CN114581174A - Recommendation method and device, electronic equipment and storage medium - Google Patents
Recommendation method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN114581174A CN114581174A CN202210117747.XA CN202210117747A CN114581174A CN 114581174 A CN114581174 A CN 114581174A CN 202210117747 A CN202210117747 A CN 202210117747A CN 114581174 A CN114581174 A CN 114581174A
- Authority
- CN
- China
- Prior art keywords
- commodity
- information
- commodity information
- determining
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 230000001960 triggered effect Effects 0.000 claims abstract description 10
- 238000004590 computer program Methods 0.000 claims description 12
- 230000000875 corresponding effect Effects 0.000 description 27
- 238000012545 processing Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention belongs to the technical field of online platform commodity recommendation, and particularly relates to a recommendation method, a recommendation device, electronic equipment and a storage medium. According to the method and the device, the scene mode of the commodity of the user is determined by obtaining the browsing condition of the historical order of the user, the database corresponding to the commodity is determined by the scene mode, and the commodity information with high association level with the order commodity browsed by the user in the database is pushed to the user, so that the commodity recommendation mode is optimized, the user can quickly obtain the commodity information related to the order, the commodity preview quantity is increased, and the commodity transaction rate is improved. A recommendation method, comprising: acquiring first commodity information in order information triggered by a user; determining a scene mode of a first commodity corresponding to the first commodity information according to the first commodity information; determining a database corresponding to the scene mode; and determining target commodity information in the database according to the first commodity information.
Description
Technical Field
The invention belongs to the technical field of online platform commodity recommendation, and particularly relates to a recommendation method, a recommendation device, electronic equipment and a storage medium.
Background
The rapid development of online shopping causes the influence of the internet on social life to be larger and larger, and the types of commodities on the shopping network are richer and richer. However, when shopping, the user needs to spend a lot of time to select the desired goods, and it is difficult to quickly purchase the goods in his mind, which further affects the transaction rate of the platform and the shopping efficiency of the user.
In order to solve the existing problems, the current platforms are all provided with a commodity recommendation mode. However, the existing commodity recommendation mode is only to recommend hot commodities, and cannot make the recommended commodities form pertinence to users. The existing commodity recommendation method is easy to cause poor user experience, and the purchasing desire of a user cannot be stimulated, so that the commodity recommendation effect is not ideal, and the purchasing rate of recommended commodities is difficult to improve.
Disclosure of Invention
In view of the above technical problems, the present invention provides a recommendation method, an apparatus, an electronic device and a storage medium. According to the method and the device, the scene mode of the commodity of the user is determined by obtaining the browsing condition of the historical order of the user, the database corresponding to the commodity is determined by the scene mode, and the commodity information with high association level with the order commodity browsed by the user in the database is pushed to the user, so that the commodity recommendation mode is optimized, the user can quickly obtain the commodity information related to the order, the commodity preview quantity is increased, and the commodity transaction rate is improved.
In order to solve the above technical problem, the technical solution adopted by the present invention includes four aspects.
In a first aspect, a recommendation method is provided, including: acquiring first commodity information in order information triggered by a user; determining a scene mode of a first commodity corresponding to the first commodity information according to the first commodity information; determining a database corresponding to the scene mode; determining target commodity information in the database according to the first commodity information; and recommending the target commodity information to the user.
In some embodiments, the first merchandise information includes: the commodities are numbered originally; the determining, according to the first commodity information, the scene mode to which the first commodity corresponding to the first commodity information belongs includes: and determining the scene mode of the first commodity according to the commodity original number.
In some embodiments, the determining target commodity information in the database according to the first commodity information includes: acquiring second commodity information of each second commodity in the database; determining the association level between the second commodity and the first commodity according to the second commodity information and the first commodity information; and determining the second commodity information with the association level exceeding the association level threshold value as the target commodity information.
In some embodiments, after obtaining the first commodity information in the order information triggered by the user, the method further comprises: acquiring the triggering times of the order information; when the triggering times are larger than or equal to a first threshold value, acquiring an attached commodity of a first commodity corresponding to the first commodity information; recommending the auxiliary commodity to the user.
