CN117635254A - Product recommendation method, system, computer equipment and storage medium - Google Patents
Product recommendation method, system, computer equipment and storage medium Download PDFInfo
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- CN117635254A CN117635254A CN202311417465.2A CN202311417465A CN117635254A CN 117635254 A CN117635254 A CN 117635254A CN 202311417465 A CN202311417465 A CN 202311417465A CN 117635254 A CN117635254 A CN 117635254A
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Abstract
The invention discloses a product recommendation method, a system, computer equipment and a storage medium, and relates to the technical field of product recommendation, wherein the method comprises the following steps: obtaining average interval duration of purchasing a first target product by each target user, wherein the target user refers to: a user purchasing a first target product at least twice; dividing a plurality of target users into a plurality of groups according to a preset average interval duration range of the average interval duration; and according to any group of corresponding preset average interval duration ranges, product information of the first target product is sent to the intelligent terminal of each target user in the group. According to the invention, the commodity recommendation is carried out on each target user in each group based on the average interval time, so that a mathematical model is not required to be established, the accuracy and efficiency of recommendation can be ensured, and the user experience can be improved.
Description
Technical Field
The present invention relates to the field of product recommendation technologies, and in particular, to a product recommendation method, system, computer device, and storage medium.
Background
At present, except for a way of blindly sending product information to be recommended, a data model of each user is often built based on the buying habit of each user, and then product recommendation is performed based on the data model of each user, but the technical problems of large data processing amount and low efficiency exist.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects of the prior art, and particularly provides a product recommendation method, a system, computer equipment and a storage medium, wherein the method comprises the following steps:
1) In a first aspect, the present invention provides a product recommendation method, which specifically includes the following technical solutions:
obtaining average interval duration of purchasing a first target product by each target user, wherein the target user refers to: a user purchasing a first target product at least twice;
dividing a plurality of target users into a plurality of groups according to a preset average interval duration range of the average interval duration;
and according to any group of corresponding preset average interval duration ranges, product information of the first target product is sent to the intelligent terminal of each target user in the group.
The product recommendation method provided by the invention has the beneficial effects that:
considering from the commodity side, based on the average interval duration, commodity recommendation is carried out on each target user in each group, a mathematical model is not required to be established, the accuracy and efficiency of recommendation can be ensured, and the user experience degree can be improved.
Based on the scheme, the product recommendation method can be improved as follows.
Further, the method further comprises the following steps:
according to the historical purchasing records of each target user, calculating the first product purchasing similarity between every two target users;
dividing target users in each group according to a preset product purchase similarity range to which the first product purchase similarity belongs to obtain a plurality of subgroups;
and determining a second target product according to the historical purchase record of any target user, and sending product information of the second target product to the intelligent terminals of each target user in the subgroup to which the target user belongs.
The beneficial effects of adopting the further scheme are as follows: the target users in each group are divided again, and accurate recommendation can be performed on other products.
Further, the method further comprises the following steps:
acquiring users to be recommended which have social relations with preset target users, acquiring historical purchase records of each user to be recommended, calculating second product purchase similarity between the preset target users and each user to be recommended, and sending product information of a first target product to an intelligent terminal of the user to be recommended corresponding to the second product purchase similarity which is larger than a preset similarity threshold, wherein the preset target user is any target user.
The beneficial effects of adopting the further scheme are as follows: the recommendation range of the product can be accurately enlarged, and the success probability of the product is improved.
Further, a process of recommending product information of the first target product to each target user includes:
and sending short links for pointing to the product information of the target products to the intelligent terminals of each target user, and pointing to the product information of the target products when any target user clicks the short links on the intelligent terminals.
The beneficial effects of adopting the further scheme are as follows: and the product recommendation is performed in a short link mode, so that the management is more convenient.
