CN111414548A - Object recommendation method and device, electronic equipment and medium - Google Patents
Object recommendation method and device, electronic equipment and medium Download PDFInfo
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- CN111414548A CN111414548A CN202010389816.3A CN202010389816A CN111414548A CN 111414548 A CN111414548 A CN 111414548A CN 202010389816 A CN202010389816 A CN 202010389816A CN 111414548 A CN111414548 A CN 111414548A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
Abstract
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 comprises target user data of the target users; determining at least one reference user from a plurality of users based on user data, wherein a first similarity between the user data of the reference user and target user data is greater than a first threshold, and each reference user 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 characteristic information of the objects to be recommended, wherein the second similarity between the characteristic information of the target object and the demand information is greater than a second threshold value; and outputting the target object so as to recommend the target object to the target user. The disclosure also provides an object recommendation device, an electronic device and a medium.
Description
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to a robot communication method, an object recommendation apparatus, an electronic device, and a medium.
Background
Currently, a product to be recommended to a specific user is often determined from a plurality of products by a simple filtering or a manual evaluation method.
In implementing the disclosed concept, the inventors found that there are at least the following problems in the related art: it is impossible to accurately recommend a suitable product to the user.
Disclosure of Invention
In view of the above, the present disclosure provides an object recommendation method and an object recommendation apparatus, an electronic device, 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 comprises target user data of the target users; determining at least one reference user from a plurality of users based on user data, wherein a first similarity between the user data of the reference user and target user data is greater than a first threshold, and each reference user 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 characteristic information of the objects to be recommended, wherein the second similarity between the characteristic information of the target object and the demand information is greater than a second threshold value; 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: obtaining an evaluation rule of an evaluation index; based on the user data and the evaluation rule, 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 among 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 within a preset time period; establishing first time characteristic information of each object to be recommended based on the object data; determining second time characteristic information of the target object based on the demand information; determining at least one first time characteristic information with a second similarity between the plurality of first time characteristic information and the second time characteristic information larger than the second threshold; and taking the object to be recommended corresponding to the first time characteristic information as the target object.
According to the embodiment of the disclosure, the method further comprises evaluating the object to be recommended to obtain an evaluation result based on the object data of each object to be recommended; determining the priority of the object to be recommended based on the evaluation result, and determining an excellent object according to the priority; wherein the establishing of the first time characteristic information of each object to be recommended based on the object data comprises: 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 system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring user data of a plurality of users, the plurality of users comprise target users, and the user data comprises target user data of the target users; a first determining module, configured to determine at least one reference user from the multiple 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; the second determining module is used for determining a target object from the objects to be recommended based on the demand information and the characteristic information of the objects to be recommended, wherein the second similarity between the characteristic information of the target object and the demand information is greater than a second threshold value; and the output module is used for outputting the target object so as to recommend the target object to the target user.
According to an embodiment of the present disclosure, the first determining module includes: the first obtaining submodule is used for obtaining an evaluation rule of the evaluation index; the evaluation submodule is used for evaluating the evaluation indexes of each user in the plurality of users respectively based on the user data and the evaluation rules so as to obtain the evaluation description of each user in the plurality of users; a first determination sub-module, configured to determine, based on the evaluation description of each user, a reference evaluation description whose first similarity to the evaluation description of the target user is greater than a first threshold; and a second determining submodule, configured to determine a 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 obtaining submodule is used for obtaining the object data of each object to be recommended in a plurality of objects to be recommended within a preset time period; the establishing submodule is used for establishing first time characteristic information of each object to be recommended based on the object data; the third determining submodule is used for determining second time characteristic information of the target object based on the demand information; a fourth determining submodule, configured to determine at least one first time characteristic information in which a second similarity between the plurality of first time characteristic information and the second time characteristic information is greater than the second threshold; and a fifth determining submodule, configured to use the object to be recommended corresponding to the first time characteristic information as the target object.
Another aspect of the present disclosure provides an electronic device comprising one or more processors; a storage device to store 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 above-described method.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure 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 diagram of object recommendation performed by an electronic device, in accordance with 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 according to an embodiment of the present disclosure;
FIG. 4 is a flow chart of a method for 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 device 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 shows 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 illustrative only 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 disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not 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 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 is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have 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 convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have 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.).
An embodiment 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 comprises target user data of the target users; determining at least one reference user from a plurality of users based on user data, wherein a first similarity between the user data of the reference user and target user data is greater than a first threshold, and each reference user 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 characteristic information of the objects to be recommended, wherein the second similarity between the characteristic information of the target object and the demand information is greater than a second threshold value; 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 is 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 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 multiple branches of a certain bank, and the multiple branches may be distributed in different regions. For example, M of the plurality of branches 101 is located in New York City.
To meet the market demand in new york city, M branch 101 needs to push out new financing products. However, not only a long period but also a large manpower is required for redevelopment of the financial product, and therefore, a new financial product suitable for promotion by the M branch 101 may be selected from the financial products of other branches other than the M branch 101, so that the new financial product is recommended to the M branch 101.
The present disclosure provides an object recommendation method, which can be applied to the above scenario, and which can accurately select a financing product suitable for promotion by the M branch 101 from a plurality of branches.
Fig. 2 schematically shows a flow chart of object recommendation performed by an electronic device according to an embodiment of the present 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 the regulatory environment, geographic personality, scale of operations, etc. of the various sub-divisions.
For another example, in a scenario where a shopping site recommends goods to a target user, the user may be a member of a certain shopping site, 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 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 includes an object to be recommended.
For example, the user data of other users than the target user in the plurality of users may be compared with the target user data, so that the user with the first similarity between the user data of the other users and the target user data larger than the first threshold value is taken as the reference user. For example, the value of the first threshold may be set by a 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 operating environment information of the other sub-division except for the M sub-division is compared with the operating environment information of the M sub-division, and the like, to determine a first similarity between the operating environment of the other sub-division and the operating environment of the M sub-division. The operation environment information may include, for example, a regulatory environment, a geographical humanity, an operation scale, and the like. Next, the sub-line of the other sub-lines whose first similarity is greater than the first threshold may be used as the 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 article, or the like.
In operation S203, demand information of a target user is acquired.
According to the embodiment of the present disclosure, the requirement information of the target user may include, for example, a description of the target user about the characteristics of the target object. The demand information may be, for example, a description of profitability of the financial product by the M branch, a limitation on buying or selling the financial product, or the like. For another example, in a scenario where a shopping website recommends a commodity to a target user, the demand information may be a description of the target user's usage, size, etc. of the commodity.
According to an embodiment of the present disclosure, for example, a target user may input requirement information in a user interaction interface, so that an electronic device executing the method collects the requirement 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 demand information and the feature information of the objects to be recommended, wherein a second similarity between the feature information of the target object and the demand information is greater than a second threshold. For example, the value of the second threshold may be 0.9. The second threshold value may be set by the person skilled in the art himself.
According to the embodiment of the disclosure, the feature information of the object to be recommended may be a description of a feature of the object to be recommended. For example, if the object to be recommended is a financial product that has already been promoted by other branches, the characteristic information of the object to be recommended may be a profit curve of the financial product, which varies with time. For another example, if the object to be recommended is a table on a shopping site, the feature information of the object to be recommended may be a feature such as a color and a size 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 each of the feature information of the plurality of objects to be recommended and the requirement information. Next, an object to be recommended, of which the second similarity between the feature information and the demand information is greater than a second threshold, is determined from the plurality of objects to be recommended.
In operation S205, the target object is output so as to be recommended 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 recommendation method can determine the reference user with characteristics similar to those of the target user from the multiple users, so that the target object meeting the requirements of the target user is screened from the objects to be recommended included by the reference user, and the accuracy of recommending the target object to the target user is improved.
Fig. 3 schematically shows a flow chart of a method of determining at least one reference user from a plurality of users according to an embodiment of the present disclosure.
As shown in fig. 3, the method may include operations S212 to S242.
In operation S212, an evaluation rule of the evaluation index is acquired.