In some embodiments, the method further comprises: acquiring order data of the deal; determining a scene mode corresponding to each commodity according to commodity information of each commodity in the order data; and establishing a corresponding relation between each scene mode and the database.
In some embodiments, the method further comprises: acquiring commodity information of each commodity in each database; determining the association level among the commodities according to the commodity information of the commodities; the association levels between the respective commodities are stored.
In some embodiments, the commodity information of each commodity includes at least one of: price, size, colour.
In some embodiments, after the determining the database corresponding to the scene mode, the method further comprises: acquiring hot commodity information of hot commodities in the database; sorting the hot commodities according to the hot indexes of the hot commodity information; acquiring target hot commodity information within a sorting threshold value in the hot commodities; and recommending the target hot commodity information to the user.
In a second aspect, the present application provides a recommendation apparatus, comprising: the first acquisition module is used for acquiring first commodity information in order information triggered by a user; the first determining module is used for determining the scene mode of the first commodity corresponding to the first commodity information according to the first commodity information; determining a database corresponding to the scene mode; the second determining module is used for determining target commodity information in the database according to the first commodity information; and the first execution module is used for recommending the target commodity information to the user.
A third aspect provides an electronic device comprising a storage storing a computer program and a processor implementing the steps of a recommendation method when executing the computer program.
A fourth aspect provides a storage medium storing a computer program executable by one or more processors, the computer program being operable to implement the steps of a method as recommended in any one of the first aspects.
The beneficial effects created by the invention are as follows: according to the method and the device, the scene mode of the commodity purchased by the user is determined by obtaining the browsing condition of the historical order of the user, the database corresponding to the purchased commodity is determined by the scene mode, the target commodity information is further determined in the database, and the target commodity information is pushed to the user, so that the commodity recommendation mode is optimized, the user can rapidly obtain the commodity information related to the order, the commodity preview quantity is increased, and the commodity transaction rate is improved.
Drawings
The scope of the present disclosure may be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings. Wherein the included drawings are:
fig. 1 is an overall flowchart of a recommendation method provided in an embodiment of the present application;
fig. 2 is a block diagram of a recommendation device according to an embodiment of the present application.
Detailed Description
In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the attached drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
The following description will be added if a similar description of "first \ second \ third" appears in the application file, and in the following description, the terms "first \ second \ third" merely distinguish similar objects and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may be interchanged under certain circumstances in a specific order or sequence, so that the embodiments of the application described herein can be implemented in an order other than that shown or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
Example 1:
to solve the problems in the background art, as shown in fig. 1, the present application provides a recommendation method, which is applied to an electronic device, where the electronic device may be a server, a mobile terminal, a computer, a cloud platform, and the like. The functions implemented by the device data processing provided by the embodiment of the present application may be implemented by calling a program code by a processor of an electronic device, where the program code may be stored in a computer storage medium, and the recommendation method includes:
step S1: and acquiring first commodity information in order information triggered by a user.
When the user operates the online platform on the completed order, the triggering of the order is completed. The operation here includes operations of browsing details, clicking to view, and the like. And when the operation of the user on the order is detected, obtaining the order information in the order operated by the user. And acquiring commodity information in the order information, and in order to distinguish obviously, the commodity is called as first commodity information.
Step S2: and determining the scene mode of the first commodity corresponding to the first commodity information according to the first commodity information. And determining a database corresponding to the scene mode.
The first product information includes basic information of the first product, such as an original number, price, size, and color of the first product.
In some embodiments, step S2 ": determining the scene mode of the first commodity corresponding to the first commodity information according to the first commodity information, the method comprises the following steps:
step S21: and determining the scene mode of the first commodity according to the commodity original number.
The original number of the commodity describes the scene mode of the commodity, namely the scene of the commodity commonly used in life. For example, the refrigerator is mainly used for storing food and is related to food making, so that the refrigerator belongs to a kitchen scene, and the original number can be given according to the functional characteristics of the refrigerator, and the format of the original number can be CF-DBX. The CF is used for indicating that the commodity belongs to the kitchen mode, so that the scene mode of the commodity can be determined through the original number of the commodity.