2) In a second aspect, the invention further provides a product recommendation system, which comprises the following specific technical schemes:
the system comprises an acquisition module, a grouping module and a first recommendation module;
the acquisition module is used for: obtaining average interval duration of purchasing a first target product by each target user, wherein the target user refers to: a user purchasing a first target product at least twice;
the grouping module is used for: dividing a plurality of target users into a plurality of groups according to a preset average interval duration range of the average interval duration;
the first recommendation module is used for: and according to any group of corresponding preset average interval duration ranges, product information of the first target product is sent to the intelligent terminal of each target user in the group.
Based on the scheme, the product recommendation system can be improved as follows.
Further, the system also comprises a second recommendation module, wherein the second recommendation module is used for:
according to the historical purchasing records of each target user, calculating the first product purchasing similarity between every two target users;
dividing target users in each group according to a preset product purchase similarity range to which the first product purchase similarity belongs to obtain a plurality of subgroups;
and determining a second target product according to the historical purchase record of any target user, and sending product information of the second target product to the intelligent terminals of each target user in the subgroup to which the target user belongs.
Further, the system also comprises a third recommendation module, wherein the third recommendation module is used for:
acquiring users to be recommended which have social relations with preset target users, acquiring historical purchase records of each user to be recommended, calculating second product purchase similarity between the preset target users and each user to be recommended, and sending product information of a first target product to an intelligent terminal of the user to be recommended corresponding to the second product purchase similarity which is larger than a preset similarity threshold, wherein the preset target user is any target user.
Further, the process of recommending the product information of the first target product to each target user by the first recommending module comprises the following steps:
and sending short links for pointing to the product information of the target products to the intelligent terminals of each target user, and pointing to the product information of the target products when any target user clicks the short links on the intelligent terminals.
3) In a third aspect, the present invention also provides a computer device, where the computer device includes a processor, and the processor is coupled to a memory, where at least one computer program is stored, where the at least one computer program is loaded and executed by the processor, so that the computer device implements any one of the product recommendation methods described above.
4) In a fourth aspect, the present invention also provides a computer readable storage medium, in which at least one computer program is stored, the at least one computer program being loaded and executed by a processor, so that the computer implements any one of the product recommendation methods described above.
It should be noted that, the technical solutions of the second aspect to the fourth aspect and the corresponding possible implementation manners of the present invention may refer to the technical effects of the first aspect and the corresponding possible implementation manners of the first aspect, which are not described herein.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings in which:
FIG. 1 is a schematic flow chart of a product recommendation method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a product recommendation system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, a product recommendation method according to an embodiment of the present invention includes the following steps:
s1, acquiring average interval duration of purchasing a first target product by each target user, wherein the target users refer to: a user purchasing a first target product at least twice;
the average interval duration of any target user purchasing the first target product is calculated as follows:
1) The first way is: dividing the preset time period by the number of times of purchasing the first target product by any target user in a preset time period, such as 6 months or one year, so as to obtain the average interval duration of purchasing the first target product by the target user;
2) The second way is: acquiring the interval duration T between the time when any target user first purchases the first target product and the time when the target user last purchases the first target product in a preset time period such as 6 months or one year Duration of interval And uses the formula: t=t Duration of interval And (2) calculating the average interval duration of the first target product purchased by the target user, wherein n represents the total times of the first target product purchased by the target user in a preset time period.
It should be noted that, although the average interval durations calculated in the first mode and the second mode are different, this does not affect the technical effects of "neither establishing a mathematical model nor ensuring the accuracy and efficiency of recommendation" of the present invention, and improving the user experience.
S2, dividing the multiple target users into multiple groups according to a preset average interval duration range of the average interval duration;
the method comprises the steps of dividing a plurality of preset average interval duration ranges according to preset interval days, for example, the preset interval days are 5 days, wherein the first preset average interval duration range is 0-5 days, the second preset average interval duration range is 6-10 days, the third preset average interval duration range is 11-15 days and the like, and the preset interval days can be set according to practical conditions.