According to the embodiment of the present disclosure, the evaluation rule of the evaluation index may be, for example, formulated by a person skilled in the art according to actual conditions, and may be stored in the storage unit of the electronic device, so that the electronic device executing 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 larger than 200 yen and large scale, the total resources are medium scale in 200 to 100 yen, and the total assets are small scale in less than 100 yen. For example, the evaluation rule corresponding to the evaluation index "geographic humanity" may be that the local per-capita deposit is excellent at more than 10 ten thousand yuan, the local per-capita deposit is good at 10 ten thousand yuan to 3 ten thousand yuan, the local per-capita deposit is good at 3 ten thousand yuan to 1 ten thousand yuan, and the local per-capita deposit is poor at less than 1 ten thousand yuan. Wherein, the evaluation rule may further include a mapping table, for example, which may specify that the score for the large scale may be 10, the score for the medium scale may be 5, the score for the small scale may be 1, and the score for the excellent scale may be 10, the score for the good scale may be 8, and the score for the passing scale may be 5, and the score for the failing scale 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 to obtain an evaluation description of each of the plurality of users, respectively.
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 humanity and an operation scale, and the evaluation description of the M division according to the evaluation rule may be, for example, that the operation scale is a medium scale, the geographical humanity is excellent, and the supervision environment is strict.
In operation S232, based on the rating description of each user, a reference rating description having a first similarity with the rating description of the target user greater than a first threshold is determined.
According to the embodiment of the present disclosure, for example, the electronic device may calculate the score of each user according to the rating description and the mapping table of the rating rule, so as to determine the similarity with the rating description of the target user according to 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, and the difference is used as the first similarity, and if the difference is greater than the first threshold, the surface 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 a 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 a first threshold.
In operation S242, a user corresponding to the reference evaluation description is determined as a reference user. That is, a user whose first similarity to the evaluation description of the target user is greater than a first threshold is determined as the reference user.
Fig. 4 schematically shows a flowchart of a method for determining a target object from a plurality of objects to be recommended according to an embodiment of the present disclosure.
As shown in fig. 4, the method may include operations S214 to S254.
In operation S214, object data of each of a plurality of objects to be recommended within a predetermined period of time is acquired.
For example, object data such as the weekly profitability data of financial product A1 in division A, the share of sold financial product A1, etc. may be obtained for 1 year.
In operation S224, first time characteristic information of each object to be recommended is established based on the object data.
According to an embodiment of the present disclosure, a change curve of profit of financial product a1 person for 1 year and a change curve of share of sold financial product a1 over time may be constructed, for example, according to the weekly profit data of financial product a 1.
According to an embodiment of the present disclosure, for example, a time series model of a financial product may be established from the object data, and first time characteristic information of the financial product may be 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 objects to be recommended are evaluated based on the object data of each object to be recommended to obtain an evaluation result, and the priority of the objects to be recommended is determined based on the evaluation result, and the excellent objects are 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, evaluating the objects to be recommended based on the object data of each 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 objects to be recommended may include financial product a1, financial product B3, and financial product C5. Financial product A1, financial product B3 and financial product C5 are evaluated based on profitability data of financial product A1, financial product B3 and financial product C5. If the profit sizes of the above three financial products are financial product a1 > financial product B3 > financial product C5, the priority of the object to be recommended may be financial product a1 > financial product B3 > financial product C5, so that it is determined that the excellent objects may be financial product a1 and financial product B3. First time characteristic information of financial product a1 may be established based on the object data of financial product a1, and first time characteristic information of financial product B3 may be established based on the object data of financial product B3 in operation S224.
In operation S234, second temporal feature information of the target object is determined based on the demand information.
According to the embodiment of the disclosure, for example, a curve of profit and a curve of sold share of the target object may be constructed according to the demand information.
In operation S244, at least one first time characteristic information, in which a second similarity of the plurality of first time characteristic information to the second time characteristic information is greater than a second threshold, is determined.
According to the embodiment of the present disclosure, for example, regression analysis may be performed on the first time characteristic information and the second time characteristic information, and the second similarity degree may be determined according to a result of the regression analysis.
In operation S254, an object to be recommended corresponding to the first time characteristic information is set as a target object.
According to the object recommendation method, the object to be recommended does not need to be evaluated manually, the product analysis timeliness is reduced rapidly, and the labor cost is reduced. The application range of the method is wide, the regional limitation of the product is broken through, and the method can be applied to the product recommendation and reuse between overseas branches of international banks, overseas branches and domestic branches, and can be used for the feasibility analysis of different products in other industries and international groups, local transregional industries and the same region.
Fig. 5 schematically illustrates a block diagram of an object recommendation device 500 according to an embodiment of the present disclosure.
