CN111754300A - Commodity recommendation method, commodity recommendation device, commodity recommendation equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a commodity recommendation method, a commodity recommendation device, commodity recommendation equipment and a storage medium. The method comprises the following steps: acquiring user characteristics of a target user; screening out related recommended commodities from the commodities to be recommended according to the user characteristics; screening out non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended; and displaying the related recommended commodities and the non-related recommended commodities to the target user according to a preset sequence. The embodiment of the invention realizes the purpose of scattering recommended commodities and improving user experience.
Description
Technical Field
The embodiment of the invention relates to the internet technology, in particular to a commodity recommendation method, a commodity recommendation device, commodity recommendation equipment and a storage medium.
Background
With the development of internet technology, more and more people tend to use online shopping, and accordingly, in order to improve the purchasing power of users, shopping platforms begin to recommend related commodities to users while the users use the shopping platforms.
At present, recommended commodities are aggregated due to relevance, for example, a plurality of commodities of a category of a commodity with high relevance to a user are aggregated together, and it often happens that tens of adjacent commodities are all of the same category or the same brand.
Disclosure of Invention
The embodiment of the invention provides a commodity recommendation method, a commodity recommendation device, commodity recommendation equipment and a storage medium, and aims to break up recommended commodities and improve user experience.
To achieve the object, an embodiment of the present invention provides a method for recommending a commodity, including:
acquiring user characteristics of a target user;
screening out related recommended commodities from the commodities to be recommended according to the user characteristics;
screening out non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended;
and displaying the related recommended commodities and the non-related recommended commodities to the target user according to a preset sequence.
Further, the user characteristics include first commodity information of commodities viewed by a target user, second commodity information of real-time hot-sold commodities, and user information of the target user.
Further, the screening of the related recommended commodities from the commodities to be recommended according to the user characteristics includes:
respectively converting the first commodity information, the second commodity information and the user information into a first feature vector, a second feature vector and a third feature vector;
inputting the commodity to be recommended, the first feature vector, the second feature vector and the third feature vector into a preset model to obtain a recommendation score of the commodity to be recommended;
and screening the commodities with the recommendation scores in the preset number from the commodities to be recommended as related recommended commodities.
Further, the step of inputting the to-be-recommended commodity, the first feature vector, the second feature vector and the third feature vector into a preset model to obtain the recommendation score of the to-be-recommended commodity includes:
combining the first feature vector, the second feature vector and the third feature vector to obtain a fourth feature vector;
and inputting the commodity to be recommended, the first characteristic vector, the second characteristic vector, the third characteristic vector and the fourth characteristic vector into a preset model to obtain a recommendation score of the commodity to be recommended.
Further, the displaying the relevant recommended commodities and the non-relevant recommended commodities to the target user according to the preset sequence includes:
combining the related recommended commodities and the non-related recommended commodities corresponding to the related recommended commodities to serve as a display module;
and displaying the display module to the target user according to the ranking order of the recommendation scores.
Further, the screening of the non-related recommended goods with the lowest similarity to the related recommended goods from the goods to be recommended includes:
acquiring the similarity between the to-be-recommended commodity and the related recommended commodity;
and screening the non-related recommended commodities with the lowest similarity with the related recommended commodities from the commodities to be recommended according to the similarity.
Further, the obtaining of the similarity between the to-be-recommended item and the related recommended item includes:
respectively converting the commodity name of the to-be-recommended commodity and the commodity name of the related recommended commodity into a first vector array and a second vector array;
and obtaining the similarity of the first vector array and the second vector array according to a cosine formula.
On one hand, the embodiment of the invention also provides a commodity recommending device, which comprises:
the characteristic acquisition module is used for acquiring the user characteristics of the target user;
the commodity screening module is used for screening out related recommended commodities from the commodities to be recommended according to the user characteristics;
the commodity screening module is further used for screening the non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended;
and the commodity display module is used for displaying the related recommended commodities and the non-related recommended commodities to the target user according to a preset sequence.
On the other hand, an embodiment of the present invention further provides a computer device, where the computer device includes: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method as provided by any embodiment of the invention.
In yet another aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method provided in any embodiment of the present invention.
The embodiment of the invention obtains the user characteristics of the target user; screening out related recommended commodities from the commodities to be recommended according to the user characteristics; screening out non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended; the related recommended commodities and the non-related recommended commodities are displayed to the target user according to the preset sequence, the problems that a plurality of commodities are very similar and fatigue is easily generated when the user views the recommended commodities are solved, and the effect of scattering the recommended commodities and improving user experience is achieved.
Drawings
Fig. 1 is a schematic flow chart of a commodity recommendation method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a commodity recommendation method according to a second embodiment of the present invention;
fig. 3 is a flowchart illustrating a step S220 of a merchandise recommendation method according to a second embodiment of the present invention;
fig. 4 is a flowchart illustrating a step S230 of a method for recommending a commodity according to a second embodiment of the present invention;
fig. 5 is a schematic structural diagram of a commodity recommending apparatus according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are for purposes of illustration and not limitation. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, the first commodity information may be referred to as second commodity information, and similarly, the second commodity information may be referred to as first commodity information, without departing from the scope of the present application. Both the first commodity information and the second commodity information are commodity information, but they are not the same commodity information. The terms "first", "second", etc. are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the embodiments of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Example one
As shown in fig. 1, a first embodiment of the present invention provides a method for recommending a commodity, where the method includes:
and S110, acquiring the user characteristics of the target user.
And S120, screening out related recommended commodities from the commodities to be recommended according to the user characteristics.
In this embodiment, when recommending a commodity to a target user, user characteristics of the target user need to be obtained first, where the user characteristics of the target user may be obtained in advance, and are directly obtained for use when recommending the commodity. After the user characteristics of the target user are obtained, relevant recommended commodities can be screened from the commodities to be recommended according to the user characteristics, the commodities to be recommended can be all commodities or commodities needing to be recommended after the merchant pays a fee, and the relevant recommended commodities screened from the commodities to be recommended are commodities which are interesting or possibly purchased by the user and are screened according to the user characteristics.
S130, screening the non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended.
And S140, displaying the relevant recommended commodities and the non-relevant recommended commodities to the target user according to a preset sequence.
In this embodiment, in order to improve user experience and prevent a user from fatigue, a non-related recommended product with the lowest similarity to a related recommended product needs to be screened from the to-be-recommended product, and then the products are displayed to a target user according to a preset sequence, where the similarity may be the similarity of product names or the similarity of product categories, and the preset sequence may be a random disorganized sequence, and although for the user characteristics of the target user, the target user may not be interested in the non-related recommended product, but the obtained user characteristics of the target user may not be comprehensive, the target user may still have an interested product but not embodied in the user characteristics, and the non-related recommended product may also excite a new purchase direction of the target user, so that the related recommended product and the non-related recommended product are recommended by disorganized sequence, under the condition that the target user does not feel fatigue, the method can also stimulate the new shopping direction of the target user, and greatly improves the shopping experience of the target user.
The embodiment of the invention obtains the user characteristics of the target user; screening out related recommended commodities from the commodities to be recommended according to the user characteristics; screening out non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended; the related recommended commodities and the non-related recommended commodities are displayed to the target user according to the preset sequence, the problems that a plurality of commodities are very similar and fatigue is easily caused when the user views the recommended commodities are solved, and the effect of scattering the recommended commodities and improving user experience is achieved.
Example two
As shown in fig. 2, a second embodiment of the present invention provides a method for recommending a commodity, and the second embodiment of the present invention provides a further explanation and explanation on the basis of the first embodiment of the present invention, where the method includes:
s210, obtaining user characteristics of the target user, wherein the user characteristics comprise first commodity information of commodities viewed by the target user, second commodity information of real-time hot-sold commodities and user information of the target user.
In this embodiment, the user characteristics of the target user include first commodity information of a commodity viewed by the target user, second commodity information of a real-time hot-sold commodity, and user information of the target user, where the first commodity information is information of a commodity historically clicked or currently clicked by the target user on a shopping platform, specifically, names, categories, brands, and the like of the commodities, and the user information of the target user may be consumption capability, income information, and the like of the target user.
S220, screening out related recommended commodities from the commodities to be recommended according to the user characteristics.
And S230, screening the non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended.
S240, combining the relevant recommended commodities and the non-relevant recommended commodities corresponding to the relevant recommended commodities to serve as a display module.
And S250, displaying the display module to the target user according to the ranking sequence of the recommendation scores.
In this embodiment, after the relevant recommended commodities are screened from the commodities to be recommended according to the user characteristics, and the non-relevant recommended commodities with the lowest similarity to the relevant recommended commodities are further screened from the commodities to be recommended, the commodities can be displayed in a scattered manner, specifically, the displaying sequence can be sorted according to the recommendation scores of the relevant recommended commodities when the relevant recommended commodities are obtained.
Illustratively, related recommended commodities A1, A2 and B1 are screened from commodities to be recommended, wherein letters are categories, numbers are brands, further, a non-related recommended commodity with the lowest similarity of A1 is C2, a non-related recommended commodity with the lowest similarity of A2 is C1, a non-related recommended commodity with the lowest similarity of B1 is B2, the related recommended commodity and the non-related recommended commodity corresponding to the related recommended commodity are combined to serve as a display module, namely, A1 and C2 serve as the display module, A2 and C1 serve as the display module, B1 and B2 serve as the display module, and if the recommendation score of A1 is greater than A2 and greater than B1, display is carried out according to the sequence of A1, C2, A2, C1, B1 and B2. Similarly, a1, a2 and B1 may be placed in fixed positions of the presentation in the current order, while C2, C1 and B2 are inserted as non-fixed positions behind a1, a2 and B1, respectively, and presented in the order of a1, C2, a2, C1, B1 and B2.
Further, as shown in fig. 3, step S220 in the embodiment of the present invention specifically includes:
s221, converting the first commodity information, the second commodity information and the user information into a first feature vector, a second feature vector and a third feature vector respectively.
S222, combining the first feature vector, the second feature vector and the third feature vector to obtain a fourth feature vector.
And S223, inputting the to-be-recommended commodity, the first feature vector, the second feature vector, the third feature vector and the fourth feature vector into a preset model to obtain a recommendation score of the to-be-recommended commodity.
S224, screening out commodities with the recommended scores in the preset number from the commodities to be recommended as related recommended commodities.
In this embodiment, when relevant recommended commodities are screened out from commodities to be recommended according to user characteristics, first commodity information, second commodity information and user information are respectively converted into a first feature vector, a second feature vector and a third feature vector, further, the first feature vector, the second feature vector and the third feature vector may be combined to obtain a fourth feature vector which better conforms to a target user, then the commodities to be recommended, the first feature vector, the second feature vector, the third feature vector and the fourth feature vector may be input into a preset model to obtain a recommendation score of the commodities to be recommended, wherein the preset model may be a pre-trained neural network model, and finally the recommendation score of each commodity to be recommended may be obtained, and the higher the recommendation score is closer to a commodity which the target user may purchase. And finally, ranking the commodities to be recommended according to the recommendation scores, taking the commodities to be recommended which are in the preset number as related recommended commodities, and exemplarily selecting 100 commodities to be recommended which are in the front of the recommendation scores as the related recommended commodities.
Further, as shown in fig. 4, step S230 in the embodiment of the present invention specifically includes:
s231, the commodity name of the to-be-recommended commodity and the commodity name of the related recommended commodity are converted into a first vector array and a second vector array respectively.
And S232, obtaining the similarity of the first vector array and the second vector array according to a cosine formula.
And S233, screening the non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended according to the similarity.
In this embodiment, when a non-relevant recommended commodity with the lowest similarity to a relevant recommended commodity is screened out from the to-be-recommended commodities, the commodity name of the to-be-recommended commodity and the commodity name of the relevant recommended commodity need to be converted into a first vector array and a second vector array respectively, and the names are used as criteria of the similarity, where the process may be a timed offline task, does not occupy high-frequency time of a platform used by a target user, and is stored in a cache after the conversion is completed for use. When the method is used, the similarity of the first vector array and the second vector array is obtained according to a cosine formula, and then one to-be-recommended commodity with the lowest similarity to the related recommended commodity is screened out from the to-be-recommended commodities to serve as a non-related recommended commodity corresponding to the related recommended commodity.
Illustratively, the product to be recommended is referred to as "razor", the corresponding vector name in this string is < shaving, beard, knife >, the value of the first vector array is <1,1,1>, the product name of the relevant recommended product is "beer", the value of the second vector array is <1,0,1>, the result of dot multiplication is 1+ 0+1 is 2, the modulus of the first vector array is root number 3, and the modulus of the second vector array is root number 2, and the result of similarity is 0.81.
EXAMPLE III
As shown in fig. 5, a third embodiment of the invention provides a product recommendation device 100, and the product recommendation device 100 provided in the third embodiment of the invention can execute the product recommendation method provided in any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. The merchandise recommendation device 100 includes a feature acquisition module 200, a merchandise screening module 300, and a merchandise display module 400.
Specifically, the feature obtaining module 200 is configured to obtain a user feature of the target user; the commodity screening module 300 is used for screening out relevant recommended commodities from the commodities to be recommended according to the user characteristics; the commodity screening module 300 is further configured to screen the non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended; the merchandise display module 400 is configured to display the relevant recommended merchandise and the non-relevant recommended merchandise to the target user according to a preset order.
In this embodiment, the user characteristics include first commodity information of a commodity viewed by a target user, second commodity information of a real-time hot-sold commodity, and user information of the target user. The commodity screening module 300 is specifically configured to convert the first commodity information, the second commodity information, and the user information into a first feature vector, a second feature vector, and a third feature vector, respectively; inputting the commodity to be recommended, the first feature vector, the second feature vector and the third feature vector into a preset model to obtain a recommendation score of the commodity to be recommended; and screening the commodities with the recommended scores in the pre-preset number as the related recommended commodities from the commodities to be recommended. The commodity screening module 300 is further specifically configured to combine the first feature vector, the second feature vector, and the third feature vector to obtain a fourth feature vector; and inputting the commodity to be recommended, the first feature vector, the second feature vector, the third feature vector and the fourth feature vector into a preset model to obtain a recommendation score of the commodity to be recommended. The commodity screening module 300 is further configured to obtain similarity between the commodity to be recommended and the related recommended commodity; and screening the non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended according to the similarity. The commodity screening module 300 is further specifically configured to convert the commodity name of the commodity to be recommended and the commodity name of the related recommended commodity into a first vector array and a second vector array, respectively; and obtaining the similarity of the first vector array and the second vector array according to a cosine formula.
