CN111784428A - Information pushing method and device, electronic commerce system and storage medium - Google Patents
Information pushing method and device, electronic commerce system and storage medium Download PDFInfo
- Publication number
- CN111784428A CN111784428A CN201910462859.7A CN201910462859A CN111784428A CN 111784428 A CN111784428 A CN 111784428A CN 201910462859 A CN201910462859 A CN 201910462859A CN 111784428 A CN111784428 A CN 111784428A
- Authority
- CN
- China
- Prior art keywords
- information
- recommended
- commodity
- store
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
-
- 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
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
Abstract
The present disclosure provides an information push method, an information push device, an electronic commerce system and a storage medium, which relate to the technical field of electronic commerce, wherein the method comprises the following steps: the method comprises the steps of establishing a first incidence relation between first electronic equipment identification information of a user and first recommended commodities according to user behavior data, establishing a second incidence relation between second recommended commodities corresponding to shops and second electronic equipment information based on the first incidence relation, shop commodity information of the shops and second electronic equipment identification information collected by equipment identifications corresponding to the shops, generating recommended information according to the second incidence relation, and sending the recommended information to corresponding users. The method, the device, the e-commerce system and the storage medium can combine online data and offline data to push shop commodity recommendation information to users, can reduce the popularization cost of offline shops, narrow the scope of popularization targets, and increase the popularization accuracy, so that the return rate is improved, and the use experience of the users is improved.
Description
Technical Field
The present disclosure relates to the field of electronic commerce technologies, and in particular, to an information pushing method and apparatus, an electronic commerce system, and a storage medium.
Background
With the continuous expansion of the electronic commerce scale, the variety and the number of commodities rapidly increase, and a user needs to spend a lot of time to find out a needed commodity from a large quantity of commodities. In order to improve the shopping experience of the user, the shopping website provides personalized decision support and commodity information service for the user through a recommendation system, and offline popularization is performed on the user. The existing offline popularization mode mainly comprises the following schemes: 1. advertising: the advertisement is popularized in the form of posters and appears in public places, vehicles and other places; 2. activity propaganda: the offline event is popularized by holding some events; 3. the business card is popularized in a mode of issuing the business card through a line; 4. the promotion of the leaflet is realized by asking people to promote the leaflet; 5. gift popularization, namely printing brand information on a gift, and then sending the gift to a client for popularization; 6. the two-dimensional code is popularized, and the two-dimensional code is popularized in a physical store or a business card two-dimensional code. The existing offline promotion mode has high promotion cost, and a great amount of manpower and financial resources are required to be invested for advertisement propaganda, activity propaganda and the like; the existing offline popularization mode has no pertinence in target, does not know the favor of popularization objects, and is usually more in investment, less in output and low in return rate.
Disclosure of Invention
In view of the above, an object of the present disclosure is to provide an information pushing method, an information pushing apparatus, an electronic commerce system, and a storage medium.
According to an aspect of the present disclosure, there is provided an information pushing method, including: obtaining a first recommended commodity corresponding to the user according to the user behavior data; acquiring first electronic equipment identification information of a user, and establishing a first association relation between the first electronic equipment identification information and the first recommended commodity; receiving second electronic equipment identification information and store commodity information which are acquired by equipment identification acquisition equipment corresponding to a store; establishing a second association relation between a second recommended commodity corresponding to a shop and the second electronic equipment information based on the first association relation, the shop commodity information and the second electronic equipment identification information; and generating recommendation information according to the second incidence relation and sending the recommendation information to the corresponding user.
Optionally, the establishing a second association relationship between a second recommended item corresponding to a store and the second electronic device information based on the first association relationship, the store item information, and the second electronic device identification information includes: obtaining all first recommended commodity information based on the first incidence relation; determining the second recommended commodity corresponding to the shop according to all the first recommended commodities and the shop commodity information; obtaining all first electronic equipment identifications corresponding to the second recommended commodities based on the first incidence relation; determining the second electronic equipment identification set corresponding to the second recommended commodity according to all the first electronic identifications and the second electronic equipment identification information; and establishing a second incidence relation between the second recommended commodity and the second electronic equipment identification set.
Optionally, the determining the second recommended item corresponding to the store from the all first recommended items and the store item information includes: calculating the similarity between the store commodities in the store and the first recommended commodity based on the commodity identification information, and determining the second recommended commodity based on the similarity; or if the SKU of the store commodity in the store is the same as the SKU of the first recommended commodity, determining the store commodity as the second recommended commodity.
Optionally, the generating recommendation information according to the second association relationship and sending the recommendation information to the corresponding user includes: determining a second recommended item corresponding to the store based on a recommendation policy; obtaining the second set of electronic devices corresponding to the second recommended commodity based on the second incidence relation; and generating recommendation information corresponding to the second recommended commodity, and sending the recommendation information to at least one electronic device in the second electronic device set.