S3, according to the preset average interval duration range corresponding to any group, product information of the first target product is sent to the intelligent terminal of each target user in the group.
The explanation of the "the first preset average interval duration range is 0-5 days" is performed, the product information of the first target product is sent to the intelligent terminals of each target user in the group every 5 days, and the explanation of the "the second preset average interval duration range is 6-10 days" is performed, the product information of the first target product is sent to the intelligent terminals of each target user in the group every 10 days.
According to the product recommendation method, consideration is given to the product side, and based on the average interval duration, commodity recommendation is carried out on each target user in each group, so that mathematical models are not required to be built, the accuracy and efficiency of recommendation can be guaranteed, and the user experience can be improved.
Optionally, in the above technical solution, the method further includes:
s4, calculating first product purchase similarity between every two target users according to the historical purchase records of each target user, wherein the first product purchase similarity can be understood as follows:
1) The first way of understanding: taking the similarity of products purchased between any two target users as the first product purchase similarity between the two target users;
specifically, for each commodity purchased by the target user, the points of the target users are accumulated by 1, for example, the point of the first target user in a preset time period is 20, the point of the second target user in the preset time period is 25, wherein the point of the first target user and the second target user purchasing the same product is 10, and 10/(20+25-10) is taken as the first product purchase similarity between the first target user and the second target user.
It should be noted that the first product purchase similarity may also be calculated according to other existing manners.
2) The first way of understanding: taking the similarity of the product class purchased between any two target users as the first product purchase similarity between the two target users; products such as cosmetics and household appliances.
Specifically, each time the target user purchases a commodity of a commodity class, the points of the target users are accumulated by 1, for example, the point of the first target user in a preset time period is 10, the point of the second target user in the preset time period is 15, wherein the point of the first target user and the second target user purchasing products of the same product class is 5, and 5/(10+15-5) is taken as the first product purchase similarity between the first target user and the second target user.
It should be noted that the first product purchase similarity may also be calculated according to other existing manners.
S5, dividing target users in each group according to a preset product purchase similarity range to which the first product purchase similarity belongs to obtain a plurality of subgroups, wherein the first product purchase similarity between every two target users in each subgroup is within the preset product purchase similarity range to which the subgroup belongs, and the plurality of preset product purchase similarity ranges can be set according to actual conditions.
S6, determining a second target product according to the historical purchase record of any target user, and sending product information of the second target product to the intelligent terminals of each target user in the subgroup to which the target user belongs, wherein the second target product can be any commodity in the historical purchase record of the target user or can be set according to actual conditions.
In this embodiment, the target users in each group are subdivided, so that accurate recommendation can be performed on other products.
Optionally, in the above technical solution, the method further includes:
s7, acquiring users to be recommended which have social relations with preset target users, acquiring historical purchase records of each user to be recommended, calculating second product purchase similarity between the preset target users and each user to be recommended, and sending product information of a first target product to an intelligent terminal of the user to be recommended corresponding to the second product purchase similarity which is larger than a preset similarity threshold, wherein the preset target user is any target user, the product recommendation range can be accurately enlarged, and the success probability of the product is improved.
The user to be recommended having social relation with the preset target user means that: the calculation manner of the second product purchase similarity refers to the calculation manner of the first product purchase similarity, and is not described herein.
Optionally, in the above technical solution, the process of recommending the product information of the first target product to each target user includes:
s30, sending short links for pointing to product information of target products to intelligent terminals of each target user, and setting specific formats of the short links according to actual conditions when any target user clicks the short links on the intelligent terminals.
In the above embodiments, although steps S1, S2, etc. are numbered, only specific embodiments of the present invention are given, and those skilled in the art may adjust the execution sequence of S1, S2, etc. according to the actual situation, which is also within the scope of the present invention, and it is understood that some embodiments may include some or all of the above embodiments.
