CN109872220A - A kind of commercial product recommending list method for pushing and commercial product recommending list supplying system - Google Patents
A kind of commercial product recommending list method for pushing and commercial product recommending list supplying system Download PDFInfo
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Abstract
The present invention relates to a kind of commercial product recommending list method for pushing and commercial product recommending list supplying systems, this method comprises: acquisition user data and commodity data on sale；The commodity of user preference and the period of user's purchase commodity, next time time buying of the prediction user to commodity are obtained according to user data；The commodity data with the goods matching of user preference is extracted from commodity data on sale；Commercial product recommending list is generated according to the commodity data of the goods matching with user preference；Commercial product recommending list is sent to the forward direction user of the time buying next time of commodity in the user of prediction.Commercial product recommending list method for pushing and commercial product recommending list supplying system of the invention pushes corresponding commodity from trend user, provides the popularization, positioning and accurate object of commodity for businessman, promotes the transaction of commodity.
Description
Technical Field
The invention relates to the technical field of data platform development, in particular to a commodity recommendation bill pushing method and a commodity recommendation bill pushing system.
Background
With the continuous development of electronic technology and network technology, more and more users enjoy online shopping. At present, no matter online shopping or offline shopping is carried out, shopping history data are precipitated and are not utilized, particularly, new products of merchants are launched, and under the condition that the portrait of user crowd cannot be confirmed, a large amount of market research is needed to determine the positioning of the commodities.
Although the e-commerce industry is developing rapidly, at present, more users like to go to an off-line physical store to experience consumption, so that the users can experience commodities before purchasing and know the commodities more.
Therefore, a commodity recommendation sheet pushing method and a commodity recommendation sheet pushing system are provided.
Disclosure of Invention
In view of the above problems, the present invention is proposed to provide a method and a system for pushing a merchandise recommendation slip, which overcome or at least partially solve the above problems, and can analyze shopping preferences of a user, automatically match merchandise to the user according to a user portrait, facilitate the user to shop more accurately and quickly, and provide a merchant with a clear user portrait and merchandise positioning.
According to one aspect of the invention, a commodity recommendation sheet pushing method is provided, which comprises the following steps:
collecting user data and on-sale commodity data;
acquiring commodities preferred by a user and a commodity purchasing period of the user according to the user data, and predicting the next commodity purchasing time of the user;
extracting commodity data matched with commodities preferred by the user from the commodity selling data;
generating a commodity recommendation sheet according to commodity data matched with the commodities preferred by the user;
sending a recommendation for the item to the user before the predicted time of the next purchase of the item by the user.
Further, the user data comprises a historical shopping list of the user and user identity data, and the commodity selling data comprises commodity types, commodity names and commodity prices.
Further, the method for pushing the commodity recommendation list further includes:
analyzing all historical shopping lists of a user to obtain commodity names, commodity types, commodity prices and shopping time in the historical shopping lists;
predicting the next purchasing time of the user for each commodity in the historical shopping list of the user;
matching commodity names, commodity types and commodity prices in the historical shopping list with commodity-on-sale data, and extracting commodity data matched with commodities preferred by users to generate a commodity recommendation sheet;
and sending a commodity recommendation list to the user before the predicted next time of purchasing the commodity by the user according to the user identity data.
Further, the method for pushing the commodity recommendation list further includes: and predicting the next purchasing time of the user according to the quality guarantee period of the commodities in the historical shopping list of the user.
Further, the method for pushing the commodity recommendation list further includes: and when the commodity discount data is acquired, judging whether the commodity discount data is matched with the commodity name, the commodity type and the commodity price in the historical shopping list of the user, if so, generating a commodity recommendation sheet according to the commodity discount data, and pushing the commodity recommendation sheet to the user.
Further, the method for pushing the commodity recommendation list further includes: generating a user representation from the user-related data;
and judging whether the on-sale commodity data is matched with the user image, if so, generating a commodity recommendation sheet from the on-sale commodity data, and pushing the commodity recommendation sheet to the user.
Further, the method for pushing the commodity recommendation list further includes: the method comprises the steps of collecting evaluation data of a user on the on-sale commodities, dividing the on-sale commodities into different grades according to the evaluation data, and selecting the on-sale commodity data with high grades to generate a commodity recommendation sheet.
Further, the method for pushing the commodity recommendation list further includes: and calculating the accumulated consumption number of the user according to the historical shopping list of the user, and classifying the user into different grades according to the accumulated consumption number.