Optionally, the establishing a second association relationship between a second recommended item corresponding to a store and the second electronic device information based on the first association relationship, the store item information, and the second electronic device identification information includes: acquiring the identification information of the second electronic equipment received in real time, and acquiring a first recommended commodity set corresponding to the identification information of the second electronic equipment based on the first association relation; determining a second recommended commodity set corresponding to the shop according to the first recommended commodity set and the shop commodity information; and establishing a second incidence relation between the second electronic equipment identification information and the second recommended commodity set.
Optionally, the determining a second recommended item set corresponding to the store according to the first recommended item set and the store item information includes: calculating the similarity between the shop goods in the shop and the first recommended goods in the first recommended goods set based on the goods identification information, and determining the second recommended goods based on the similarity; or if the SKU of the store commodity in the store is the same as the SKU of the first recommended commodity in the first recommended commodity set, determining the store commodity as the second recommended commodity.
Optionally, the generating recommendation information according to the second association relationship and sending the recommendation information to the corresponding user includes: generating recommendation information corresponding to at least one second recommended commodity in the second recommended commodity set; and sending the recommendation information to the electronic equipment corresponding to the identification information of the second electronic equipment received in real time.
Optionally, the obtaining of the first recommended item corresponding to the user according to the user behavior data includes: obtaining user behavior data and commodity attribute characteristics corresponding to the user behavior data; the user behavior data comprises at least one of search behavior, collection behavior, browsing behavior, shopping behavior and comment behavior data; the commodity attribute features comprise at least one of commodity classification, brand, price and click rate; extracting label information and calculating the weight of a label based on the user behavior data and the commodity attribute characteristics so as to construct a user portrait; and obtaining a first recommended commodity corresponding to the user based on the user portrait.
Optionally, the device identifier acquiring device includes: a WIFI device; the first electronic device and the second electronic device each include: a mobile phone; the first electronic device identification information and the second electronic device identification information both include at least one of a MAC address, a mobile phone number, and a mobile phone identification code.
According to another aspect of the present disclosure, there is provided an information pushing apparatus including: the first associated information establishing module is used for obtaining a first recommended commodity corresponding to the user according to the user behavior data; acquiring first electronic equipment identification information of a user, and establishing a first association relation between the first electronic equipment identification information and the first recommended commodity; the shop information receiving module is used for receiving second electronic equipment identification information and shop commodity information which are acquired by equipment identification acquisition equipment corresponding to shops; a second association information establishing module, configured to establish a second association relationship between a second recommended product corresponding to a store and the second electronic device information based on the first association relationship, the store product information, and the second electronic device identification information; and the recommendation information processing module is used for generating recommendation information according to the second incidence relation and sending the recommendation information to the corresponding user.
According to another aspect of the present disclosure, there is provided an information pushing apparatus including: a memory; and a processor coupled to the memory, the processor configured to perform the method as described above based on instructions stored in the memory.
According to still another aspect of the present disclosure, there is provided an electronic commerce system including: the information pushing device as claimed in claim.
According to yet another aspect of the present disclosure, a computer-readable storage medium is provided, which stores computer instructions for execution by a processor to perform the method as described above.
The information pushing method, the information pushing device, the electronic commerce system and the storage medium establish a first incidence relation between first electronic equipment identification information of a user and first recommended commodities according to user behavior data, establish a second incidence relation between second recommended commodities corresponding to shops and second electronic equipment information based on the first incidence relation, shop commodity information of the shops and second electronic equipment identification information collected by equipment identifications corresponding to the shops, generate recommended information according to the second incidence relation and send the recommended information to the corresponding user; the online data and the offline data can be combined, the shop commodity recommendation information is pushed to the user, the popularization cost of the offline shop can be reduced, and the popularization accuracy is improved.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive exercise.
FIG. 1 is a schematic flow chart diagram illustrating one embodiment of an information push method according to the present disclosure;
fig. 2 is a schematic flow chart illustrating establishment of a second association relationship in an embodiment of an information push method according to the present disclosure;
FIG. 3 is a schematic flow chart of generating and sending recommendation information in an embodiment of an information pushing method according to the present disclosure;
fig. 4 is a schematic flow chart illustrating establishment of a second association relationship in another embodiment of an information push method according to the present disclosure;
fig. 5 is a schematic flow chart of generating and sending recommendation information in another embodiment of an information pushing method according to the present disclosure;
FIG. 6 is a schematic diagram of collecting store data in one embodiment of an information push method according to the present disclosure;
FIG. 7 is a block diagram view of one embodiment of an information pushing device according to the present disclosure;
FIG. 8 is a block diagram illustrating a second correlation information creation module in an embodiment of an information pushing device according to the present disclosure;
FIG. 9 is a block diagram illustrating a first associated information creation module in an embodiment of an information pushing device according to the present disclosure;
fig. 10 is a schematic diagram of another embodiment of an information pushing device according to the present disclosure.