As shown in fig. 2, a product recommendation system 200 according to an embodiment of the present invention includes an acquisition module 201, a grouping module 202, and a first recommendation module 203;
the acquisition module 201 is configured to: obtaining average interval duration of purchasing a first target product by each target user, wherein the target user refers to: a user purchasing a first target product at least twice;
the grouping module 202 is configured to: dividing a plurality of target users into a plurality of groups according to a preset average interval duration range of the average interval duration;
the first recommendation module 203 is configured to: and according to any group of corresponding preset average interval duration ranges, product information of the first target product is sent to the intelligent terminal of each target user in the group.
Optionally, in the above technical solution, the system further includes a second recommendation module, where the second recommendation module is configured to:
according to the historical purchasing records of each target user, calculating the first product purchasing similarity between every two target users;
dividing target users in each group according to a preset product purchase similarity range to which the first product purchase similarity belongs to obtain a plurality of subgroups;
and determining a second target product according to the historical purchase record of any target user, and sending product information of the second target product to the intelligent terminals of each target user in the subgroup to which the target user belongs.
Optionally, in the above technical solution, the system further includes a third recommendation module, where the third recommendation module is configured to:
acquiring users to be recommended which have social relations with preset target users, acquiring historical purchase records of each user to be recommended, calculating second product purchase similarity between the preset target users and each user to be recommended, and sending product information of a first target product to an intelligent terminal of the user to be recommended corresponding to the second product purchase similarity which is larger than a preset similarity threshold, wherein the preset target user is any target user.
Optionally, in the above technical solution, the process of recommending, by the first recommending module 203, the product information of the first target product to each target user includes:
and sending short links for pointing to the product information of the target products to the intelligent terminals of each target user, and pointing to the product information of the target products when any target user clicks the short links on the intelligent terminals.
It should be noted that, the beneficial effects of the product recommendation system 200 provided in the above embodiment are the same as those of the product recommendation method described above, and will not be described herein. In addition, when the system provided in the above embodiment implements the functions thereof, only the division of the above functional modules is used as an example, in practical application, the above functional allocation may be implemented by different functional modules according to needs, that is, the system is divided into different functional modules according to practical situations, so as to implement all or part of the functions described above. In addition, the system and method embodiments provided in the foregoing embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.
As shown in fig. 3, in a computer device 300 according to an embodiment of the present invention, the computer device 300 includes a processor 320, the processor 320 is coupled to a memory 310, at least one computer program 330 is stored in the memory 310, and the at least one computer program 330 is loaded and executed by the processor 320, so that the computer device 300 implements any one of the product recommendation methods described above, specifically:
the computer device 300 may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, where the one or more memories 310 store at least one computer program 330, and the at least one computer program 330 is loaded and executed by the one or more processors 320 to enable the computer device 300 to implement any one of the product recommendation methods provided in the embodiments above. Of course, the computer device 300 may also have a wired or wireless network interface, a keyboard, an input/output interface, and other components for implementing the functions of the device, which are not described herein.
The embodiment of the invention provides a computer readable storage medium, wherein at least one computer program is stored in the computer readable storage medium, and the at least one computer program is loaded and executed by a processor, so that a computer realizes any one of the product recommendation methods.
Alternatively, the computer readable storage medium may be a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a compact disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform any of the product recommendation methods described above.
It should be noted that the terms "first," "second," and the like in the description and in the claims of the present application are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. The order of use of similar objects may be interchanged where appropriate so that embodiments of the present application described herein may be implemented in other sequences than those illustrated or described.
Those skilled in the art will appreciate that the invention may be embodied as a system, method or computer program product, and that the invention may therefore be embodied in the form of: either entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or entirely software, or a combination of hardware and software, referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media, which contain computer-readable program code.
Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. 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.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. A method of product recommendation, comprising:
obtaining average interval duration of purchasing a first target product by each target user, wherein the target user refers to: a user purchasing the first target product at least twice;
dividing a plurality of target users into a plurality of groups according to a preset average interval duration range of the average interval duration;
and according to any group of corresponding preset average interval duration ranges, sending the product information of the first target product to the intelligent terminal of each target user in the group.
2. The product recommendation method as claimed in claim 1, further comprising:
according to the historical purchasing records of each target user, calculating the first product purchasing similarity between every two target users;
dividing target users in each group according to a preset product purchase similarity range to which the first product purchase similarity belongs to obtain a plurality of subgroups;
and determining a second target product according to the historical purchase record of any target user, and sending product information of the second target product to the intelligent terminals of each target user in the subgroup to which the target user belongs.
3. A product recommendation method according to claim 1 or 2, further comprising:
acquiring users to be recommended which have social relations with preset target users, acquiring historical purchase records of each user to be recommended, calculating second product purchase similarity between the preset target users and each user to be recommended, and sending product information of the first target product to an intelligent terminal of the user to be recommended corresponding to the second product purchase similarity which is larger than a preset similarity threshold, wherein the preset target user is any target user.
4. A product recommendation method according to claim 1 or 2, wherein the process of recommending product information of said first target product to each target user comprises:
and sending short links for pointing to the product information of the target products to the intelligent terminals of each target user, and pointing to the product information of the target products when any target user clicks the short links on the intelligent terminals.
5. The product recommendation system is characterized by comprising an acquisition module, a grouping module and a first recommendation module;
the acquisition module is used for: obtaining average interval duration of purchasing a first target product by each target user, wherein the target user refers to: a user purchasing the first target product at least twice;
the grouping module is used for: dividing a plurality of target users into a plurality of groups according to a preset average interval duration range of the average interval duration;
the first recommendation module is used for: and according to any group of corresponding preset average interval duration ranges, sending the product information of the first target product to the intelligent terminal of each target user in the group.
6. The product recommendation system of claim 5, further comprising a second recommendation module for:
according to the historical purchasing records of each target user, calculating the first product purchasing similarity between every two target users;
dividing target users in each group according to a preset product purchase similarity range to which the first product purchase similarity belongs to obtain a plurality of subgroups;
and determining a second target product according to the historical purchase record of any target user, and sending product information of the second target product to the intelligent terminals of each target user in the subgroup to which the target user belongs.
7. The product recommendation system of claim 5 or 6, further comprising a third recommendation module for:
acquiring users to be recommended which have social relations with preset target users, acquiring historical purchase records of each user to be recommended, calculating second product purchase similarity between the preset target users and each user to be recommended, and sending product information of the first target product to an intelligent terminal of the user to be recommended corresponding to the second product purchase similarity which is larger than a preset similarity threshold, wherein the preset target user is any target user.
8. The product recommendation system according to claim 5 or 6, wherein said first recommendation module recommends product information of said first target product to each target user, comprising:
and sending short links for pointing to the product information of the target products to the intelligent terminals of each target user, and pointing to the product information of the target products when any target user clicks the short links on the intelligent terminals.
9. A computer device, characterized in that it comprises a processor coupled to a memory, in which at least one computer program is stored, which is loaded and executed by the processor, in order to make the computer device implement a product recommendation method according to any of the claims 1 to 4.
10. A computer readable storage medium, wherein at least one computer program is stored in the computer readable storage medium, the at least one computer program being loaded and executed by a processor to cause the computer to implement a product recommendation method according to any of claims 1 to 4.
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| CN202311417465.2A CN117635254A (en) | 2023-10-27 | 2023-10-27 | Product recommendation method, system, computer equipment and storage medium |
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| CN202311417465.2A CN117635254A (en) | 2023-10-27 | 2023-10-27 | Product recommendation method, system, computer equipment and storage medium |
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| CN202311417465.2A Pending CN117635254A (en) | 2023-10-27 | 2023-10-27 | Product recommendation method, system, computer equipment and storage medium |
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