According to another aspect of the present invention, there is provided a goods recommendation sheet pushing system, including:
the data acquisition module is used for acquiring user data and on-sale commodity data;
the secondary purchase time prediction module is used for acquiring commodities preferred by the user and the period of purchasing the commodities by the user according to the user data and predicting the next purchase time of the commodities by the user;
the preference commodity matching module is used for extracting commodity data matched with commodities preferred by the user from the commodity selling data;
the commodity recommendation sheet generation module is used for generating a commodity recommendation sheet according to the commodity data matched with the commodity preferred by the user;
and the commodity recommendation sheet sending module is used for sending the commodity recommendation sheet to the user before the predicted next time of the user purchasing the commodity.
Further, the user data comprises a historical shopping list of the user and user identity data, and the commodity selling data comprises commodity types, commodity names and commodity prices.
Compared with the prior art, the invention has the following advantages:
the commodity recommendation sheet pushing method and the commodity recommendation sheet pushing system can fully utilize the historical shopping habits, the preference and the basic information of the user, accurately analyze the shopping requirements of the user, push the commodity recommendation sheet and the shopping prompt to the user by combining the historical shopping list of the user, and greatly provide convenience for the user;
the commodity recommendation sheet pushing method and the commodity recommendation sheet pushing system automatically push the corresponding commodities to the user, provide popularization, positioning and accurate objects of the commodities for merchants, and promote the transaction of the commodities.
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The invention is further illustrated by the following figures and examples.
FIG. 1 is a diagram of steps of a method for pushing a recommendation list of goods according to the present invention;
fig. 2 is a block diagram of a product recommendation sheet pushing system according to the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, 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 will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Fig. 1 is a step diagram of a method for pushing a product recommendation slip according to the present invention, and referring to fig. 1, the method for pushing a product recommendation slip according to the present invention includes the following steps:
s101, collecting user data and on-sale commodity data;
specifically, each online shopping or offline shopping of a user automatically acquires and collects personal information of the user and all historical shopping lists of the user through a payment system or a supermarket settlement system, or inquires all historical shopping lists of the user through a WeChat public number or APP (Application), or manually scans shopping tickets and uploads all historical shopping lists, wherein the personal information of the user comprises a personal identification card, and a mobile phone number and a payment account which are associated by taking the personal identification card as a unique identification code.
S102, obtaining commodities preferred by a user and the period of purchasing the commodities by the user according to the user data, and predicting the next time of purchasing the commodities by the user;
specifically, shopping history records in all shopping lists of a user are analyzed, shopping commodities are classified according to the commodity types and the shopping time of shopping, commodities which the user must buy at each time and the period of buying the commodities are analyzed, and the next time of buying the commodities is automatically predicted according to the period of buying the commodities.
S103, extracting commodity data matched with the commodities preferred by the user from the commodity selling data;
s104, generating a commodity recommendation sheet according to the commodity data matched with the commodities preferred by the user;
and S105, sending a commodity recommendation list to the user before the predicted time of the next purchase of the commodity by the user, for example, providing a consumption reminding function for the user when sending the commodity recommendation list to the user.
The user data comprises a historical shopping list of a user and user identity data, and the commodity-on-sale data comprises commodity types, commodity names and commodity prices.
The commodity recommendation sheet pushing method can fully utilize the historical shopping habits, the preferences and the basic information of the user, accurately analyze the shopping requirements of the user, push the commodity recommendation sheet by combining the historical shopping list of the user, push the shopping reminder to the user, and greatly provide convenience for the user.
The commodity recommendation sheet pushing method automatically pushes the corresponding commodities to the user, provides promotion, positioning and accurate objects of the commodities for merchants, and promotes the transaction of the commodities.
Further, the method for pushing the commodity recommendation list further includes:
analyzing all historical shopping lists of a user to obtain commodity names, commodity types, commodity prices and shopping time in the historical shopping lists;
predicting the next purchasing time of the user for each commodity in the historical shopping list of the user;
matching commodity names, commodity types and commodity prices in the historical shopping list with commodity-on-sale data, and extracting commodity data matched with commodities preferred by users to generate a commodity recommendation sheet;
specifically, after the user places an order, the historical shopping list is divided into a plurality of commodity sub-orders according to different commodity types, the purchase period and the matched commodity on sale are calculated for the commodities of each commodity sub-order, and a plurality of commodity recommendation lists are formed.
And sending a commodity recommendation list to the user before the predicted next time of purchasing the commodity by the user according to the user identity data.
Further, the method for pushing the commodity recommendation list further includes: and predicting the next purchasing time of the user according to the quality guarantee period of the commodities in the historical shopping list of the user.
