CN112581232A - E-commerce commodity recommendation method and system based on images - Google Patents
E-commerce commodity recommendation method and system based on images Download PDFInfo
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- CN112581232A CN112581232A CN202011553080.5A CN202011553080A CN112581232A CN 112581232 A CN112581232 A CN 112581232A CN 202011553080 A CN202011553080 A CN 202011553080A CN 112581232 A CN112581232 A CN 112581232A
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- 238000004422 calculation algorithm Methods 0.000 description 3
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- 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
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- 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]
- G06Q30/0641—Shopping interfaces
- G06Q30/0643—Graphical representation of items or shoppers
Abstract
The invention discloses an image-based E-commerce commodity recommendation method and system, wherein the method comprises the following steps: obtaining an AR model of a commodity; associating the AR model with a commodity attribute of the commodity; receiving a user purchase demand sent by a user terminal; combining the user purchasing demand, the user information and the behavior data to generate a commodity purchasing recommendation scheme; sending the commodity purchasing recommendation scheme and the commodity attributes of the corresponding commodities to the user terminal for visual display; and receiving a user shopping list sent by the user terminal. The method realizes commodity combined recommendation of multiple consumption behaviors of the user and a trial function, the user can simultaneously select multiple products by ordering once, and the E-commerce platform can realize maximization of sales benefits through a more accurate calculation and recommendation scheme.
Description
Technical Field
The invention relates to the technical field of computer software, in particular to an image-based E-commerce commodity recommendation method and system.
Background
With the continuous development of AR and big data technologies, the functions of AR fitting, AR furniture placement, prediction of user preference and the like can be realized at the present stage.
However, in the e-commerce field, products are diversified in types and have different prices and brands, under the influence of many factors, consumers need a long time to screen a shopping scheme of a single or a combination of multiple commodities, or have missing consumption demands, such as cosmetics, snacks, supplies for daily use need to be supplemented after consumption, and the like, and the e-commerce platform and the merchants are lost of potential consumer users.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an image-based E-commerce commodity recommendation method and system to solve the technical problems in the background art.
In a first aspect, the invention provides an image-based e-commerce commodity recommendation method, which comprises the following steps:
obtaining an AR model of a commodity;
associating the AR model with a commodity attribute of the commodity;
receiving a user purchase demand sent by a user terminal;
combining the user purchasing demand, the user information and the behavior data to generate a commodity purchasing recommendation scheme;
sending the commodity purchasing recommendation scheme and the commodity attributes of the corresponding commodities to the user terminal for visual display;
and receiving a user shopping list sent by the user terminal.
Further, the method for generating the AR model of the commodity specifically includes:
acquiring commodity data of a commodity;
and generating an AR model of the commodity according to the commodity data.
Further, the commodity data includes commodity size, commodity shape, commodity color and commodity image feature point information.
Further, the user purchase demand includes keywords and real scene feature information, and the scene feature information is acquired by identifying a real scene picture taken by the user through the user terminal.
Further, the keyword includes at least one of a name, a color, a size, a model, and a brand of an article that the user wants to purchase.
Further, the real scene feature information includes face information, human body image feature point information, and usage scene information.
Further, the user information comprises gender, age and region information, and the behavior data comprises browsing data, collecting data, forwarding data, purchasing data, evaluation data and star evaluation data.
Further, the commodity purchase recommendation scheme comprises a commodity recommendation information general list and a commodity recommendation information sub-list;
the commodity recommendation information general list comprises commodity types;
the commodity recommendation information sub-list comprises a specific commodity recommendation list corresponding to the commodity type.
In a second aspect, the present invention further provides an image-based e-commerce commodity recommendation system, which is suitable for the commodity recommendation method in the first aspect, and the system includes:
the AR model acquisition module is used for acquiring an AR model of the commodity;
the attribute association module is used for associating the AR model with the commodity attributes of the commodities;
the demand receiving module is used for receiving a user purchase demand sent by a user terminal;
the recommendation scheme generation module is used for generating a commodity purchase recommendation scheme by combining the user purchase demand, the user information and the behavior data;
the recommendation scheme sending module is used for sending the commodity purchase recommendation scheme and the commodity attributes of the corresponding commodities to the user terminal for visual display;
and the shopping list receiving module is used for receiving the user shopping list sent by the user terminal.
Further, the E-commerce commodity recommendation system further comprises:
and the AR model generation module is used for acquiring commodity data of the commodity and generating the AR model of the commodity according to the commodity data.
The invention has the beneficial effects that:
the invention realizes the commodity combined recommendation of multiple consumption behaviors of the user and the trial function, the user can simultaneously select multiple products by ordering once, and the E-commerce platform can realize the maximization of sales benefits through more accurate calculation and recommendation schemes.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a flowchart of an image-based e-commerce commodity recommendation method according to an embodiment of the present invention;
fig. 2 is a structural diagram of an image-based e-commerce commodity recommendation system according to a second embodiment of the present invention;
fig. 3 is a structural diagram of an image-based e-commerce commodity recommendation terminal according to a third embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
The first embodiment is as follows:
the embodiment of the invention provides an image-based e-commerce commodity recommendation method, as shown in fig. 1, comprising the following steps:
s1: an AR model of the commodity is obtained.