Detailed Description
The present disclosure now will be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the disclosure are shown. The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure. The technical solution of the present disclosure is described in various aspects below with reference to various figures and embodiments.
The terms "first", "second", and the like are used hereinafter only for descriptive distinction and have no other special meaning.
Fig. 1 is a schematic flow chart diagram of an embodiment of an information pushing method according to the present disclosure, as shown in fig. 1:
And 103, receiving second electronic equipment identification information and store commodity information acquired by equipment identification acquisition equipment corresponding to the store.
The equipment sign collection equipment corresponding to the shop can be various WIFI equipment, for example, wireless router, radar equipment and the like, and can be arranged in the shop. The electronic device may be a mobile phone or the like, the first electronic device identification information and the second electronic device identification information may be an MAC address, a mobile phone number, a mobile phone identification code or the like, and the mobile phone identification code may be a mobile phone PIN, IMSI or the like.
The store merchandise information may be SKU (Stock Keeping Unit) information of merchandise in the store. The user can browse, access, shop and the like through the mobile phone APP, the mobile phone APP obtains the first electronic equipment identification information of the user, and the first electronic equipment identification information of the user is returned to the server side.
And 104, establishing a second association relation between a second recommended commodity corresponding to the shop and the second electronic equipment information based on the first association relation, the shop commodity information and the second electronic equipment identification information.
And 105, generating recommendation information according to the second association relation and sending the recommendation information to the corresponding user. The short message interface can be called to send recommendation information in a short message pushing mode for the user, or the APP pushing function is used for pushing the recommendation information for the user, and the purpose of accurate shop popularization is achieved.
In one embodiment, the second association between the second recommended item corresponding to the store and the second electronic device information may be established in various ways. Fig. 2 is a schematic flow chart of establishing a second association relationship in an embodiment of an information push method according to the present disclosure, as shown in fig. 2:
In step 202, a second recommended item corresponding to the store is determined based on all the first recommended items and the store item information.
And step 203, obtaining all first electronic equipment identifications corresponding to the second recommended goods based on the first association relation.
And 204, determining a second electronic equipment identification set corresponding to the second recommended commodity according to all the first electronic identifications and the second electronic equipment identification information.
There may be various ways to determine the second recommended item corresponding to the store from all of the first recommended items and the store item information. For example, the similarity between the store goods in the store and the first recommended goods is calculated based on the goods identification information, and the second recommended goods are determined based on the similarity; alternatively, if the SKU of the store item within the store is the same as the SKU of the first recommended item, then the store item is determined to be the second recommended item.
There may be various ways to calculate the similarity between the store item in the store and the first recommended item based on the item identification information. For example, the article identification information may include an article name, a model number, a manufacturer, and the like. Presetting a machine learning model, generating training samples, wherein the training samples comprise commodity identification information of a plurality of commodities and similarity information among the commodities, and training the machine learning model by using the training samples.
Inputting the commodity identification information of the store commodity and the commodity identification information of the first recommended commodity into a trained machine learning model, and outputting the similarity value of the store commodity and the first recommended commodity by the machine learning model. And if the similarity value of the shop commodity and the first recommended commodity is larger than a preset similarity threshold value, determining that the shop commodity is the second recommended commodity. The machine learning model may be a neural network model or the like.
Fig. 3 is a schematic flowchart of a process of generating and sending recommendation information in an embodiment of an information pushing method according to the present disclosure, as shown in fig. 3:
in step 301, a second recommended item corresponding to the store is determined based on the recommendation policy.
The recommendation strategy may be various. For example, when a product in a store is discounted, the discounted product is determined as a second recommended product; alternatively, when the store performs a plurality of activities, the item participating in the activity is determined as the second recommended item.
In one embodiment, a big data platform in the e-commerce system obtains user behavior data, may obtain a first recommended commodity corresponding to a user according to the user behavior data, obtain first MAC information of the user, and establish a first association relationship between the first MAC information and the first recommended commodity. The big data platform may use non-relational databases (Hbase, Redis, etc.) to store relevant data.
And the radar equipment in the shop scans the second MAC information of the passing users nearby in real time, reports the second MAC information to a message queue, and then forwards the second MAC information to the big data platform for storage. An offline aggregation of the data throughout the day is performed. All the first recommended-item information is obtained based on the first association, for example, all the first recommended-item information is SKU information of the first recommended item A, B, C, D, and the store item in the store a is A, E, F, G.