Further, the method for pushing the commodity recommendation list further includes: and when the commodity discount data is acquired, judging whether the commodity discount data is matched with the commodity name, the commodity type and the commodity price in the historical shopping list of the user, if so, generating a commodity recommendation sheet according to the commodity discount data, and pushing the commodity recommendation sheet to the user.
Further, the method for pushing the commodity recommendation list further includes:
generating a user representation from the user-related data;
specifically, the user-related data includes gender, occupation, region, hobbies, favorite commodities, favorite brands and the like input by the user during login, for example, personal information of the user and shopping hobbies of the user are used as user images, and the commodities are automatically matched with the user, so that the user can shop more accurately and quickly, convenience is provided for the user, and clear user images and commodity positioning are provided for merchants.
And judging whether the on-sale commodity data is matched with the user image, if so, generating a commodity recommendation sheet from the on-sale commodity data, and pushing the commodity recommendation sheet to the user.
Specifically, the on-sale commodity is matched with the user portrait, the matching degree of the on-sale commodity and the user is automatically judged, and when the on-sale commodity is matched with the shopping information and habits of the user, a recommended commodity list is generated according to the on-sale commodity and pushed to the user.
Further, the method for pushing the commodity recommendation list further includes: the method comprises the steps of collecting evaluation data of a user on the on-sale commodities, dividing the on-sale commodities into different grades according to the evaluation data, and selecting the on-sale commodity data with high grades to generate a commodity recommendation sheet.
Further, the method for pushing the commodity recommendation list further includes: and calculating the accumulated consumption number of the user according to the historical shopping list of the user, classifying the user into different grades according to the accumulated consumption number, and inviting the user with high grade to the physical store for experience.
Further, the method for pushing the commodity recommendation list further includes: providing a user with columns for activity release and participation so that the user can obtain money free of charge; releasing commodity evaluation content, and displaying details at the front end to enable a user to know the commodity content; allowing the user to recharge, and storing recharging records and consumption records; the grouping service is provided for the user, so that the commodity price for the user to shop is lower; allowing the user to snatch free commodities at a specific time according to the time setting; allowing the user to obtain the commodity for free by sharing the commodity content with the friends; allowing the user to view order management, address management, coupon management, activity management, and my collection.
Further, the method for pushing the commodity recommendation list further includes: inputting and storing a commodity list, and storing a commodity label and a commodity order; storing a list of commodity orders and periodic order details; inputting and storing a merchant list and merchant detailed information; inputting and storing basic information of the user and a historical shopping list of the user.
Further, the method for pushing the commodity recommendation list further includes: logging in or registering a merchant and a user; displaying the income of the merchant in the current month and an order number list, and displaying the order sale condition of the current month in the last week every day; the merchant and the user log out, modify the password and change the coverage cell.
For simplicity of explanation, the method embodiments are described as a series of acts or combinations, but those skilled in the art will appreciate that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently with other steps in accordance with the embodiments of the invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Fig. 2 is a block diagram of a product recommendation slip pushing system according to the present invention, and referring to fig. 2, the product recommendation slip pushing system according to the present invention includes:
the data acquisition module is used for acquiring user data and on-sale commodity data;
the secondary purchase time prediction module is used for acquiring commodities preferred by the user and the period of purchasing the commodities by the user according to the user data and predicting the next purchase time of the commodities by the user;
the preference commodity matching module is used for extracting commodity data matched with commodities preferred by the user from the commodity selling data;
the commodity recommendation sheet generation module is used for generating a commodity recommendation sheet according to the commodity data matched with the commodity preferred by the user;
and the commodity recommendation sheet sending module is used for sending the commodity recommendation sheet to the user before the predicted next time of the user purchasing the commodity.
The commodity recommendation list pushing system can fully utilize the historical shopping habits, the preferences and the basic information of the user, accurately analyze the shopping requirements of the user, push the commodity recommendation list by combining the historical shopping list of the user, push the shopping reminder to the user, and greatly provide convenience for the user.
The commodity recommendation sheet pushing system automatically pushes corresponding commodities to the user, provides promotion, positioning and accurate objects of the commodities for merchants, and promotes the transaction of the commodities.
Further, the user data comprises a historical shopping list of the user and user identity data, and the commodity selling data comprises commodity types, commodity names and commodity prices.
And further, the repurchase time prediction module is also used for predicting the next purchase time of the user according to the quality guarantee period of the commodities for the commodities in the historical shopping list of the user.