In some embodiments, the recommendation method further discloses how to establish the corresponding relationship between each scene mode and the database. The method comprises the following steps:
step S22: acquiring the order data of the deal.
All historical order data that has been committed is obtained.
Step S23: and determining a scene mode corresponding to each commodity according to the commodity information of each commodity in the order data.
And determining the scene mode of each commodity according to the functional characteristics of each commodity in the historical order data.
Step S24: and establishing a corresponding relation between each scene mode and the database.
Each commodity in the same scene mode is divided into different databases according to other commodity information such as size, color and price. For example, a double-door refrigerator may be relegated to a database of large-sized goods in a kitchen setting. For example a white refrigerator may be attributed to a light color family database in a kitchen scene. Therefore, in addition to determining the scene mode to which the first commodity belongs through the original number, a database corresponding to the scene mode needs to be determined through the size, price, color, and the like of the first commodity. These items that have an association with the first item may all be recommended to the user.
Step S3: and determining target commodity information in the database according to the first commodity information.
A selection is made among a plurality of associated databases based on the size, color, price, etc. of the first item, with the item that the user may like as the target item.
In some embodiments, the step S3 of determining target commodity information in the database according to the first commodity information includes:
step S31: and acquiring second commodity information of each second commodity in the database.
For ease of distinction, the second item is used herein to denote the item of the database that is associated with the first item. The article information of the second article is the second article information.
Step S32: and determining the association level between the second commodity and the first commodity according to the second commodity information and the first commodity information.
In some embodiments, the method of the present application further discloses how to obtain a level of association between the second item and the first item, the method further comprising:
step S321: and acquiring commodity information of each commodity in each database.
The commodity information includes: color, price and size of the goods.
Step S322: and determining the association level among the commodities according to the commodity information of the commodities.
The association level is determined by each item of information of the second item. Generally, association grade assignment can be performed on the second commodity according to various information of the second commodity, and finally, the final association grade of the second commodity is obtained. And the final association grade is the sum of the association grade assignments of all information. The assignment of the association level depends on the user's preference characteristics. And the association grade assignment conditions corresponding to different users are different.
Therefore, the method of the present application further comprises:
step S324: and acquiring a historical order record of the user. And analyzing the personal preference of the user according to the information of each commodity in the historical order record.
For example, by analyzing all shopping orders of the user, it is found that the user prefers light-colored goods, next larger-sized goods, and finally price-wise high-cost-performance goods. Then, when the association level is determined, the color information of the commodity brings the largest association level assignment to the commodity, the size of the commodity is the second largest association level assignment, and the cost performance of the commodity is the third largest association level assignment. Of course, the association level assignment therein also looks at the proximity to the product.
The association level main user expresses the association degree between at least two different commodities in the same scene mode. The characteristics of the two commodities can be correlated by acquiring the information of the two commodities, so that the correlation degree between the two commodities is obtained. Such as rice cookers and refrigerators, are also in kitchen settings. Wherein the electric cooker is 100 yuan, which belongs to low-price goods in the electric cooker, and the refrigerator of 300 yuan also belongs to low-price products in the refrigerator, so the association grade assignment between the electric cooker of 100 yuan and the refrigerator of 300 yuan is larger. However, a 1000 yuan middle-grade refrigerator is also arranged at the moment, and then the association grade assignment between the 1000 yuan refrigerator and the 100 yuan electric cooker in the aspect of price is lower.
Step S323: the association levels between the respective commodities are stored.
And (4) assigning and superposing the association levels of the commodity information of each commodity, and finally calculating the association level between the commodity and other commodities, wherein the association level is used as a screening condition in the step S32.
Step S33: and determining the second commodity information with the association level exceeding the association level threshold value as the target commodity information.
And recommending the commodities with the relevance grades not exceeding the preset relevance grade threshold value as target commodities. Wherein the associated merchandise threshold is dependent on a rate of purchase of the recommended merchandise by the user.
Step S4: and recommending the target commodity information to the user.