And the SKU of the shop commodity A is the same as that of the first recommended commodity A, and the shop commodity A is determined as the second recommended commodity. The product identification information of the first recommended product B, C, D and the store product E, F, G is input into the trained machine learning model, the machine learning model outputs a similarity value between the first recommended product and the store product, and the store product E, F corresponding to the similarity value larger than a preset threshold is determined as the second recommended product.
All the first MAC information corresponding to the second recommended article a, which is the MAC1, 2, 3, 4 information, is obtained based on the first association relationship. The radar device in the store a scans the second MAC information of the passing users nearby as MAC1, 2, 3 information. And determining a second electronic device identification set corresponding to the second recommended commodity A according to the MAC1, 2, 3 and 4 information and the MAC1, 2 and 3 information, wherein the second electronic device identification set comprises the MAC1, 2 and 3 information, and establishing a second association relationship between the second recommended commodity A and the second electronic device identification set. Based on the same method, a second association relationship between the second recommended item E, F and the second set of electronic device identifications may be established, respectively.
When the shop commodity A in the shop A has discount offers, a second electronic device identification set corresponding to the SKU of the second recommended commodity A is inquired based on the second association relation, recommendation information corresponding to the second recommended commodity A is generated, and the recommendation information is sent to at least one electronic device corresponding to the MAC1, 2 and 3 information in the second electronic device set.
Fig. 4 is a schematic flowchart of establishing a second association relationship in another embodiment of the information pushing method according to the present disclosure, as shown in fig. 4:
And calculating the similarity between the shop goods in the shop and the first recommended goods in the first recommended goods set based on the goods identification information, and determining the second recommended goods based on the similarity. For example, the item identification information of the store item and the item identification information of the first recommended item in the first recommended item set are input to a trained machine learning model, and the machine learning model outputs a similarity value between the store item and the first recommended item in the first recommended item set.
Determining the shop commodity corresponding to the similarity value larger than the preset threshold value as a second recommended commodity; alternatively, if the SKU of the store item in the store is the same as the SKU of the first recommended item in the first set of recommended items, then the store item is determined to be the second recommended item.
Fig. 5 is a schematic flowchart of a process of generating and sending recommendation information in another embodiment of an information pushing method according to the present disclosure, as shown in fig. 5:
In one embodiment, the radar device in the store B scans the second MAC information of the passing user W in real time, and reports the second MAC information to the message queue, and the e-commerce system obtains the second MAC information in real time through a real-time computing engine (spark streaming, Storm, flash, etc.). As shown in fig. 6, the store product information is sku information of all products in the store, and is obtained by docking a product stocking and selling system in the store. Second MAC information passing through the vicinity of the shop can be obtained by scanning hardware equipment (with a WIFI probe function); and reporting the obtained mobile phone MAC information (second MAC information) of the user passing by near the shop to an IOT platform through an interface, and then transferring the mobile phone MAC information to a big data platform or carrying out real-time analysis.
Obtaining a first recommended commodity set corresponding to the second MAC information based on the first incidence relation; for example, the first set of recommended merchandise includes the first recommended merchandise A, B, C, D, and the store merchandise information within store A is the SKU of the store merchandise A, E, F, G. Inputting the commodity identification information of the first recommended commodity A, B, C, D and the store commodity A, E, F, G into a trained machine learning model, outputting a similarity value between the first recommended commodity and the store commodity by the machine learning model, determining the store commodity A, E with the similarity value larger than a preset threshold value as a second recommended commodity, and establishing a second association relationship between the second MAC information of the user W and a second recommended commodity set, wherein the second recommended commodity set comprises the store commodity A, E.
And generating recommendation information corresponding to at least one second recommended commodity A, E in the second recommended commodity set, sending the recommendation information to the electronic equipment corresponding to the second MAC information of the user W received in real time, informing the user that the store through which the user passes has a commodity of interest, and enabling store-in experience.
In one embodiment, a plurality of methods may be employed to obtain the first recommended goods corresponding to the user according to the user behavior data. For example, user behavior data and commodity attribute characteristics corresponding to the user behavior data are obtained, the user behavior data comprises at least one of search behavior, collection behavior, browsing behavior, shopping behavior and comment behavior data, and the commodity attribute characteristics comprise at least one of classification, brand, price and click rate of commodities. Extracting label information and calculating a weight of a label based on the user behavior data and the commodity attribute characteristics to construct a user portrait, and obtaining a first recommended commodity corresponding to the user based on the user portrait.