Further, the commodity recommendation sheet generation module is further configured to determine whether the commodity discount data matches the commodity name, the commodity type, and the commodity price in the historical shopping list of the user when the commodity discount data is acquired, and if the commodity discount data matches the commodity name, the commodity type, and the commodity price, generate a commodity recommendation sheet according to the commodity discount data, and push the commodity recommendation sheet to the user.
Further, the method for pushing the commodity recommendation list further includes:
a user representation generation module for generating a user representation from the user-related data;
wherein,
and the commodity recommendation sheet generation module is also used for judging whether the on-sale commodity data is matched with the user image, and if so, generating a commodity recommendation sheet from the on-sale commodity data and pushing the commodity recommendation sheet to the user.
Further, the commodity recommendation list generating module is further used for collecting evaluation data of the user on the commodities sold in the market, dividing the commodities sold in the market into different grades according to the evaluation data, and selecting the commodity data sold in the market with high grade to generate the commodity recommendation list.
Further, the above mentioned commodity recommendation form pushing system further includes: and the user grade calculation module is used for calculating the accumulated consumption number of the user according to the historical shopping list of the user, dividing the user into different grades according to the accumulated consumption number and inviting the user with high grade to experience in the physical store.
Specifically, the user supplements with money in a commodity recommendation sheet pushing system to obtain membership, so that the member can experience brand commodities in an entity store on line, and can directly take away favorite commodities. The treatments enjoyed by different levels of members may also be different, for example, higher level members may carry more valuable and more expensive merchandise. The off-line experience store implements a membership system to control the outflow of goods. On the other hand, the brand party can make brand propaganda in places with large traffic and high accurate user value by only providing a small amount of resources.
Above-mentioned commodity recommendation list push system still includes: the earning fragment module is used for providing activity publishing and participation columns for the user, so that the user can obtain money freely, the user can obtain the corresponding virtual exchange money of the platform freely, the virtual money can be exchanged for equivalent commodities, the stickiness of the user is increased, and the exposure of the commodities is greatly improved; the evaluation area module is used for publishing commodity evaluation content and displaying details at the front end so that a user can know the commodity content, for example, the commodity evaluation content is displayed in a video and text mode, and the cognition of the user on the commodity is improved through the rendering on the content, so that the popularization quality of the commodity is improved more effectively; the recharging module is used for allowing the user to recharge and storing recharging records and consumption records, so that the user can directly recharge to obtain the virtual currency and obtain the commodity at a lower price; the group-piecing module is used for providing group-piecing service for the user, so that the commodity price for the user to shop is lower, the order placing quantity of the user is improved, the stickiness of the user is enhanced, and the user can obtain commodities at a lower price; the system comprises a point-ordering and robbing module, a point-ordering and robbing module and a control module, wherein the point-ordering and robbing module is used for setting to allow a user to robbe free commodities at specific time according to time, and carrying out point-ordering and robbing activities at different time points to allow the user to pay attention to the commodity contents in real time; the friend power-assisted module is used for allowing the user to freely obtain commodities by sharing the commodity contents with friends, is beneficial to fission of a new user, allows the user to share the commodity contents through a channel in a short time, and allows the user quantity to be increased more rapidly; and the user center module is used for allowing a user to check order management, address management, coupon management, activity management and my collection.
Further, the above mentioned commodity recommendation form pushing system further includes: the commodity data management module is used for inputting and storing a commodity list, and storing commodity labels and commodity orders; the order management module is used for storing a commodity order list and periodic order details; the merchant data management module is used for inputting and storing a merchant list and merchant detailed information; and the user data management module is used for inputting and storing the basic information of the user and the historical shopping list of the user.
Further, the above mentioned commodity recommendation form pushing system further includes: the login and registration module is used for logging in or registering a merchant and a user; the report management module is used for displaying the income of the merchant in the current month and the order list and displaying the order sale condition of the merchant in the last week of the current month every day; the setting module is used for logging out of a user merchant and a user, modifying a password and changing a coverage cell, modifying a goods receiving address and a goods receiver by the user, setting self-service payment and payment on delivery and the like.
For the system embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The commodity recommendation sheet pushing system is constructed by integrating two open source frames of Spring and MyBatis by using an SSM (Spring + Spring MVC + MyBatis, wherein Spring MVC is part of the content in Spring), and optimizes the whole commodity recommendation sheet pushing system by using a Redis (Redis is an open source written by using ANSI C language, supports a network, can be based on a log type and a Key-Value database which can be stored or persisted, and provides API of multiple languages) three-level cache and mq (Message queue, namely a container for storing messages) asynchronous notification, so that the burden of the database is reduced, the system performance is improved, a Redis distributed lock is added, high concurrency problems are solved, and the integrity of data is ensured.