And sending the commodity information of the target commodity to the user. The terminal used by the user can be displayed in a pop-up window mode, and the user can be made to know the target commodity in other modes. Therefore, the user can quickly acquire the commodity information related to the order, the commodity preview quantity is increased, and the commodity transaction rate is increased.
The above steps S1 to S4 are mainly for recommending merchandise according to merchandise characteristics of the user' S order, but sometimes the user may need to purchase merchandise that is not in the same scene pattern as the purchased merchandise, and may be additional merchandise or subsidiary merchandise of the purchased merchandise. For example, if the user's order is a mobile phone, the user may want to buy the accessory items such as the headset, the data line, the mobile phone cradle, etc.
Therefore, in some embodiments, after "acquiring the first commodity information in the order information triggered by the user" in step S1, the method further includes:
step S5: and acquiring the triggering times of the order information.
Step S6: and when the triggering times are larger than or equal to a first threshold value, acquiring the attached commodity of the first commodity corresponding to the first commodity information.
Generally, when a user triggers order information within a limited number of times, a commodity recommendation which belongs to the same scene mode and has a high association level with a first commodity is obtained. However, if the user triggers the order information for many times, the commodity recommendation which is not in the same scene and is required by the user is indicated. The user may desire an ancillary item to the first item. When the number of times of triggering the order information is greater than or equal to 3 times, the attached commodity of the first commodity is acquired.
Step S7: recommending the auxiliary commodity to the user.
And sending the commodity information of the attached commodity to the user. The terminal used by the user can be displayed in a pop-up window mode, and the user can be made to know the target commodity in other modes. Therefore, the user can quickly acquire the attached commodity information related to the order, the commodity preview quantity is increased, and the commodity transaction rate is increased.
In order to improve the transaction rate and the preview amount, and the recommendation may have errors, the method of the present application further includes recommending popular goods to the user in order to improve the fault tolerance rate.
In some embodiments, after the step S2 "determining the database corresponding to the scene mode", the method further includes:
step S81: and acquiring hot commodity information of the hot commodities in the database.
Step S82: and sequencing the hot commodities according to the hot indexes of the hot commodity information.
The trending index of the trending commodity is calculated mainly depending on the sales information about the commodity, such as the purchase rate, the repurchase rate and the collection conversion rate of other users to the commodity.
Step S83: and acquiring target hot commodity information within a sorting threshold value in the hot commodities.
Step S84: and recommending the target hot commodity information to the user.
The hot commodity recommendation mainly recommends the user for the purchased commodities according to the same scene mode of different users, so that the user can quickly acquire the accessory commodity information related to the order, the commodity preview quantity is increased, and the commodity transaction rate is increased.
Example 2:
based on the foregoing embodiments, the present application provides a recommendation apparatus, where the modules included in the apparatus and the units included in the modules may be implemented by a processor in a computer device; of course, the implementation can also be realized through a specific logic circuit; in the implementation process, the processor may be a Central Processing Unit (CPU), a Microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.
As shown in fig. 2, a second aspect provides a recommendation apparatus comprising: the device comprises a first acquisition module 1, a first determination module 2, a second determination module 3 and a first execution module 4.
The first obtaining module 1 is configured to obtain first commodity information in order information triggered by a user. The first determining module 2 is configured to determine, according to the first commodity information, a scene mode to which a first commodity corresponding to the first commodity information belongs. And determining a database corresponding to the scene mode. The second determining module 3 is configured to determine target commodity information in the database according to the first commodity information. And the first execution module 4 is used for recommending the target commodity information to the user.
In some embodiments, the first determining module 2 comprises: and a third determining module. And the third determining module is used for determining the scene mode of the first commodity according to the commodity original number.
In some embodiments, the second determination module 3 comprises: the device comprises a second obtaining module, a seventh determining module and a fourth determining module.
The second obtaining module is used for obtaining second commodity information of each second commodity in the database. The seventh determining module is used for determining the association level between the second commodity and the first commodity according to the second commodity information and the first commodity information. The fourth determining module is used for determining the second commodity information with the association level exceeding the association level threshold value as the target commodity information.
In some embodiments, the recommendation device further comprises: the device comprises a third acquisition module, a fourth acquisition module and a second execution module.