Specifically, the method for generating the AR model of the commodity specifically includes: acquiring commodity data of a commodity; and generating an AR model of the commodity according to the commodity data. The commodity data comprises commodity size, commodity shape, commodity color matching and commodity image characteristic point information.
The embodiment can acquire the characteristic information of the commodity through the camera and derive the computer virtual model through an algorithm. For some used commodity characteristic information, such as actual use effect of cosmetics like lipstick and foundation, a map or corresponding filter effect can be generated as a model by computer image technology.
S2: and associating the AR model with the commodity attributes of the commodity.
After the AR model of the commodity is obtained, the AR model is associated with commodity attributes of the commodity, wherein the commodity attributes comprise characteristic information of the brand, the model, the price, applicable crowds and the like of the commodity. For the added AR model information, for commodities with different attributes, collision detection, association of human feature points, and different AR model information under different environments, such as shadows generated by influences of light brightness angles in real environments, can be optionally included.
S3: and receiving the user purchase demand sent by the user terminal.
In this embodiment, the user purchase demand includes a keyword and real scene feature information, and the scene feature information is acquired by identifying, by the user terminal, a real scene picture taken by the user. The keyword includes at least one of a name, a color, a size, a model number, and a brand of an article that the user wants to purchase. The real scene feature information comprises face information, human body image feature point information and use scene information.
The user interacts with the server through the user terminal, and the following two interaction modes can be provided:
(1) the user firstly conducts searching action or selects a commodity recommending function, and then enters an information acquisition link of a real scene.
Namely, a user inputs keywords of a commodity, then a real scene picture is shot through a user terminal, and scene characteristic information is obtained after the real scene picture is subjected to characteristic recognition. For example, a user has first searched for a "cup" and then taken a partial or panoramic picture of a table. After the user terminal is processed, the keyword 'water cup' and the scene feature information of the table are sent to the server.
(2) The user firstly carries out the information acquisition link of a real scene and then carries out the function of searching or selecting commodity recommendation.
The method comprises the steps that a user shoots a real scene picture through a user terminal, the user terminal performs feature recognition on the real scene picture to obtain scene feature information, and then the user searches keywords of input commodities.
S4: and generating a commodity purchasing recommendation scheme by combining the user purchasing demand, the user information and the behavior data.
In this embodiment, the user information includes gender, age, and region information, and the behavior data includes browsing data, collection data, forwarding data, purchase data, evaluation data, and star evaluation data.
And according to the face information, the human body characteristic point information and the use scene information acquired in the real scene acquisition, recommending commodities by taking the keywords searched by the user as the basis and combining a computer algorithm to generate a commodity purchase recommendation scheme.
In this embodiment, the commodity purchase recommendation scheme includes a commodity recommendation information general list and a commodity recommendation information sub-list. The commodity recommendation information general list comprises commodity types; the commodity recommendation information sub-list comprises a specific commodity recommendation list corresponding to the commodity type. The commodity recommendation information sub-lists are sorted according to data priority, and a specific commodity recommendation list is calculated in each recommendation option according to comprehensive information such as different prices, different brands and different types.
S5: and sending the commodity purchasing recommendation scheme and the commodity attributes of the corresponding commodities to the user terminal for visual display.
In the embodiment, the user can select different commodities by screening through the user terminal in a real scene, and the AR model is displayed to the user in the real scene by combining the AR augmented reality technology, the collected human face, the collected human body characteristic points, the collected surrounding environment and the like, so that the trial effect is achieved.
S6: and receiving a user shopping list sent by the user terminal.
In this embodiment, the user freely screens out the commodities to be purchased from the commodity purchase recommendation scheme according to personal preferences, trial results and price budgets, generates a comprehensive shopping list, and sends the comprehensive shopping list to the server through the user terminal.
In conclusion, the invention realizes the commodity combined recommendation of multiple consumption behaviors of the user and the trial function, the user can simultaneously select multiple products by ordering once, and the E-commerce platform can realize the maximization of sales benefits through more accurate calculation and recommendation schemes.
Example two:
the embodiment provides an e-commerce commodity recommendation system based on images, which is suitable for the commodity recommendation method in the first embodiment, and as shown in fig. 2, the system includes:
the AR model acquisition module is used for acquiring an AR model of the commodity;
the attribute association module is used for associating the AR model with the commodity attributes of the commodities;
the demand receiving module is used for receiving a user purchase demand sent by a user terminal;
the recommendation scheme generation module is used for generating a commodity purchase recommendation scheme by combining the user purchase demand, the user information and the behavior data;
the recommendation scheme sending module is used for sending the commodity purchase recommendation scheme and the commodity attributes of the corresponding commodities to the user terminal for visual display;
and the shopping list receiving module is used for receiving the user shopping list sent by the user terminal.
Further, the E-commerce commodity recommendation system further comprises:
and the AR model generation module is used for acquiring commodity data of the commodity and generating the AR model of the commodity according to the commodity data.
The system in the embodiment executes the method in the first embodiment, so that the combined commodity recommendation of multiple consumption behaviors of the user is realized, the trial function can be realized, the user can select multiple products at the same time by ordering once, and the E-commerce platform can realize the maximization of sales benefits through a more accurate calculation and recommendation scheme.
Example three:
based on the same inventive concept, the present embodiment provides an image-based e-commerce goods recommendation terminal, which includes, as shown in fig. 3, the system may include: one or more processors 101, one or more input devices 102, one or more output devices 103, and memory 104, the processors 101, input devices 102, output devices 103, and memory 104 being interconnected via a bus 105. The memory 104 is used for storing a computer program, the computer program includes program instructions, and the processor 101 is configured to call the program instructions to execute the article recommendation method according to the first embodiment.
It should be understood that, in the embodiment of the present invention, the Processor 101 may be a Central Processing Unit (CPU), and the Processor may also be other general processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 102 may include a keyboard, microphone, etc., and the output device 103 may include a display (LCD, etc.), speaker, etc.
The memory 104 may include read-only memory and random access memory, and provides instructions and data to the processor 101. A portion of the memory 104 may also include non-volatile random access memory. For example, the memory 104 may also store device type information.
In a specific implementation, the processor 101, the input device 102, and the output device 103 described in the embodiment of the present invention may execute the implementation manner described in the first embodiment of the present invention, and are not described herein again.
Example four:
the present embodiments also provide a readable storage medium storing a computer program comprising program instructions that when executed by a processor implement: the embodiment I discloses an image-based E-commerce commodity recommendation method.
The computer readable storage medium may be an internal storage unit of the background server described in the foregoing embodiment, for example, a hard disk or a memory of the system. The computer readable storage medium may also be an external storage device of the system, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) Card, Flash memory Card (Flash Card), etc. provided on the system. Further, the computer readable storage medium may also include both an internal storage unit and an external storage device of the system. The computer-readable storage medium is used for storing the computer program and other programs and data required by the system. The computer readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. An image-based e-commerce commodity recommendation method is characterized by comprising the following steps:
obtaining an AR model of a commodity;
associating the AR model with a commodity attribute of the commodity;
receiving a user purchase demand sent by a user terminal;
combining the user purchasing demand, the user information and the behavior data to generate a commodity purchasing recommendation scheme;
sending the commodity purchasing recommendation scheme and the commodity attributes of the corresponding commodities to the user terminal for visual display;
and receiving a user shopping list sent by the user terminal.
2. The image-based e-commerce commodity recommendation method according to claim 1, wherein: the method for generating the AR model of the commodity specifically includes:
acquiring commodity data of a commodity;
and generating an AR model of the commodity according to the commodity data.
3. The image-based e-commerce commodity recommendation method according to claim 2, wherein: the commodity data includes commodity size, commodity shape, commodity color matching and commodity image feature point information.
4. The image-based e-commerce commodity recommendation method according to claim 1, wherein: the user purchase demand comprises keywords and real scene characteristic information, and the scene characteristic information is obtained by identifying a real scene picture shot by the user through the user terminal.
5. The image-based e-commerce commodity recommendation method according to claim 4, wherein: the keyword includes at least one of a name, a color, a size, a model number, and a brand of an article that the user wants to purchase.
6. The image-based e-commerce commodity recommendation method according to claim 4, wherein: the real scene feature information comprises face information, human body image feature point information and use scene information.
7. The image-based e-commerce commodity recommendation method according to claim 1, wherein: the user information comprises gender, age and region information, and the behavior data comprises browsing data, collecting data, forwarding data, purchasing data, evaluation data and star evaluation data.
8. The image-based e-commerce commodity recommendation method according to claim 1, wherein: the commodity purchase recommendation scheme comprises a commodity recommendation information general list and a commodity recommendation information sub-list;
the commodity recommendation information general list comprises commodity types;
the commodity recommendation information sub-list comprises a specific commodity recommendation list corresponding to the commodity type.
9. An image-based e-commerce commodity recommendation system suitable for the commodity recommendation method of any one of claims 1 to 8, wherein the system comprises:
the AR model acquisition module is used for acquiring an AR model of the commodity;
the attribute association module is used for associating the AR model with the commodity attributes of the commodities;
the demand receiving module is used for receiving a user purchase demand sent by a user terminal;
the recommendation scheme generation module is used for generating a commodity purchase recommendation scheme by combining the user purchase demand, the user information and the behavior data;
the recommendation scheme sending module is used for sending the commodity purchase recommendation scheme and the commodity attributes of the corresponding commodities to the user terminal for visual display;
and the shopping list receiving module is used for receiving the user shopping list sent by the user terminal.
10. The image-based e-commerce merchandise recommendation system of claim 9, further comprising:
and the AR model generation module is used for acquiring commodity data of the commodity and generating the AR model of the commodity according to the commodity data.
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