CN110517103B - Commodity recommendation method and system based on user collection - Google Patents

Commodity recommendation method and system based on user collection Download PDF

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CN110517103B
CN110517103B CN201910614834.4A CN201910614834A CN110517103B CN 110517103 B CN110517103 B CN 110517103B CN 201910614834 A CN201910614834 A CN 201910614834A CN 110517103 B CN110517103 B CN 110517103B
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CN110517103A (en
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范芳铭
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Guangzhou Pinwei Software Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • 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
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
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    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
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    • G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
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    • 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
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0641—Shopping interfaces
    • G06Q30/0643—Graphical representation of items or shoppers

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Abstract

The invention discloses a commodity recommendation method based on user collection, which comprises the following steps of S1: acquiring information of the collected commodity in the latest time period of the user from the user data through data extraction; s2: inquiring the selling condition of the collected commodity in the step S1 in each channel, and judging whether the collected commodity is a shelved commodity or not at the current time; if yes, executing S3; if not, the information of the on-sale/preheated goods is directly pushed to the user; s3: judging whether the user has purchased the collected commodity within a set time range, and judging whether the collected commodity needs to be recommended to the user according to the class of the collected commodity; s4: and extracting and screening out the on-sale/preheated commodities belonging to the same money as the collected commodities according to the sku code of the collected commodities, and then selecting one of the on-sale/preheated commodities to push to a collection page for display. And the commodity recommendation system comprises a server for executing the method. According to the invention, the goods which are collected by the user and are taken off the shelf and sold on line again are recommended again, so that the sales conversion rate is improved.

Description

Commodity recommendation method and system based on user collection
Technical Field
The invention relates to the field of electronic commerce data processing, in particular to a commodity recommendation method and system based on user collection.
Background
At present, the existing online shopping software can provide a collection function, so that a user can collect commodities in the same list page before whether to purchase or not is not determined, the user can know the selling condition of the commodities in the collection page conveniently, and the user can purchase the required commodities in the collection page directly.
However, the user may use the collection function in shopping software as follows: when the user collects the commodity, the commodity is in a special selling and robbing mode, and the commodity in the current shelf-down state is not displayed on the collection page directly because the commodity is not successfully purchased at the time, so that the user cannot see the condition of collecting the commodity from the collection page. If the same commodity is online again in a short time, the purchase probability of the user is higher than that of a general commodity, but the commodity which is collected in the early stage and is taken off the shelf is not displayed on the collection page, so that the user cannot know whether the collected commodity is in a selling state at present, the conversion rate during a large-scale sales promotion is directly influenced, and the sales amount cannot be increased.
Disclosure of Invention
In order to overcome the defects of the prior art, one of the purposes of the invention is to provide a commodity recommendation method based on user collection, which can help users to know the current selling condition of the prior collected commodities and recommend the same commodities newly put on shelves to the users, thereby improving the sales conversion rate.
The second object of the present invention is to provide a commodity recommendation system based on user collection, wherein the recommendation method is executed by a server, so as to improve sales conversion rate.
One of the purposes of the invention is realized by adopting the following technical scheme:
a commodity recommendation method based on user collection comprises the following steps:
step S1: acquiring information of the collected commodity in the latest time period of the user from the user data through data extraction; wherein the user commodity collection information comprises the commodity type of the collected commodity and the commodity collection time;
step S2: inquiring the selling condition of the collected commodity in the step S1 in each channel, and judging whether the collected commodity is a shelved commodity or not at the current time; if yes, executing step S3; if not, the on-sale/preheating information of the collected commodity is directly pushed to the user;
step S3: judging whether the user has purchased the collected commodity within a set time range, and judging whether the collected commodity needs to be recommended to the user according to the class of the collected commodity; the time range is set from the collection time starting point to the current time;
step S4: the extracting step S3 judges the sku code of the collected commodity which is required to be recommended to the user, screens out the on-sale/preheated commodity which belongs to the same money as the collected commodity according to the sku code of the collected commodity, and selects one commodity with the same money from the on-sale/preheated commodity and pushes the commodity to a collection page for online display again.
Further, the latest time period in step S1 refers to N months from the current time, where n=1, 2, or 3.
Further, in the step S2, the positioning of the article that has been put down is that the article is put down in the up-down state, or that the article is put down in the up-down state of the special field is that the article is put down.
Further, the items of the collected commodity in the step S3 include daily household items and household appliances 3C, and the case that whether the collected commodity needs to be recommended to the user at least includes the following cases: if the type of the collected commodity is a commodity of a daily household type, whether the user has purchased the collected commodity of the daily household type within a set time range or not is judged as the collected commodity to be continuously recommended to the user; if the type of the collected commodity is the commodity of the household appliance 3C class, judging that the user does not purchase the collected commodity of the household appliance 3C class within the set time range, and judging that the collected commodity is required to be continuously recommended to the user; when it is determined that the user has purchased the collected commodity of the home appliance 3C class within the set time range, the collected commodity is determined not to need to be recommended to the user.
Further, in the step S3, when the user has purchased the collected commodity, it is further required to determine whether the commodity belongs to a commodity that can be purchased repeatedly, and if so, the commodity is recommended to the user; if not, no recommendation is made to the user.
Further, in the step S4, the method for determining whether the current time is within the display time range of the shelves is that the goods are on-shelf or off-shelf, and the state of the special field is that the goods are on-shelf or off-shelf; the judging method of the commodity preheating is that whether the commodity is preheated or not is marked as yes in the preheating time of the current time belonging to the stage, the state of loading and unloading the commodity is on shelf, and the state of loading and unloading the commodity in the special field is on shelf.
Further, the number of the same commodities screened in the step S4 is as follows: if only one commodity with the same money is found for selling/preheating, the commodity is directly pushed to a collection page for display; if a plurality of same-money on-sale/pre-heated commodities are found, selecting the commodity with the highest priority for pushing, wherein the priority of the on-sale commodity is higher than that of the pre-heated commodity; if a plurality of commodities are found, the commodity with the latest selling time is preferentially pushed and ended; if the selling times are the same, randomly selecting one commodity for pushing; when a plurality of preheated commodities are found, one commodity with the earliest pushing and selling time is pushed.
Further, in the step S4, if the on-sale/preheated goods belonging to the same money as the collected goods cannot be selected according to the sku code of the collected goods, the details of the placed goods of the goods are continuously displayed in the collection page.
Further, before pushing a same commodity to the collection page for display in step S4, filtering the commodity information displayed on the collection page, retaining the commodity in the collection page in the selling/preheating state at the current time, replacing the shelved commodity with the same commodity in the selling/preheating state, and displaying the commodity in the collection page in sequence according to the collection time of the original commodity.
The second purpose of the invention is realized by adopting the following technical scheme:
a commodity recommendation system based on user collection comprises a server, wherein the server executes the commodity recommendation method based on user collection.
Compared with the prior art, the invention has the beneficial effects that:
the goods which are collected by the user and are taken off the shelf and sold on line again are found and recommended to the user again, so that the purchase rate of the user can be improved, and the sales conversion rate is improved.
Drawings
Fig. 1 is a flow chart of a commodity recommendation method based on user collection according to the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and detailed description, wherein it is to be understood that, on the premise of no conflict, the following embodiments or technical features may be arbitrarily combined to form new embodiments.
Example 1
A commodity recommendation method based on user collection, as shown in figure 1, comprises the following steps:
step S1: acquiring information of the collected commodity in the latest time period of the user from the user data through data extraction; the data extraction may use etl tools (etl is an abbreviation of english Extract-Transform-Load, and is used to describe a process of extracting Extract, transposing Transform, and loading Load from a source end to a destination end of data) to obtain user's collection information from a database of a purchasing platform, where the user's collection information includes information about a category of a collection commodity, a time of commodity collection, and the like.
The above-mentioned latest time period defaults to within N months from the current time, where n=1, 2, or 3; and the number of the extracted collection commodity information is 300 at most.
Step S2: inquiring the selling condition of the collected commodity in the step S1 in each channel, and judging whether the collected commodity is a shelved commodity or not at the current time; if yes, executing step S3; if not, the information of the on-sale/preheated goods is directly pushed to the user;
checking whether the current time of the collected commodity is in a selling state or not in real time, comprehensively searching each channel, and if the collected commodity sold at the current time is found, displaying on a collection page of a user for the user to select and purchase; if the collected commodity is not found to be in the on-sale/preheating state in each channel, the collected commodity is judged to be the off-shelf commodity, and the same commodity of the off-shelf commodity is searched for recommendation.
And the commodity under shelf is defined as the commodity under shelf state being under shelf or the commodity under shelf state being under shelf.
Step S3: judging whether the user has purchased the collected commodity within a set time range, and judging whether the collected commodity needs to be recommended to the user according to the class of the collected commodity; the time range is set from the collection time starting point to the current time;
judging whether the products which are put down are purchased or not, and carrying out secondary screening on the products which are put down by the user, wherein the price of the products which are similar to the 3C products of the household appliances is higher than that of the products which are daily used in the household appliances, so that the probability of purchasing the products which are similar to the 3C products of the household appliances in a short time after the user purchases the products which are put down by the user is small, and the products which are put down and are purchased and are similar to the 3C products of the household appliances are not recommended to be displayed in a collection page; similar to daily household products, such as clothing products, daily necessities, cosmetics and the like, the possibility of re-purchasing by the user is still high even if the user has purchased the products, so that the user can still recommend the products for secondary purchase even if the user purchases the products; if the household appliances are 3C or daily household commodities, no purchase record is provided within a set time range, the household appliances are in a state of being required to recommend commodities to users.
When the user has purchased the collected commodity, whether the commodity belongs to the commodity capable of being purchased repeatedly is also judged, if the commodity is purchased only once by the user, the commodity is marked as the commodity incapable of being purchased repeatedly, and if the commodity is not limited by the number of times of purchase, the commodity is marked as the commodity capable of being purchased repeatedly; if the user has purchased the collected commodity and the commodity belongs to the repeatedly-purchased commodity, recommending to the user; if not, no recommendation is made to the user.
Step S4: the extracting step S3 judges the sku code of the collected commodity which is required to be recommended to the user, screens out the on-sale/preheated commodity which belongs to the same money as the collected commodity according to the sku code of the collected commodity, and selects one commodity with the same money from the on-sale/preheated commodity and pushes the commodity to a collection page for online display again.
Each commodity is marked with a sku code on the shopping e-commerce platform, so that the e-commerce platform can conveniently identify the commodity. And after the sku codes of the collected commodities are extracted, the same type of commodities with the same sku codes of the collected commodities are screened out from a commodity warehouse according to the sku codes, and then the commodities in a selling/preheating state are selected out of the same type of commodities. In the judging method of the sold commodity, the current time belongs to the display time range of the stage, the state of loading and unloading the commodity is "loading", and the state of loading and unloading the commodity in the special field is "loading"; the judging method of the commodity preheating is that whether the commodity is preheated or not is marked as yes in the preheating time of the current time belonging to the stage, the state of loading and unloading the commodity is on shelf, and the state of loading and unloading the commodity in the special field is on shelf.
If only one commodity with the same money is found for selling/preheating, the commodity is directly pushed to a collection page for display; if a plurality of on-sale/preheated commodities with the same money are found, selecting the commodity with the highest priority for pushing, wherein the priority of the on-sale commodity is higher than that of the preheated commodity, namely, when a plurality of on-sale commodities and preheated commodities are found, the on-sale commodity is selected for pushing; if a plurality of commodities are found, the commodity with the latest selling time is preferentially pushed and ended; if the selling times are the same, randomly selecting one commodity for pushing; when a plurality of preheated commodities are found, one commodity with the earliest pushing and selling time is pushed.
Before pushing a commodity of the same money to a collection page for display, filtering commodity information displayed by the collection page, reserving the commodity which is still in a selling/preheating state at the current time in the collection page, replacing the commodity which is put down by the commodity which is sold/preheated in the same money, and putting down the commodity which is found out to be replaced in the same money on a label which is put down by the commodity and is put on line again so as to enable a user to know the commodity replacement condition; and then, the commodities in the collection page are displayed in sequence according to the collection time of the original commodities, so that a user can acquire the selling state of each collection commodity at the current time on the collection page, and the selling conversion rate is improved. If the on-sale/pre-heated commodity belonging to the same money as the collected commodity cannot be selected according to the sku size of the collected commodity, the commodity which does not belong to the same money as the collected commodity and is in the on-sale/pre-heated state in the commodity warehouse representing the current time is continuously displayed in the collection page, and information such as commodity pictures, commodity names, the platform price of the electronic commerce, market price, selected size and the like is returned.
The method of the embodiment recommends the goods which are collected by the user and are taken off the shelf and sold on line again to the user collection, so that the purchase rate of the user can be improved, and the sales conversion rate is improved.
Example two
A commodity recommendation system based on user collection comprises a server, wherein the server executes the commodity recommendation method of the first embodiment. Finding out the on-sale/pre-heating commodity of the same commodity according to the sku code of the off-shelf commodity, executing the on-sale/pre-heating commodity through a UDP protocol (the UDP protocol is a user datagram protocol), starting data calculation by a UDP practical timing task, and pushing the found commodity to a redis storage system for calling by an interface. The data storage time period of redis is two days, the update frequency of the stored data is daily, the updated data can cover the original data, the commodity on the collection page is updated every day, and the accuracy is improved.
The above embodiments are only preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but any insubstantial changes and substitutions made by those skilled in the art on the basis of the present invention are intended to be within the scope of the present invention as claimed.

Claims (9)

1. A commodity recommendation method based on user collection, comprising:
step S1: acquiring information of the collected commodity in the latest time period of the user from the user data through data extraction; wherein the user commodity collection information comprises the commodity type of the collected commodity and the commodity collection time; the recent time period refers to N months from the current time, where n=1, 2, or 3;
step S2: inquiring the selling condition of the collected commodity in the step S1 in each channel, and judging whether the collected commodity is a shelved commodity or not at the current time; if yes, executing step S3; if not, the on-sale/preheating information of the collected commodity is directly pushed to the user;
step S3: judging whether the user has purchased the collected commodity within a set time range, and judging whether the collected commodity needs to be recommended to the user according to the class of the collected commodity; the time range is set from the collection time starting point to the current time;
step S4: the extracting step S3 judges the sku code of the collected commodity which is required to be recommended to the user, screens out the on-sale/preheated commodity which belongs to the same money as the collected commodity according to the sku code of the collected commodity, and selects one commodity with the same money from the on-sale/preheated commodity and pushes the commodity to a collection page for online display again.
2. The method according to claim 1, wherein the positioning of the off-shelf commodity in step S2 is that the commodity is "off-shelf" or that the commodity is "off-shelf" in the private area.
3. The method for recommending commodity according to the present invention as set forth in claim 1, wherein the commodity category of the commodity in the step S3 includes daily household class and home appliance 3C class, and the need of recommending the commodity to the user includes at least the following cases: if the type of the collected commodity is a commodity of a daily household type, whether the user has purchased the collected commodity of the daily household type within a set time range or not is judged as the collected commodity to be continuously recommended to the user; if the type of the collected commodity is the commodity of the household appliance 3C class, judging that the user does not purchase the collected commodity of the household appliance 3C class within the set time range, and judging that the collected commodity is required to be continuously recommended to the user; when it is determined that the user has purchased the collected commodity of the home appliance 3C class within the set time range, the collected commodity is determined not to need to be recommended to the user.
4. The method for recommending commodities based on user collection according to claim 3, wherein in the step S3, when the user has purchased the collected commodity, it is further determined whether the commodity belongs to a repeatedly-purchasable commodity, and if the commodity can be repeatedly purchased, the commodity is recommended to the user; if not, no recommendation is made to the user.
5. The method for recommending commodities based on user collection according to claim 1, wherein in the step S4, the commodity is put on shelf in a state of "put on shelf" and the commodity is put on shelf in a state of "put on shelf" in a display time range in which the current time belongs to a shelf period in the judging method of selling commodities; the judging method of the commodity preheating is that whether the commodity is preheated or not is marked as yes in the preheating time of the current time belonging to the stage, the state of loading and unloading the commodity is on shelf, and the state of loading and unloading the commodity in the special field is on shelf.
6. The method for recommending commodities based on user collection according to claim 1, wherein the number of the same commodities screened in step S4 is as follows: if only one commodity with the same money is found for selling/preheating, the commodity is directly pushed to a collection page for display; if a plurality of same-money on-sale/pre-heated commodities are found, selecting the commodity with the highest priority for pushing, wherein the priority of the on-sale commodity is higher than that of the pre-heated commodity; if a plurality of commodities are found, the commodity with the latest selling time is preferentially pushed and ended; if the selling times are the same, randomly selecting one commodity for pushing; when a plurality of preheated commodities are found, one commodity with the earliest pushing and selling time is pushed.
7. The method according to claim 1, wherein in step S4, if the on-sale/pre-heated merchandise belonging to the same type as the collected merchandise cannot be selected according to the sku code of the collected merchandise, the details of the off-shelf merchandise of the merchandise are continuously displayed in the collection page.
8. The method of claim 7, wherein before pushing a same item to the collection page for display in step S4, the information of the item displayed on the collection page is filtered, the item in the collection page that is still in the selling/preheating state at the current time is reserved, the item that is put down is replaced by the same item that is sold/preheated, and the items in the collection page are displayed in order according to the collection time of the original item.
9. A commodity recommendation system based on user collection, comprising a server that executes the commodity recommendation method based on user collection according to any one of claims 1 to 8.
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CN111062787A (en) * 2019-12-28 2020-04-24 广东奥园奥买家电子商务有限公司 Commodity recommendation method, device and equipment based on E-commerce platform
CN111523042B (en) * 2020-07-03 2020-10-23 南京梦饷网络科技有限公司 Method, electronic device, and storage medium for recommending merchandise
CN111882388A (en) * 2020-07-23 2020-11-03 深圳市分期乐网络科技有限公司 Commodity recommendation method, commodity recommendation device, commodity recommendation equipment and storage medium
CN112116427A (en) * 2020-09-22 2020-12-22 深圳市分期乐网络科技有限公司 Commodity recommendation method and device, electronic equipment and storage medium
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