Further, the commodity display module 400 is specifically configured to combine the related recommended commodities and the non-related recommended commodities corresponding to the related recommended commodities as a display module; and displaying the display modules to the target user according to the ranking order of the recommendation scores.
Example four
Fig. 6 is a schematic structural diagram of a computer device 12 according to a fourth embodiment of the present invention. FIG. 6 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in FIG. 6 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 6, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, implementing the methods provided by the embodiments of the present invention:
acquiring user characteristics of a target user;
screening out related recommended commodities from the commodities to be recommended according to the user characteristics;
screening out non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended;
and displaying the related recommended commodities and the non-related recommended commodities to the target user according to a preset sequence.
EXAMPLE five
The fifth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the methods provided in all the embodiments of the present invention of the present application:
acquiring user characteristics of a target user;
screening out related recommended commodities from the commodities to be recommended according to the user characteristics;
screening out non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended;
and displaying the related recommended commodities and the non-related recommended commodities to the target user according to a preset sequence.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for recommending an article, comprising:
acquiring user characteristics of a target user;
screening out related recommended commodities from the commodities to be recommended according to the user characteristics;
screening out non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended;
and displaying the related recommended commodities and the non-related recommended commodities to the target user according to a preset sequence.
2. The method of claim 1, wherein the user characteristics include first commodity information of commodities viewed by a target user, second commodity information of real-time hot commodities, and user information of the target user.
3. The method according to claim 2, wherein the screening out relevant recommended commodities from the commodities to be recommended according to the user characteristics comprises:
respectively converting the first commodity information, the second commodity information and the user information into a first feature vector, a second feature vector and a third feature vector;
inputting the commodity to be recommended, the first feature vector, the second feature vector and the third feature vector into a preset model to obtain a recommendation score of the commodity to be recommended;
and screening the commodities with the recommendation scores in the preset number from the commodities to be recommended as related recommended commodities.
4. The method according to claim 3, wherein the inputting the to-be-recommended commodity, the first feature vector, the second feature vector and the third feature vector into a preset model to obtain the recommendation score of the to-be-recommended commodity comprises:
combining the first feature vector, the second feature vector and the third feature vector to obtain a fourth feature vector;
and inputting the commodity to be recommended, the first characteristic vector, the second characteristic vector, the third characteristic vector and the fourth characteristic vector into a preset model to obtain a recommendation score of the commodity to be recommended.
5. The method of claim 3, wherein the presenting the related recommended merchandise and the non-related recommended merchandise to the target user in a preset order comprises:
combining the related recommended commodities and the non-related recommended commodities corresponding to the related recommended commodities to serve as a display module;
and displaying the display module to the target user according to the ranking order of the recommendation scores.
6. The method according to claim 1, wherein the step of screening the non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended comprises the following steps:
acquiring the similarity between the to-be-recommended commodity and the related recommended commodity;
and screening the non-related recommended commodities with the lowest similarity with the related recommended commodities from the commodities to be recommended according to the similarity.
7. The method according to claim 6, wherein the obtaining the similarity between the to-be-recommended item and the related recommended item comprises:
respectively converting the commodity name of the to-be-recommended commodity and the commodity name of the related recommended commodity into a first vector array and a second vector array;
and obtaining the similarity of the first vector array and the second vector array according to a cosine formula.
8. An article recommendation device, comprising:
the characteristic acquisition module is used for acquiring the user characteristics of the target user;
the commodity screening module is used for screening out related recommended commodities from the commodities to be recommended according to the user characteristics;
the commodity screening module is further used for screening the non-related recommended commodities with the lowest similarity to the related recommended commodities from the commodities to be recommended;
and the commodity display module is used for displaying the related recommended commodities and the non-related recommended commodities to the target user according to a preset sequence.
9. A computer device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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| CN202010576350.8A CN111754300A (en) | 2020-06-22 | 2020-06-22 | Commodity recommendation method, commodity recommendation device, commodity recommendation equipment and storage medium |
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| CN202010576350.8A CN111754300A (en) | 2020-06-22 | 2020-06-22 | Commodity recommendation method, commodity recommendation device, commodity recommendation equipment and storage medium |
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