Extracting behavior data of a user from various log data to obtain attribute characteristics of commodities corresponding to the user behaviors, wherein the behavior data of the user comprises data such as search behaviors, collection behaviors, browsing behaviors, shopping behaviors and comment behaviors; the commodity attribute features include the classification of the commodity, brand, price, and click-through rate. Extracting label information based on the behavior data and the commodity attribute characteristics of the user to construct a user portrait, wherein the user portrait comprises a plurality of labels such as a user interest preference label, a position label, a consumption capacity label, an activity label and a loyalty label.
For example, the behavior data of the user and the commodity attribute feature information are input into the trained machine learning model, and the weight values of the user interest preference tag, the position tag, the consumption capability tag, the activity tag, the loyalty tag and the like can be output, so that the user representation can be generated according to the weight values of the user interest preference tag, the position tag, the consumption capability tag, the activity tag, the loyalty tag and the like. The existing various recommendation engines or recommendation systems can be adopted to obtain the first recommended commodity corresponding to the user based on the user portrait, so that the combination of online data and offline data is realized, and the commodity or the commodity which the user may be interested in is obtained by utilizing the existing recommendation engines.
The recommendation system can be divided into a data layer, a recall layer, a ranking layer and the like. The data layer is used for data generation and data storage, and is mainly used for cleaning original logs by various data processing tools, processing the original logs into formatted data, and landing the formatted data in different types of storage systems for use by downstream algorithms and models.
The recall layer is mainly used for generating recommended candidate sets by utilizing various trigger strategies from the perspectives of historical behaviors, real-time behaviors and the like of users, fusing the candidate sets generated by different strategies and algorithms and filtering according to product rules, and generally performing coarse sorting on the recall layer, performing primary coarse sorting on the fused candidate sets and filtering out the candidate sets with lower coarse sorting scores. The sorting layer is mainly used for carrying out fine sorting on the candidate set screened out by the recall layer by utilizing a machine learning model.
In one embodiment, as shown in fig. 7, the present disclosure provides an information pushing apparatus 80, including: a first associated information establishing module 81, a shop information receiving module 82, a second associated information establishing module 83, and a recommended information processing module 84.
The first associated information establishing module 81 obtains a first recommended commodity corresponding to the user according to the user behavior data. The first association information establishing module 81 obtains first electronic device identification information of the user, and establishes a first association relationship between the first electronic device identification information and the first recommended product. The store information receiving module 82 receives the second electronic device identification information and the store commodity information collected by the device identification collecting device corresponding to the store. The second association information establishing module 83 establishes a second association relationship between the second recommended commodity corresponding to the store and the second electronic device information based on the first association relationship, the store commodity information, and the second electronic device identification information. And the recommendation information processing module 84 generates recommendation information according to the second association relationship and sends the recommendation information to the corresponding user.
In one embodiment, as shown in fig. 8, the second association information establishing module 83 includes: a first article obtaining unit 831, a first device obtaining unit 832, and a first relationship establishing unit 833. The first article obtaining unit 831 obtains all the first recommended article information based on the first association relationship, and determines the second recommended article corresponding to the store from all the first recommended articles and the store article information.
The first device obtaining unit 832 obtains all the first electronic device identifiers corresponding to the second recommended product based on the first association relationship, and determines a second electronic device identifier set corresponding to the second recommended product according to all the first electronic device identifiers and the second electronic device identifier information. The first relationship establishing unit 833 establishes a second association relationship between the second recommended product and the second electronic device identification set.
The first product obtaining unit 831 calculates the similarity between the store product in the store and the first recommended product based on the product identification information, and determines a second recommended product based on the similarity; alternatively, if the SKU of the store item in the store is the same as the SKU of the first recommended item, the first item obtaining unit 831 determines this store item as the second recommended item.
The recommendation information processing module 84 determines a second recommended item corresponding to the store based on the recommendation policy, and obtains a second set of electronic devices corresponding to the second recommended item based on the second association relationship. The recommendation information processing module 84 generates recommendation information corresponding to the second recommended item, and sends the recommendation information to at least one electronic device in the second set of electronic devices.
In one embodiment, as shown in fig. 8, the second association information establishing module 83 includes: a second device obtaining unit 834, a second goods obtaining unit 835 and a second relationship establishing unit 836. The second device obtaining unit 834 obtains the second electronic device identification information received in real time. The second item obtaining unit 835 obtains a first recommended item set corresponding to the second electronic device identification information based on the first association relationship. The second relationship establishing unit 836 determines a second recommended product set corresponding to the store according to the first recommended product set and the store product information, and establishes a second association relationship between the second electronic device identification information and the second recommended product set.
The second product obtaining unit 835 calculates the similarity between the store products in the store and the first recommended product in the first recommended product set based on the product identification information, and determines a second recommended product based on the similarity; alternatively, if the SKU of the store item within the store is the same as the SKU of the first recommended item in the first set of recommended items, the second item obtaining unit 835 determines the store item as the second recommended item.