As shown in fig. 5, the object recommending apparatus 500 includes a first obtaining module 510, a first determining module 520, a second obtaining module 530, a second determining module 540, and an output module 550.
The first obtaining module 510, for example, may 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, for example, may perform operation S202 described above with reference to fig. 2, and is configured to determine at least one reference user from the multiple 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 obtaining module 530 may, for example, perform operation S203 described above with reference to fig. 2, so as to obtain the requirement information of the target user.
The second determining module 540, for example, may perform operation S204 described above with reference to fig. 2, and is configured to determine a target object from the multiple 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, for example, may perform the 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 obtaining 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, for example, may perform the operation S212 described above with reference to fig. 3, for obtaining the evaluation rule of the evaluation index.
The evaluation sub-module 522, for example, may perform the operation S222 described above with reference to fig. 3, and is configured to evaluate the evaluation index of each of the multiple users respectively based on the user data and the evaluation rule to obtain an evaluation description of each of the multiple users.
The first determining sub-module 523 may, for example, perform operation S232 described above with reference to fig. 3, and is configured to determine, based on the evaluation description of each user, a reference evaluation description having a first similarity with the evaluation description of the target user greater than a first threshold.
The second determining sub-module 524, for example, may perform 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 the second determination module 540 according to an embodiment of the disclosure.
As shown in fig. 7, the second determination module 540 includes a second acquisition sub-module 541, a setup sub-module 542, a third determination sub-module 543, a fourth determination sub-module 544, and a fifth determination sub-module 545.
The second obtaining sub-module 541, for example, may perform operation S214 described above with reference to fig. 4, configured to obtain object data of each of the objects to be recommended in a plurality of objects to be recommended within a predetermined time period.
The creating sub-module 542, for example, may perform the operation S224 described above with reference to fig. 4, for creating the first time characteristic information of each of the objects to be recommended based on the object data.
The third determining sub-module 543, for example, may perform operation S234 described above with reference to fig. 4, for determining second temporal feature information of the target object based on the demand information.
The fourth determining sub-module 544 may, for example, perform operation S244 described above with reference to fig. 4, and is configured to determine at least one first time characteristic information of which the second similarity between the plurality of first time characteristic information and the second time characteristic information is greater than the second threshold.
The fifth determining sub-module 545 may perform, for example, the operation S254 described above with reference to fig. 4, and is configured to use the object to be recommended corresponding to the first time characteristic information as the target object.
According to the embodiment of the disclosure, the objects to be recommended comprise products to be recommended, and the evaluation of the objects to be recommended based on the object data of each object to be recommended comprises: and evaluating the products to be recommended based on the income data of each product to be recommended.
Any one or more of the modules, sub-modules, units, sub-units, or sub-units according to embodiments of the present disclosure may be implemented at least in part as hardware circuitry, e.g., a Field Programmable Gate Array (FPGA), a programmable logic array (P L a), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or any other reasonable manner of integrating or packaging circuitry, or in any one of or a suitable combination of software, hardware, and firmware.
For example, any number of the first obtaining module 510, the first determining module 520, the second obtaining module 530, the second determining module 540, and the output module 550 may be combined into one module, or any one of them may be split into a plurality of modules, or at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module according to embodiments of the present disclosure, at least one of the first obtaining module 510, the first determining module 520, the second obtaining module 530, the second determining module 540, and the output module 550 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a programmable logic array (P L A), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or any other reasonable manner that may integrate or package circuits, or the like, or may be implemented in any one or suitable combination of any number of software, hardware and any number of three, or the first obtaining module 510, the second determining module 540, and the output module 550 may be implemented as a computer program when executed, the computer module executes at least part of the computer module 530, the computer module, and the computer module may execute the program to implement at least one computer program.
FIG. 8 schematically shows a block diagram of an electronic device according to an embodiment of the disclosure. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 8, a computer electronic device 800 according to an embodiment of the present disclosure includes a processor 801 which 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 associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.
In the RAM 803, various programs and data necessary for the operation of the electronic apparatus 800 are stored. The processor 801, the ROM802, and the RAM 803 are connected to each other by a bus 804. The processor 801 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM802 and/or RAM 803. Note that the programs may also be stored in one or more memories other than the ROM802 and RAM 803. The processor 801 may also perform various operations of 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 apparatus 800 may further include AN input/output (I/O) interface 805, the input/output (I/O) interface 805 also being connected to the bus 804, the electronic apparatus 800 may further include one or more of AN input section 806 including a keyboard, a mouse, and the like, AN output section 807 including a Cathode Ray Tube (CRT), a liquid crystal display (L CD), and the like, a speaker, and the like, a storage section 808 including a hard disk, and the like, and a communication section 809 including a network interface card such as a L AN card, a modem, and the like, the communication section 809 performs communication processing via a network such as the Internet, the driver 810 is also connected to the I/O interface 805 as necessary, a removable medium 811 such as a magnetic disk, AN optical disk, a magneto-optical disk, a semiconductor memory 810, and the like is mounted on the driver 810 as necessary, so that a computer program read out therefrom is mounted into the storage section 808 as necessary.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. 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 containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program, when executed by the processor 801, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the 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 present 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, a computer-readable storage medium may include the ROM802 and/or RAM 803 described above and/or one or more memories other than the ROM802 and RAM 803.
The flowchart 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 various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been 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 separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (10)
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 comprises 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 between the user data of the reference user and the target user data is greater than a first threshold, and each reference user comprises an object to be recommended;
acquiring the demand information of the target user;
determining a target object from the objects to be recommended based on the demand information and the characteristic information of the objects to be recommended, wherein a second similarity between the characteristic information of the target object and the demand information is greater than a second threshold value; and
outputting the target object so as to recommend the target object to the target user.
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:
obtaining an evaluation rule of an evaluation index;
based on the user data and the evaluation rule, 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 according to claim 1 or 2, wherein the determining a target object from the plurality of objects to be recommended comprises:
acquiring object data of each object to be recommended in a plurality of objects to be recommended within a preset time period;
establishing first time characteristic information of each object to be recommended based on the object data;
determining second time characteristic information of the target object based on the demand information;
determining at least one first time characteristic information with a second similarity between the plurality of first time characteristic information and the second time characteristic information larger than the second threshold; and
and taking the object to be recommended corresponding to the first time characteristic information as the target object.
4. The method of claim 3, further comprising:
evaluating the objects 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 the establishing of the first time characteristic information of each object to be recommended based on the object data comprises:
establishing first time characteristic information of each excellent object based on the object data.
5. The method according to claim 4, wherein the object to be recommended comprises a product to be recommended, and the evaluating the object to be recommended based on the object data of each object to be recommended comprises:
and evaluating the products to be recommended based on the income data of each product to be recommended.
6. An object recommendation apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring user data of a plurality of users, the plurality of users comprise target users, and the user data comprises target user data of the target users;
a first determining module, configured to determine at least one reference user from the multiple 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;
the second determining module is used for determining a target object from the objects to be recommended based on the demand information and the characteristic information of the objects to be recommended, wherein the second similarity between the characteristic information of the target object and the demand information is greater than a second threshold value; and
and the output module is used for outputting the target object so as to recommend the target object to the target user.
7. The apparatus of claim 6, wherein the first determining means comprises:
the first obtaining submodule is used for obtaining an evaluation rule of the evaluation index;
the evaluation submodule is used for evaluating the evaluation indexes of each user in the plurality of users respectively based on the user data and the evaluation rules so as to obtain the evaluation description of each user in the plurality of users;
a first determination sub-module, configured to determine, based on the evaluation description of each user, a reference evaluation description whose first similarity to the evaluation description of the target user is greater than a first threshold; and
a second determining submodule, configured to determine a user corresponding to the reference evaluation description as the reference user.
8. The apparatus of claim 6 or 7, wherein the second determining means comprises:
the second obtaining submodule is used for obtaining the object data of each object to be recommended in a plurality of objects to be recommended within a preset time period;
the establishing submodule is used for establishing first time characteristic information of each object to be recommended based on the object data;
the third determining submodule is used for determining second time characteristic information of the target object based on the demand information;
a fourth determining submodule, configured to determine at least one first time characteristic information in which a second similarity between the plurality of first time characteristic information and the second time characteristic information is greater than the second threshold; and
and the fifth determining submodule is used for taking the object to be recommended corresponding to the first time characteristic information as the target object.
9. An electronic device, comprising:
one or more processors;
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 of any of claims 1-5.
10. 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 one of claims 1 to 5.
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