In practical application, a first specific business process of the commodity recommendation sheet pushing system is as follows: the method comprises the steps that a merchant registers and logs in a commodity recommendation sheet pushing system, merchant information and commodity information are edited and input, the commodity recommendation sheet pushing system manages merchant commodities, a user registers and logs in the commodity recommendation sheet pushing system, after commodities are selected and placed, the commodity recommendation sheet pushing system automatically splits a historical shopping list of the time into a plurality of commodity sub-orders according to different commodity types, calculation of a purchase period and matched commodities on sale are carried out on the commodities of each commodity sub-order, a plurality of commodity recommendation sheets are formed, pushing time of each commodity recommendation sheet is calculated, for example, the pushing time is 2 days before the next purchase time, the commodity recommendation sheets are pushed to the user at the pushing time, and a to-be-delivered reminder is pushed to the merchant.
In practical application, a second specific business process of the commodity recommendation sheet pushing system is as follows: the user fills in personal related information and preferences, the commodity recommendation form pushing system recommends matched commodities, the user places an order, the commodity recommendation form pushing system generates an order, the user confirms the order to complete the order, the user evaluates the order, and order information is fed back to the background.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A commodity recommendation sheet pushing method is characterized by comprising the following steps:
collecting user data and on-sale commodity data;
acquiring commodities preferred by a user and a commodity purchasing period of the user according to the user data, and predicting the next commodity purchasing time of the user;
extracting commodity data matched with commodities preferred by the user from the commodity selling data;
generating a commodity recommendation sheet according to commodity data matched with the commodities preferred by the user;
sending a recommendation for the item to the user before the predicted time of the next purchase of the item by the user.
2. The method for pushing the commodity recommendation list according to claim 1, wherein the user data comprises a historical shopping list and user identity data of the user, and the commodity selling data comprises commodity types, commodity names and commodity prices.
3. The commodity recommendation bill pushing method according to claim 2, further comprising:
analyzing all historical shopping lists of a user to obtain commodity names, commodity types, commodity prices and shopping time in the historical shopping lists;
predicting the next purchasing time of the user for each commodity in the historical shopping list of the user;
matching commodity names, commodity types and commodity prices in the historical shopping list with commodity-on-sale data, and extracting commodity data matched with commodities preferred by users to generate a commodity recommendation sheet;
and sending a commodity recommendation list to the user before the predicted next time of purchasing the commodity by the user according to the user identity data.
4. The commodity recommendation bill pushing method according to claim 3, further comprising:
and predicting the next purchasing time of the user according to the quality guarantee period of the commodities in the historical shopping list of the user.
5. The commodity recommendation bill pushing method according to claim 4, further comprising: and when the commodity discount data is acquired, judging whether the commodity discount data is matched with the commodity name, the commodity type and the commodity price in the historical shopping list of the user, if so, generating a commodity recommendation sheet according to the commodity discount data, and pushing the commodity recommendation sheet to the user.
6. The commodity recommendation bill pushing method according to claim 5, further comprising:
generating a user representation from the user-related data;
and judging whether the on-sale commodity data is matched with the user image, if so, generating a commodity recommendation sheet from the on-sale commodity data, and pushing the commodity recommendation sheet to the user.
7. The commodity recommendation bill pushing method according to claim 6, further comprising:
the method comprises the steps of collecting evaluation data of a user on the on-sale commodities, dividing the on-sale commodities into different grades according to the evaluation data, and selecting the on-sale commodity data with high grades to generate a commodity recommendation sheet.
8. The commodity recommendation bill pushing method according to claim 7, further comprising:
and calculating the accumulated consumption number of the user according to the historical shopping list of the user, and classifying the user into different grades according to the accumulated consumption number.
9. The commodity recommendation bill pushing system is characterized by comprising:
the data acquisition module is used for acquiring user data and on-sale commodity data;
the secondary purchase time prediction module is used for acquiring commodities preferred by the user and the period of purchasing the commodities by the user according to the user data and predicting the next purchase time of the commodities by the user;
the preference commodity matching module is used for extracting commodity data matched with commodities preferred by the user from the commodity selling data;
the commodity recommendation sheet generation module is used for generating a commodity recommendation sheet according to the commodity data matched with the commodity preferred by the user;
and the commodity recommendation sheet sending module is used for sending the commodity recommendation sheet to the user before the predicted next time of the user purchasing the commodity.
10. The item recommendation slip pushing system according to claim 9, wherein the user data comprises a historical shopping list and user identity data of the user, and the item on sale data comprises an item type, an item name and an item price.
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