And the third acquisition module is used for acquiring the triggering times of the order information. The fourth obtaining module is configured to obtain an affiliated commodity of the first commodity corresponding to the first commodity information when the number of times of triggering is greater than or equal to a first threshold. And the third execution module is used for recommending the auxiliary commodity to the user.
In some embodiments, the recommendation device further comprises: the device comprises a fifth acquisition module, a fifth determination module and a fourth execution module.
The fifth acquisition module is used for acquiring the order data of the deal. And the fifth determining module is used for determining the scene mode corresponding to each commodity according to the commodity information of each commodity in the order data. The fourth execution module is used for establishing the corresponding relation between each scene mode and the database.
In some embodiments, the recommendation device further comprises: the device comprises a sixth acquisition module, a sixth determination module and a fifth execution module.
The sixth acquisition module is used for acquiring the commodity information of each commodity in each database. The sixth determining module is used for determining the association level among the commodities according to the commodity information of the commodities. And the fifth execution module is used for storing the association level among the commodities.
In some embodiments, the recommendation device further comprises: the device comprises a seventh acquisition module, a sixth execution module, an eighth acquisition module and a seventh execution module.
The seventh obtaining module is used for obtaining hot commodity information of the hot commodities in the database. And the sixth execution module is used for sequencing the hot commodities according to the hot indexes of the hot commodity information. The eighth obtaining module is used for obtaining the target hot commodity information in the hot commodities within the sorting threshold. And the seventh execution module is used for recommending the target popular commodity information to the user.
The modules in the recommendation device can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the device, and can also be stored in a memory in the processing device in a software form, so that the processor can call and execute operations corresponding to the modules. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation.
Example 3:
a third aspect provides an electronic device comprising a storage and a processor, the storage storing a computer program, the processor implementing the steps of a recommendation method when executing the computer program.
Example 4:
a fourth aspect provides a storage medium storing a computer program executable by one or more processors, the computer program being operable to implement the steps of the method as recommended in any one of the first aspects.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not imply any order of execution, and the order of execution of the processes should be determined by their functions and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only for the embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (11)
1. A recommendation method, comprising:
acquiring first commodity information in order information triggered by a user;
determining a scene mode of a first commodity corresponding to the first commodity information according to the first commodity information; determining a database corresponding to the scene mode;
determining target commodity information in the database according to the first commodity information;
and recommending the target commodity information to the user.
2. The recommendation method according to claim 1, wherein the first commodity information includes: the commodities are numbered originally; the determining, according to the first commodity information, the scene mode to which the first commodity corresponding to the first commodity information belongs includes:
and determining the scene mode of the first commodity according to the commodity original number.
3. The recommendation method according to claim 1, wherein the determining target commodity information in the database according to the first commodity information comprises:
acquiring second commodity information of each second commodity in the database;
determining the association level between the second commodity and the first commodity according to the second commodity information and the first commodity information;
and determining the second commodity information with the association level exceeding the association level threshold value as the target commodity information.
4. The recommendation method according to claim 1, wherein after acquiring the first item information in the order information triggered by the user, the method further comprises:
acquiring the triggering times of the order information;
when the triggering times are larger than or equal to a first threshold value, acquiring an attached commodity of a first commodity corresponding to the first commodity information;
and recommending the auxiliary commodity to the user.
5. A recommendation method according to claim 3, characterized in that said method further comprises:
acquiring order data of the deal;
determining a scene mode corresponding to each commodity according to commodity information of each commodity in the order data;
and establishing a corresponding relation between each scene mode and the database.
6. The method of claim 5, further comprising:
acquiring commodity information of each commodity in each database;
determining the association level among the commodities according to the commodity information of the commodities;
the association levels between the respective commodities are stored.
7. The method of claim 6, wherein the merchandise information of each merchandise item comprises at least one of: price, size, colour.
8. The recommendation method according to claim 1, wherein after said determining the database corresponding to the scene mode, the method further comprises:
acquiring hot commodity information of hot commodities in the database;
sorting the hot commodities according to the hot indexes of the hot commodity information;
acquiring target hot commodity information within a sorting threshold value in the hot commodities;
and recommending the target hot commodity information to the user.