The recommendation information processing module 84 generates recommendation information corresponding to at least one second recommended item in the second set of recommended items, and sends the recommendation information to the electronic device corresponding to the second electronic device identification information received in real time.
In one embodiment, as shown in fig. 9, the first association information establishing module 81 includes: the third article obtainment unit 811. The third commodity obtaining unit 811 obtains user behavior data and commodity attribute features corresponding to the user behavior data, where the user behavior data includes at least one of search behavior, collection behavior, browsing behavior, shopping behavior, and comment behavior data; the commodity attribute features include at least one of a category, a brand, a price, and a click-through rate of the commodity. The third commodity obtaining unit 811 extracts tag information and calculates a weight of a tag based on the user behavior data and the commodity attribute features to construct a user portrait, and obtains a first recommended commodity corresponding to the user based on the user portrait.
Fig. 10 is a block diagram of another embodiment of an information pushing device according to the present disclosure. As shown in fig. 10, the apparatus may include a memory 1001, a processor 1002, a communication interface 1003, and a bus 1004. The memory 1001 is used for storing instructions, the processor 1002 is coupled to the memory 1001, and the processor 1002 is configured to execute the information pushing method based on the instructions stored in the memory 1001.
The memory 1001 may be a high-speed RAM memory, a non-volatile memory (non-volatile memory), or the like, and the memory 1001 may be a memory array. The storage 1001 may also be partitioned and the blocks may be combined into virtual volumes according to certain rules. The processor 1002 may be a central processing unit CPU, or an application specific Integrated circuit asic (application specific Integrated circuit), or one or more Integrated circuits configured to implement the information pushing methods of the present disclosure.
In one embodiment, the present disclosure provides an electronic commerce system, including the information pushing apparatus in any one of the above embodiments.
In one embodiment, the present disclosure provides a computer-readable storage medium having stored thereon computer instructions for execution by a processor to perform a method as in any of the above embodiments.
The information pushing method, the information pushing device, the electronic commerce system and the storage medium in the embodiments establish a first association relationship between first electronic equipment identification information of a user and a first recommended commodity according to user behavior data, establish a second association relationship between a second recommended commodity corresponding to a store and second electronic equipment information based on the first association relationship, store commodity information of the store and second electronic equipment identification information acquired by an equipment identification corresponding to the store, generate recommended information according to the second association relationship, and send the recommended information to a corresponding user; the online data and the offline data can be combined, the shop commodity recommendation information can be pushed to the user, the promotion cost of the offline shop can be reduced, the manpower input to offline promotion is saved, the scope of promotion targets can be reduced, the promotion accuracy is improved, the return rate is improved, and the use experience of the user is improved.
The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
The description of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (13)
1. An information push method, comprising:
obtaining a first recommended commodity corresponding to the user according to the user behavior data;
acquiring first electronic equipment identification information of a user, and establishing a first association relation between the first electronic equipment identification information and the first recommended commodity;
receiving second electronic equipment identification information and store commodity information which are acquired by equipment identification acquisition equipment corresponding to a store;
establishing a second association relation between a second recommended commodity corresponding to a shop and the second electronic equipment information based on the first association relation, the shop commodity information and the second electronic equipment identification information;
and generating recommendation information according to the second incidence relation and sending the recommendation information to the corresponding user.
2. The method of claim 1, wherein the establishing a second association between a second recommended item corresponding to a store and the second electronic device information based on the first association, the store item information, and the second electronic device identification information comprises:
obtaining all first recommended commodity information based on the first incidence relation;
determining the second recommended commodity corresponding to the shop according to all the first recommended commodities and the shop commodity information;
obtaining all first electronic equipment identifications corresponding to the second recommended commodities based on the first incidence relation;
determining the second electronic equipment identification set corresponding to the second recommended commodity according to all the first electronic identifications and the second electronic equipment identification information;
and establishing a second incidence relation between the second recommended commodity and the second electronic equipment identification set.
3. The method of claim 2, the determining the second recommended item corresponding to the store from the all first recommended items and the store item information comprising:
calculating the similarity between the store commodities in the store and the first recommended commodity based on the commodity identification information, and determining the second recommended commodity based on the similarity;
or if the SKU of the store commodity in the store is the same as the SKU of the first recommended commodity, determining the store commodity as the second recommended commodity.
4. The method of claim 2, wherein the generating recommendation information according to the second association and sending the recommendation information to the corresponding user comprises:
determining a second recommended item corresponding to the store based on a recommendation policy;
obtaining the second set of electronic devices corresponding to the second recommended commodity based on the second incidence relation;
and generating recommendation information corresponding to the second recommended commodity, and sending the recommendation information to at least one electronic device in the second electronic device set.
5. The method of claim 1, wherein the establishing a second association between a second recommended item corresponding to a store and the second electronic device information based on the first association, the store item information, and the second electronic device identification information comprises:
acquiring the identification information of the second electronic equipment received in real time, and acquiring a first recommended commodity set corresponding to the identification information of the second electronic equipment based on the first association relation;
determining a second recommended commodity set corresponding to the shop according to the first recommended commodity set and the shop commodity information;
and establishing a second incidence relation between the second electronic equipment identification information and the second recommended commodity set.
6. The method of claim 5, the determining a second set of recommended merchandise corresponding to the store from the first set of recommended merchandise and the store merchandise information comprising:
calculating the similarity between the shop goods in the shop and the first recommended goods in the first recommended goods set based on the goods identification information, and determining the second recommended goods based on the similarity;
or if the SKU of the store commodity in the store is the same as the SKU of the first recommended commodity in the first recommended commodity set, determining the store commodity as the second recommended commodity.
7. The method of claim 5, wherein the generating recommendation information according to the second association and sending the recommendation information to the corresponding user comprises:
generating recommendation information corresponding to at least one second recommended commodity in the second recommended commodity set;
and sending the recommendation information to the electronic equipment corresponding to the identification information of the second electronic equipment received in real time.
8. The method of claim 1, wherein the obtaining of the first recommended good corresponding to the user according to the user behavior data comprises:
obtaining user behavior data and commodity attribute characteristics corresponding to the user behavior data; the user behavior data comprises at least one of search behavior, collection behavior, browsing behavior, shopping behavior and comment behavior data; the commodity attribute features comprise at least one of commodity classification, brand, price and click rate;
extracting label information and calculating the weight of a label based on the user behavior data and the commodity attribute characteristics so as to construct a user portrait;
and obtaining a first recommended commodity corresponding to the user based on the user portrait.
9. The method of any one of claims 1 to 8,
the device identification acquisition device includes: a WIFI device;
the first electronic device and the second electronic device each include: a mobile phone; the first electronic device identification information and the second electronic device identification information both include at least one of a MAC address, a mobile phone number, and a mobile phone identification code.
10. An information pushing apparatus comprising:
the first associated information establishing module is used for obtaining a first recommended commodity corresponding to the user according to the user behavior data; acquiring first electronic equipment identification information of a user, and establishing a first association relation between the first electronic equipment identification information and the first recommended commodity;
the shop information receiving module is used for receiving second electronic equipment identification information and shop commodity information which are acquired by equipment identification acquisition equipment corresponding to shops;
a second association information establishing module, configured to establish a second association relationship between a second recommended product corresponding to a store and the second electronic device information based on the first association relationship, the store product information, and the second electronic device identification information;
and the recommendation information processing module is used for generating recommendation information according to the second incidence relation and sending the recommendation information to the corresponding user.
11. An information pushing apparatus comprising:
a memory; and a processor coupled to the memory, the processor configured to perform the method of any of claims 1-9 based on instructions stored in the memory.
12. An electronic commerce system, comprising:
the information pushing apparatus according to any one of claims 10 to 11.
13. A computer-readable storage medium having stored thereon computer instructions for execution by a processor to perform the method of any one of claims 1 to 9.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910462859.7A CN111784428A (en) | 2019-05-30 | 2019-05-30 | Information pushing method and device, electronic commerce system and storage medium |
| PCT/CN2020/080877 WO2020238363A1 (en) | 2019-05-30 | 2020-03-24 | Method and apparatus for pushing information, electronic commerce system and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910462859.7A CN111784428A (en) | 2019-05-30 | 2019-05-30 | Information pushing method and device, electronic commerce system and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111784428A true CN111784428A (en) | 2020-10-16 |
Family
ID=72755247
