characteristic information as the target object;
the first time characteristic information characterizes a profit-over-time profile of the first financial product over the predetermined period of time and a share-over-time profile of the first financial product sold; the second time characteristic information characterizes a curve of the profitability of the target object and a curve of the sales fraction.
2. The method of claim 1, wherein the determining at least one reference user from the plurality of users based on the user data comprises:
acquiring an evaluation rule of an evaluation index;
based on the user data and the evaluation rules, evaluating the evaluation index of each user in the plurality of users respectively to obtain an evaluation description of each user in the plurality of users;
determining, based on the rating description of each user, a reference rating description having a first similarity to the rating description of the target user greater than the first threshold; and
and determining the user corresponding to the reference evaluation description as the reference user.
3. The method of claim 1, further comprising:
evaluating the object to be recommended based on the object data of each object to be recommended to obtain an evaluation result;
determining the priority of the object to be recommended based on the evaluation result, and determining an excellent object according to the priority;
wherein, based on the object data, establishing the first time feature information of each object to be recommended includes:
and establishing first time characteristic information of each excellent object based on the object data.
4. A method according to claim 3, wherein the object to be recommended includes a product to be recommended, and the evaluating the object to be recommended based on the object data of each of the objects to be recommended includes:
and evaluating the products to be recommended based on the income data of each product to be recommended.
5. An object recommendation device, comprising:
the first acquisition module is used for acquiring user data of a plurality of users, wherein the plurality of users comprise target users, and the user data comprise target user data of the target users;
a first determining module, configured to determine at least one reference user from the plurality of users based on the user data, where a first similarity between the user data of the reference user and the target user data is greater than a first threshold, where each reference user includes an object to be recommended;
the second acquisition module is used for acquiring the demand information of the target user;
a second determining module, configured to determine a target object from a plurality of objects to be recommended based on the requirement information and feature information of the objects to be recommended, where a second similarity between the feature information of the target object and the requirement information is greater than a second threshold; and
the output module is used for outputting the target object so as to recommend the target object to the target user;
wherein the second determining module includes:
the second acquisition submodule is used for acquiring object data of each to-be-recommended object in a plurality of to-be-recommended objects within a preset time period;
the establishing sub-module is used for establishing first time characteristic information of each object to be recommended based on the object data;
a third determining sub-module, configured to determine second time feature information of the target object based on the requirement information;
a fourth determining sub-module, configured to determine at least one first time feature information that a plurality of second similarities between the first time feature information and the second time feature information are greater than the second threshold; and
a fifth determining submodule, configured to take an object to be recommended corresponding to the first time feature information as the target object;
the first time characteristic information characterizes a profit-over-time profile of the first financial product over the predetermined period of time and a share-over-time profile of the first financial product sold; the second time characteristic information characterizes a curve of the profitability of the target object and a curve of the sales fraction.
6. The apparatus of claim 5, wherein the first determination module comprises:
the first acquisition submodule is used for acquiring an evaluation rule of the evaluation index;
the evaluation sub-module is used for evaluating the evaluation index of each user in the plurality of users based on the user data and the evaluation rule so as to obtain the evaluation description of each user in the plurality of users;
a first determination sub-module for determining, based on the rating description of each user, a reference rating description having a first similarity to the rating description of the target user greater than a first threshold; and
and the second determining submodule is used for determining the user corresponding to the reference evaluation description as the reference user.
7. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-4.
8. A computer readable storage medium having stored thereon executable instructions which when executed by a processor cause the processor to perform the method of any of claims 1 to 4.
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| CN112100489B (en) * | 2020-08-27 | 2022-07-15 | 北京百度网讯科技有限公司 | Object recommendation method, device and computer storage medium |
| CN112100507B (en) * | 2020-11-10 | 2021-02-12 | 南京研利科技有限公司 | Object recommendation method, computing device and computer-readable storage medium |
| CN112785197B (en) * | 2021-02-04 | 2024-03-29 | 中国工商银行股份有限公司 | Information recommendation method, apparatus, computing device, medium and program product |
| CN114756762B (en) * | 2022-06-13 | 2022-09-02 | 腾讯科技（深圳）有限公司 | Data processing method, device, equipment and storage medium |
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