9. A recommendation device, comprising:
the first acquisition module is used for acquiring first commodity information in order information triggered by a user;
the first determining module is used for determining the scene mode of the first commodity corresponding to the first commodity information according to the first commodity information; determining a database corresponding to the scene mode;
the second determining module is used for determining target commodity information in the database according to the first commodity information;
and the first execution module is used for recommending the target commodity information to the user.
10. An electronic device, comprising:
a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs a method of recommendation according to any one of claims 1 to 8.
11. A storage medium storing a computer program executable by one or more processors, the computer program being operable to implement the steps of a recommendation method as claimed in any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210117747.XA CN114581174A (en) | 2022-02-08 | 2022-02-08 | Recommendation method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210117747.XA CN114581174A (en) | 2022-02-08 | 2022-02-08 | Recommendation method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114581174A true CN114581174A (en) | 2022-06-03 |
Family
ID=81772667
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210117747.XA Pending CN114581174A (en) | 2022-02-08 | 2022-02-08 | Recommendation method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114581174A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114820142A (en) * | 2022-06-29 | 2022-07-29 | 国能(北京)商务网络有限公司 | Commodity information recommendation method facing to B-end purchasing user |
| CN115880037A (en) * | 2023-03-03 | 2023-03-31 | 量子数科科技有限公司 | Commodity recommendation method based on multi-project planning integration analysis |
-
2022
- 2022-02-08 CN CN202210117747.XA patent/CN114581174A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114820142A (en) * | 2022-06-29 | 2022-07-29 | 国能(北京)商务网络有限公司 | Commodity information recommendation method facing to B-end purchasing user |
| CN114820142B (en) * | 2022-06-29 | 2022-09-16 | 国能(北京)商务网络有限公司 | Commodity information recommendation method for B-side purchasing user |
| CN115880037A (en) * | 2023-03-03 | 2023-03-31 | 量子数科科技有限公司 | Commodity recommendation method based on multi-project planning integration analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114581174A (en) | Recommendation method and device, electronic equipment and storage medium | |
| CN113327151B (en) | Commodity object recommendation method, commodity object recommendation device, computer equipment and storage medium | |
| CN108288219A (en) | The method for pushing and system of food product information | |
| CN110674391B (en) | Product data pushing method and system based on big data and computer equipment | |
| JP5831204B2 (en) | Information providing system, information providing method, and program | |
| CN112561581A (en) | Recommendation method and device, electronic equipment and storage medium | |
| CN113327152A (en) | Commodity recommendation method and device, computer equipment and storage medium | |
| CN111899068A (en) | Commodity shopping guide method and device, storage medium and computer equipment | |
| CN111144874A (en) | Payment mode recommendation method, device and equipment | |
| CN112307274A (en) | Inventory information query method and device | |
| CN113689259A (en) | Commodity personalized recommendation method and system based on user behaviors | |
| CN113689258A (en) | Commodity recommendation heat generation method and system | |
| CN110827104A (en) | Method and device for recommending commodities to user | |
| CN113554532B (en) | Broker list page ordering method, storage medium and program product | |
| CN115018593A (en) | Independent site commodity recommendation method and device, equipment, medium and product thereof | |
| CN114926237A (en) | Commodity recommendation method and device, storage medium and electronic equipment | |
| CN110570271A (en) | information recommendation method and device, electronic equipment and readable storage medium | |
| CN110555752A (en) | Personalized recommendation method and device, electronic equipment and readable storage medium | |
| CN113516539A (en) | Commodity recommendation method and device and computer-readable storage medium | |
| JP6424194B2 (en) | Information processing apparatus, information processing method, and information processing program | |
| CN116228342B (en) | Commodity recommendation method and device and computer readable storage medium | |
| CN112529666A (en) | Commodity recommendation method, commodity recommendation system, computer equipment and storage medium | |
| CN116664222A (en) | Data processing method and device | |
| CN115860865A (en) | Commodity combination construction method and device, equipment, medium and product thereof | |
| CN114677198A (en) | Sheet splitting method and device, storage medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |