WO2017124414A1 - Procédé de recommandation automatique d'un bon, et système de recommandation - Google Patents

Procédé de recommandation automatique d'un bon, et système de recommandation Download PDF

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Publication number
WO2017124414A1
WO2017124414A1 PCT/CN2016/071671 CN2016071671W WO2017124414A1 WO 2017124414 A1 WO2017124414 A1 WO 2017124414A1 CN 2016071671 W CN2016071671 W CN 2016071671W WO 2017124414 A1 WO2017124414 A1 WO 2017124414A1
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WO
WIPO (PCT)
Prior art keywords
user
consumption
label
period
recording
Prior art date
Application number
PCT/CN2016/071671
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English (en)
Chinese (zh)
Inventor
赵政荣
Original Assignee
赵政荣
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 赵政荣 filed Critical 赵政荣
Priority to PCT/CN2016/071671 priority Critical patent/WO2017124414A1/fr
Publication of WO2017124414A1 publication Critical patent/WO2017124414A1/fr

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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
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the present invention belongs to the field of the Internet, and in particular, to a method and a recommendation system for automatically recommending a discount.
  • Big data analysis refers to the analysis of large-scale data. Big data can be summarized as 5 V, large amount of data (Volume) , Velocity, Variety, Value, Veracity. Big data as the hottest IT nowadays Industry vocabulary, the resulting data warehousing, data security, data analytics, data mining, etc. around the business value of big data The use of the industry has gradually become the focus of profits that the industry is vying for. With the advent of the era of big data, big data analysis has emerged.
  • the present invention needs to provide a kind of situation that the current advertisement delivery is not targeted and cannot be accurately delivered according to the user's consumption habits.
  • the consumer volume is sent more specifically according to the user's customary consumption period, and the user can also quickly grasp the latest consumption information that suits his consumption habits.
  • a method of automatically recommending a discount comprising the following steps:
  • a period in which the label with the most repetition is located is a period of consumption of the user
  • a coupon having the consumption habit tag is sent to the user during the user's consumption habit tag period.
  • An embodiment of the present invention further provides a recommendation system, where the recommendation system includes:
  • Recording unit uploading unit, analyzing unit, sending unit, wherein:
  • a recording unit located locally, for recording a type label corresponding to a consumer product, recording a user-purchased consumer product type label and a purchase time;
  • the uploading unit is located at the local end, and the input end thereof is connected to the output end of the recording unit, and is configured to upload the user-purchased consumer product type label and time to the cloud;
  • the analyzing unit is located in the cloud, and is configured to determine, according to the number of repetitions of the label in a specific time period, a period in which the label with the most repetition is located as a user's consumption habit label period;
  • the sending unit is located in the cloud, and the input end thereof is connected to the output of the analyzing unit, and is configured to send the coupon with the consumption habit tag to the user during the consumption habit tag period of the user.
  • the invention analyzes the user's consumption habits and analyzes the user's consumption habits, so that the consumption volume is more targeted according to the consumption time of the user's habit, and the user can also quickly grasp the latest consumption information in accordance with his own consumption habits.
  • FIG. 1 is a schematic flow chart of a method for automatically recommending a discount according to an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a recommendation system according to an embodiment of the present invention.
  • FIG. 1 is a schematic flowchart of a method for automatically recommending a discount according to an embodiment of the present invention. For the convenience of description, only parts related to the embodiment of the present invention are shown.
  • step S100 the type label corresponding to the consumer product is recorded, and the user purchases the consumer product type label and the purchase time.
  • the technology is prior art. At present, the user will consume the card through a bank card or a credit card, or the cash will also store the consumption record at the counter cash register.
  • step S101 the user purchases the consumer product type label and the time is uploaded to the cloud.
  • the consumption type label can record the user's consumption more carefully, such as 'Sichuan hot pot', 'cycling supplies' and other types. Recording the consumption time can analyze the time period that the user often consumes, and the average user will consume it when he is off work or on weekends.
  • the time period in which the tag with the most repetition is located is determined as the user's consumption habit tag period according to the number of repetitions of the tag in a certain time period.
  • step S103 a coupon having the consumption habit tag is transmitted to the user during the consumer habit tag period of the user.
  • the label with the most repetition is determined as the user's consumption habit label, for example, the user likes to shop at the mall on Saturday, like to buy what brand or price item, and send the purchase ⁇ in advance.
  • the user also enjoyed the corresponding discount.
  • the invention analyzes the user's consumption habits and analyzes the user's consumption habits, so that the consumption volume is more targeted according to the consumption time of the user's habit, and the user can also quickly grasp the latest consumption information in accordance with his own consumption habits.
  • FIG. 2 is a schematic structural diagram of a recommendation system according to an embodiment of the present invention, where the recommendation system includes:
  • Recording unit 21 located locally, for recording the type label corresponding to the consumer product, recording the user's purchase of the consumer product type label and the purchase time;
  • the uploading unit 22 is located at the local location, and its input terminal and recording unit 21 The output end is connected to upload the user-purchased consumer product type label and time to the cloud;
  • the time period in which the label with the most repetition is located is determined as the consumption habit period of the user according to the number of repetitions of the label in a certain period of time;
  • the sending unit 24 is located in the cloud, and its input end and analysis unit 23 The output is connected to send a coupon having the consumption habit tag to the user during a consumption habit tag period of the user.
  • the user is in the recording unit 21
  • the type label corresponding to the consumer product is recorded
  • the uploading unit 22 uploads the user purchase consumer product type label and time to the cloud
  • the analyzing unit 23 Determining, according to the number of repetitions of the label in a specific time period, the period in which the label with the most repetition is located is the user's consumption habit label period, and the sending unit 24 A coupon having the consumption habit tag is sent to the user during the user's consumption habit tag period.
  • the invention analyzes the user's consumption habits and analyzes the user's consumption habits, so that the consumption volume is more targeted according to the consumption time of the user's habit, and the user can also quickly grasp the latest consumption information in accordance with his own consumption habits.

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  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un procédé de recommandation automatique d'un bon ainsi qu'un système de recommandation, qui se rapportent au domaine d'Internet. Le procédé comprend : l'enregistrement d'une étiquette de type correspondant à un produit de consommation, et l'enregistrement d'une étiquette de type d'un produit de consommation acheté par un utilisateur ainsi que d'une heure d'achat (S100) ; le téléchargement dans une extrémité de cloud de l'étiquette de type du produit de consommation acheté par l'utilisateur et de l'heure (S101) ; selon le nombre de répétitions de l'étiquette dans une période spécifique, la détermination d'une période, où une étiquette ayant le plus grand nombre de répétitions est située, comme étant une période d'une étiquette d'habitude de consommation de l'utilisateur (S102) ; et l'envoi d'un bon doté de l'étiquette d'habitude de consommation à l'utilisateur pendant la période de l'étiquette d'habitude de consommation de l'utilisateur (S103). Grâce à l'enregistrement d'une situation de consommation d'un utilisateur et à l'analyse d'une habitude de consommation de l'utilisateur, un bon de consommation est envoyé avec plus de directivité en fonction d'une période de consommation habituelle de l'utilisateur. De plus, l'utilisateur peut également maîtriser plus vite les dernières informations de consommation conformes à son habitude de consommation.
PCT/CN2016/071671 2016-01-21 2016-01-21 Procédé de recommandation automatique d'un bon, et système de recommandation WO2017124414A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2016/071671 WO2017124414A1 (fr) 2016-01-21 2016-01-21 Procédé de recommandation automatique d'un bon, et système de recommandation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2016/071671 WO2017124414A1 (fr) 2016-01-21 2016-01-21 Procédé de recommandation automatique d'un bon, et système de recommandation

Publications (1)

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WO2017124414A1 true WO2017124414A1 (fr) 2017-07-27

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101493925A (zh) * 2009-03-09 2009-07-29 浙江工商大学 一种采用增量式挖掘的零售行业折扣券生成方法
CN102087733A (zh) * 2010-12-08 2011-06-08 天津市翔晟远电力设备实业有限公司 一种顾客消费习惯的分类统计方法及系统
CN102254276A (zh) * 2011-06-27 2011-11-23 百度在线网络技术（北京）有限公司 一种用于分配推广应用的推广预算的方法与设备
US20140310080A1 (en) * 2012-06-04 2014-10-16 Visa International Service Association Systems and methods to process loyalty benefits
CN104881802A (zh) * 2015-06-26 2015-09-02 深圳市华阳信通科技发展有限公司 智能装置及其关联推荐商品的方法
CN105118194A (zh) * 2015-09-01 2015-12-02 浙江博润电子商务有限公司 消费管理系统以及基于互联网的电子商城消费管理系统

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101493925A (zh) * 2009-03-09 2009-07-29 浙江工商大学 一种采用增量式挖掘的零售行业折扣券生成方法
CN102087733A (zh) * 2010-12-08 2011-06-08 天津市翔晟远电力设备实业有限公司 一种顾客消费习惯的分类统计方法及系统
CN102254276A (zh) * 2011-06-27 2011-11-23 百度在线网络技术（北京）有限公司 一种用于分配推广应用的推广预算的方法与设备
US20140310080A1 (en) * 2012-06-04 2014-10-16 Visa International Service Association Systems and methods to process loyalty benefits
CN104881802A (zh) * 2015-06-26 2015-09-02 深圳市华阳信通科技发展有限公司 智能装置及其关联推荐商品的方法
CN105118194A (zh) * 2015-09-01 2015-12-02 浙江博润电子商务有限公司 消费管理系统以及基于互联网的电子商城消费管理系统

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