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910462859.7A Pending CN111784428A (en) | 2019-05-30 | 2019-05-30 | Information pushing method and device, electronic commerce system and storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN111784428A (en) |
| WO (1) | WO2020238363A1 (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112883263A (en) * | 2021-02-09 | 2021-06-01 | 浙江口碑网络技术有限公司 | Information recommendation method and device and electronic equipment |
| CN113129053A (en) * | 2021-03-29 | 2021-07-16 | 北京沃东天骏信息技术有限公司 | Information recommendation model training method, information recommendation method and storage medium |
| CN113256388A (en) * | 2021-06-15 | 2021-08-13 | 新石器慧通（北京）科技有限公司 | Method for predicting commodities to be sold on unmanned vehicle |
| CN115687786A (en) * | 2022-11-18 | 2023-02-03 | 广东大比特网络科技有限公司 | Personalized recommendation method, system and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005122369A (en) * | 2003-10-15 | 2005-05-12 | Kddi Corp | Item recommendation degree presenting apparatus for recommender system |
| CN107527268A (en) * | 2017-08-23 | 2017-12-29 | 苏州浩哥文化传播有限公司 | It is a kind of to be used for the do shopping intelligent community service kiosk to navigate and its method of work |
| CN108123913A (en) * | 2016-11-29 | 2018-06-05 | 中国电信股份有限公司 | Based on the user behavior data acquisition method of scene, device and system under line |
| CN108648059A (en) * | 2018-05-16 | 2018-10-12 | 口口相传(北京)网络技术有限公司 | Order recommends method and device |
| CN109189904A (en) * | 2018-08-10 | 2019-01-11 | 上海中彦信息科技股份有限公司 | Individuation search method and system |
-
2019
- 2019-05-30 CN CN201910462859.7A patent/CN111784428A/en active Pending
-
2020
- 2020-03-24 WO PCT/CN2020/080877 patent/WO2020238363A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005122369A (en) * | 2003-10-15 | 2005-05-12 | Kddi Corp | Item recommendation degree presenting apparatus for recommender system |
| CN108123913A (en) * | 2016-11-29 | 2018-06-05 | 中国电信股份有限公司 | Based on the user behavior data acquisition method of scene, device and system under line |
| CN107527268A (en) * | 2017-08-23 | 2017-12-29 | 苏州浩哥文化传播有限公司 | It is a kind of to be used for the do shopping intelligent community service kiosk to navigate and its method of work |
| CN108648059A (en) * | 2018-05-16 | 2018-10-12 | 口口相传(北京)网络技术有限公司 | Order recommends method and device |
| CN109189904A (en) * | 2018-08-10 | 2019-01-11 | 上海中彦信息科技股份有限公司 | Individuation search method and system |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112883263A (en) * | 2021-02-09 | 2021-06-01 | 浙江口碑网络技术有限公司 | Information recommendation method and device and electronic equipment |
| CN113129053A (en) * | 2021-03-29 | 2021-07-16 | 北京沃东天骏信息技术有限公司 | Information recommendation model training method, information recommendation method and storage medium |
| CN113256388A (en) * | 2021-06-15 | 2021-08-13 | 新石器慧通（北京）科技有限公司 | Method for predicting commodities to be sold on unmanned vehicle |
| CN113256388B (en) * | 2021-06-15 | 2022-05-03 | 新石器慧通（北京）科技有限公司 | Method for predicting commodities to be sold on unmanned vehicle |
| CN115687786A (en) * | 2022-11-18 | 2023-02-03 | 广东大比特网络科技有限公司 | Personalized recommendation method, system and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020238363A1 (en) | 2020-12-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110222272B (en) | Potential customer mining and recommending method | |
| CN111784428A (en) | Information pushing method and device, electronic commerce system and storage medium | |
| CN103841122B (en) | Target object information recommends method, server and client | |
| TWI512653B (en) | Information providing method and apparatus, method and apparatus for determining the degree of comprehensive relevance | |
| CN108022152A (en) | The automatic commending system of user's commodity and recommendation method based on image recognition | |
| CN106911801B (en) | method for associating user information and information pushing system | |
| CN104835066A (en) | Embarking channel selection method and system | |
| CN104978346A (en) | User evaluation information providing method and user evaluation information providing system | |
| CN108596695B (en) | Entity pushing method and system | |
| CN108429776B (en) | Network object pushing method, device, client, interaction equipment and system | |
| CN107679103B (en) | Attribute analysis method and system for entity | |
| CN113205382B (en) | Method, apparatus, device, storage medium and program product for determining object | |
| CN104616178A (en) | Recommendation method of E-commerce goods based on big-data multi-label classification method | |
| CN110634015A (en) | Consumption habit analysis system based on computer software | |
| CN104615721A (en) | Method and system for recommending communities based on returned goods related information | |
| CN106022841A (en) | Information transmission method and information transmission device | |
| CN111626767A (en) | Resource data distribution method, device and equipment | |
| CN105488705A (en) | Auxiliary system and method of online shopping | |
| CN109272351B (en) | Passenger flow line and passenger flow hot area determining method and device | |
| CN110427545B (en) | Information pushing method and system | |
| US20190318386A1 (en) | Information processing method, program, information processing system, and information processing apparatus | |
| CN111091409B (en) | Client tag determination method and device and server | |
| CN102789615B (en) | Book information correlation recommendation method, server and system | |
| JP2016062358A (en) | Extractor, and method and program for extraction | |
| CN111681055A (en) | Commodity re-purchasing pushing method, system, equipment and computer readable medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |