WO2020019189A1 - Système de guide d'achat web basé sur des mégadonnées - Google Patents

Système de guide d'achat web basé sur des mégadonnées Download PDF

Info

Publication number
WO2020019189A1
WO2020019189A1 PCT/CN2018/097016 CN2018097016W WO2020019189A1 WO 2020019189 A1 WO2020019189 A1 WO 2020019189A1 CN 2018097016 W CN2018097016 W CN 2018097016W WO 2020019189 A1 WO2020019189 A1 WO 2020019189A1
Authority
WO
WIPO (PCT)
Prior art keywords
online shopping
user
product
product list
shopping guide
Prior art date
Application number
PCT/CN2018/097016
Other languages
English (en)
Chinese (zh)
Inventor
陈钦鹏
Original Assignee
深圳齐心集团股份有限公司
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 深圳齐心集团股份有限公司 filed Critical 深圳齐心集团股份有限公司
Priority to PCT/CN2018/097016 priority Critical patent/WO2020019189A1/fr
Publication of WO2020019189A1 publication Critical patent/WO2020019189A1/fr

Links

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 invention relates to a network-based shopping guide system based on big data.
  • big data has more comprehensive characteristics. From the data dimension, time dimension, spatial dimension, and cross-border data, they all converge. The value of big data is reflected in more accurate and personalized services provided to customers.
  • big data has great value potential in artificial intelligence such as language, vision, and prediction.For example, behavior analysis based on consumer habits can give Users bring many significant conveniences, which can provide customers with precise and extreme personalized services.
  • the system including a server and a plurality of mobile terminals connected to the server.
  • the server is provided with an e-commerce network service platform and a network shopping guide module.
  • the mobile terminal is provided with an online shopping client.
  • the online shopping client is used for Log on to the e-commerce network service platform to display the product list;
  • the online shopping guide module is used to provide a product recommendation list for the online shopping client; wherein the process of the network shopping module providing the product recommendation list to the online shopping client is as follows:
  • the online shopping client receives the user's product search command and collects the product list browsed by the user, and forwards the product search command and the product list browsed by the user to the e-commerce network service platform;
  • the e-commerce network service platform receives the search product command sent by the online shopping client and the product list browsed by the user and stores it.
  • the online shopping guide module establishes a subjective information model and analyzes the product search command of the online shopping client and the product list browsed by the user to obtain users.
  • Subjective information including age, gender, and preferences;
  • the online shopping guide module sends the recommended product list to the e-commerce network service platform according to the search product order sent by the online shopping client and the subjective information of the user;
  • the e-commerce network service platform sends the recommended product list to the online shopping client for users to select.
  • the process of providing a product recommendation list for the online shopping client by the online shopping guide module further includes: the online shopping client collects the product list information in the recommended product list browsed by the user, and sends the product list information to the e-commerce network service platform;
  • the e-commerce network service platform receives and stores the product list information, and the network shopping guide module performs a comparative analysis on the product list information to modify the subjective information model.
  • the online shopping guide module also recommends the product list according to the product evaluation, and the online shopping client analyzes the product list information in the recommended product list: comprehensively evaluate the product list information by the quality, price, and user evaluation of similar products, and according to the comprehensive The ranking of the product list information is from high to low.
  • the online shopping guide module establishes and analyzes the subjective information model of the online shopping client's product search command and the product list browsed by the user, including:
  • the online shopping guide module correlates the search product commands of the online shopping client within a preset time period to form a related set, and analyzes the related set to obtain a combined combined product command;
  • the online shopping guide module compares the product list browsed by the user with the subjective information model for a period of time to modify the combined product order to make it more accurately match the user's subjective trademark list.
  • the present invention has the following advantages:
  • the creative user's search for product commands within a certain period of time are associated and combined to form a combined product command, which can get a closer search list, which greatly reduces the user's search time;
  • This application judges the user ’s age range, gender, and preferences through user operations. On the one hand, it respects the user ’s privacy (the user ’s true information is not obtained). On the other hand, it can provide more information based on the user ’s actual needs rather than the actual age good service;
  • the user's subjective attribute set S is used to describe the user's preferences and understand the user's psychological needs.
  • the combination of product orders is also modified to further reduce users. Search time, and get a more subjective list of users, giving users a good sense of experience; this application can not only speed up the search, but also accurately retrieve the customized information that users need, which is very humane Experience.
  • the invention provides a big data-based online shopping guide system.
  • the system includes a server and a plurality of mobile terminals connected to the server.
  • the server is provided with an e-commerce network service platform and a network shopping guide module.
  • the mobile terminal is provided with an online shopping client.
  • the online shopping client is used to log in to the e-commerce network service platform to display the product list;
  • the online shopping guide module is used to provide a product recommendation list for the online shopping client; the process of providing the online shopping client with a product recommendation list is as follows:
  • the online shopping client receives the user's product search command and collects the product list browsed by the user, and forwards the product search command and the product list browsed by the user to the e-commerce network service platform;
  • the e-commerce network service platform receives the search product command sent by the online shopping client and the product list browsed by the user and stores it in the database;
  • the online shopping guide module reads the database to obtain the search product command of the online shopping client and the list of products browsed by the user, and analyzes it according to a pre-established subjective information model (the more times the user uses the e-commerce network service platform, the platform models it The more accurate) to obtain the subjective information of the user, the subjective information includes at least the age range, gender, and preference; wherein preference is data that reflects the user ’s personality and preferences, for example, when users browse e-commerce online service platforms, they often pay attention to Current affairs information, or often pay attention to emotional articles or books, or often pay attention to articles or books full of righteousness, etc., from the attention of information and preset big data judgment methods to analyze user preferences; preferences are used to understand the user's psychological needs ;
  • the online shopping guide module sends the recommended product list to the e-commerce network service platform according to the search product order sent by the online shopping client and the subjective information of the user;
  • the e-commerce network service platform sends the recommended product list to the online shopping client for users to select;
  • the online shopping client collects the product list information in the recommended product list browsed by the user, and sends the product list information to the e-commerce network service platform; the e-commerce network service platform receives and stores the product list information, and the online shopping guide module stores the product list information
  • a comparative analysis is performed to modify the subjective information model.
  • the online shopping guide module establishes and analyzes a subjective information model of the product search command of the online shopping client and the product list browsed by the user, including:
  • the online shopping guide module establishes a subjective information model in advance according to the product list browsed by the user; the subjective information model is used to describe the weight analysis of the subjective information.
  • the online shopping guide module correlates the search merchandise commands of the online shopping client within a preset time period to form an association set, and analyzes the association set to obtain the combined merchandise command. For example, some users do not know when to search for merchandise. What kind of search is required, so several search product commands are required. However, the platforms in the prior art search for a certain command and do not search for the deep needs of the user. For example, a user needs to search for a piece of clothing, There will be several search product orders: “mature, two-piece, professional wear” "dress” "dress, professional” “dress, heavy industry embroidery” "professional wear, slim fit", the search results of existing platforms are targeted at the above key respectively Words are retrieved one by one to get a list of conclusions to the user.
  • the user search command mature, Heavy industry embroidery
  • the e-commerce network service platform of the present application also sets detailed labels for describing the attributes of the products on each of the products on the e-commerce network service platform, including at least the characteristics for describing the subjective characteristics of the user.
  • the label includes product classification (for example, the major category of the product is clothes, then the small category can be subdivided into business wear, casual wear, student wear, etc.), and product specifications (for example, if the product is clothes, it indicates what body type and weight range are suitable) , Product style (for example, if the product is clothes, it indicates that it is capable, gentle, or neutral, etc.), product evaluation (thinner, thinner), and product rating (such as setting a perfect score of 10 points), which is conducive to the online shopping guide module Recommended.
  • product style is the characteristic that describes the subjective characteristics of users. It is also possible to set characteristics that are subjective to the user according to the specific broad category of the product.
  • the invention not only improves the user experience mentioned above, but also improves the intelligent characteristics: when the user does a search when he does not know the specific product style, he no longer needs to explore it step by step.
  • the online shopping guide module of this application is based on its The subjective information model and the combination of product commands to directly give a list of products suitable for the user's subjective information have very good intelligent characteristics.
  • the method of the present invention is not limited to being performed in the chronological order described in the specification, but may also be performed in other chronological order, in parallel, or independently. Therefore, the execution order of the methods described in this specification does not limit the technical scope of the present invention.

Landscapes

  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (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)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

L'invention concerne un système de guide d'achat web basé sur des mégadonnées, le système comprenant une plateforme de service web de commerce électronique, un module de guide d'achat web et un client d'achat web. Le client d'achat web reçoit une instruction de recherche de produit provenant d'un utilisateur, collecte une liste de produits visualisée par l'utilisateur, et transmet celle-ci à la plateforme de service web de commerce électronique. La plateforme de service web de commerce électronique reçoit et stocke l'instruction de recherche de produit et la liste de produits visualisées par l'utilisateur, et le module de guide d'achat web établit un modèle d'informations personnelles et effectue une analyse, de façon à acquérir des informations personnelles de l'utilisateur comprenant un groupe d'âge, un sexe et une préférence. Le module de guide d'achat web recommande, selon l'instruction de recherche de produit envoyée par le client web et les informations personnelles de l'utilisateur, une liste de produits, et les envoie à la plateforme de service web de commerce électronique. La plateforme de service web de commerce électronique envoie la liste de produits recommandés au client d'achat web en vue d'une sélection par l'utilisateur. La présente invention accélère les recherches, et trouve des informations précises répondant à des demandes d'utilisateur, ce qui permet d'obtenir une meilleure expérience.
PCT/CN2018/097016 2018-07-25 2018-07-25 Système de guide d'achat web basé sur des mégadonnées WO2020019189A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2018/097016 WO2020019189A1 (fr) 2018-07-25 2018-07-25 Système de guide d'achat web basé sur des mégadonnées

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2018/097016 WO2020019189A1 (fr) 2018-07-25 2018-07-25 Système de guide d'achat web basé sur des mégadonnées

Publications (1)

Publication Number Publication Date
WO2020019189A1 true WO2020019189A1 (fr) 2020-01-30

Family

ID=69181091

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/097016 WO2020019189A1 (fr) 2018-07-25 2018-07-25 Système de guide d'achat web basé sur des mégadonnées

Country Status (1)

Country Link
WO (1) WO2020019189A1 (fr)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013126648A1 (fr) * 2012-02-22 2013-08-29 Cobrain Company Procédés et appareil de recommandation de produits et services
CN105117418A (zh) * 2015-07-30 2015-12-02 百度在线网络技术（北京）有限公司 基于搜索的服务信息管理系统及方法
CN106530058A (zh) * 2016-11-29 2017-03-22 广东聚联电子商务股份有限公司 一种基于历史搜索浏览记录推荐商品的方法
CN106708821A (zh) * 2015-07-21 2017-05-24 广州市本真网络科技有限公司 基于用户个性化购物行为进行商品推荐的方法
CN107403359A (zh) * 2017-07-20 2017-11-28 义乌洞开网络科技有限公司 一种电商平台商品精准推荐系统及其方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013126648A1 (fr) * 2012-02-22 2013-08-29 Cobrain Company Procédés et appareil de recommandation de produits et services
CN106708821A (zh) * 2015-07-21 2017-05-24 广州市本真网络科技有限公司 基于用户个性化购物行为进行商品推荐的方法
CN105117418A (zh) * 2015-07-30 2015-12-02 百度在线网络技术（北京）有限公司 基于搜索的服务信息管理系统及方法
CN106530058A (zh) * 2016-11-29 2017-03-22 广东聚联电子商务股份有限公司 一种基于历史搜索浏览记录推荐商品的方法
CN107403359A (zh) * 2017-07-20 2017-11-28 义乌洞开网络科技有限公司 一种电商平台商品精准推荐系统及其方法

Similar Documents

Publication Publication Date Title
Wei et al. A survey of e-commerce recommender systems
US20180165745A1 (en) Intelligent Recommendation Method and System
CN109189904A (zh) 个性化搜索方法及系统
CN112015998B (zh) 一种基于用户画像的商品推荐方法
US10198520B2 (en) Search with more like this refinements
US20190355041A1 (en) Cognitive fashion product recommendation system and method
KR100963996B1 (ko) 감성에 기반한 개인별 제품 추천 장치 및 방법, 그 기록 매체
JP2016511906A (ja) 商品検索結果の順位付け
US11704374B2 (en) Systems and methods for personalizing search engine recall and ranking using machine learning techniques
US8321279B2 (en) Rule-based bidding platform
US10956928B2 (en) Cognitive fashion product advertisement system and method
CN106326318B (zh) 搜索方法及装置
Rezaeinia et al. Recommender system based on customer segmentation (RSCS)
CN107993133A (zh) 一种基于自然语言的智能分析推荐方法及系统
US20210217053A1 (en) Methods and apparatuses for selecting advertisements using semantic matching
CN107093122B (zh) 对象分类方法及装置
KR20100123206A (ko) 인공지능에 기반한 랭킹 분석 방법, 이를 기록한 기록 매체, 그 장치
Anusha et al. Segmentation of retail mobile market using HMS algorithm
Wu et al. [Retracted] Using the Mathematical Model on Precision Marketing with Online Transaction Data Computing
WO2020019189A1 (fr) Système de guide d'achat web basé sur des mégadonnées
Pirani et al. Analysis and optimization of online sales of products
JP2017037577A (ja) 装置、方法、および、プログラム
JP2011209979A (ja) 商品推奨方法及び商品推奨システム
Mu Application of user research in E-commerce app design
Kumar et al. The application of artificial intelligence in electronic commerce

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18927694

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18927694

Country of ref document: EP

Kind code of ref document: A1