WO2017012406A1 - 一种商品推荐方法及系统 - Google Patents

一种商品推荐方法及系统 Download PDF

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Publication number
WO2017012406A1
WO2017012406A1 PCT/CN2016/082856 CN2016082856W WO2017012406A1 WO 2017012406 A1 WO2017012406 A1 WO 2017012406A1 CN 2016082856 W CN2016082856 W CN 2016082856W WO 2017012406 A1 WO2017012406 A1 WO 2017012406A1
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Prior art keywords
current user
item
purchase record
module
obtaining
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PCT/CN2016/082856
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English (en)
French (fr)
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王慧民
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中兴通讯股份有限公司
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Publication of WO2017012406A1 publication Critical patent/WO2017012406A1/zh

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    • 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

  • This application relates to, but is not limited to, the field of e-commerce technology.
  • This paper provides a product recommendation method and system to solve the problem that the product recommendation system in the related art cannot make a specific recommendation according to the specifications of the product.
  • a product recommendation method including:
  • the item with the highest degree of matching is recommended to the current user.
  • the method further includes:
  • Data cleaning is performed on the purchase record, and the cleaned data is used as the user information of the current user and stored.
  • the data cleaning is performed on the purchase record, including:
  • the frequency of occurrence of the same category of SKU attributes is counted, and the highest frequency SKU attribute is used as the user information of the current user.
  • the method before the obtaining the purchase record of the current user in the e-commerce system, the method further includes:
  • the obtaining the purchase record of the current user in the e-commerce system includes:
  • Data cleaning is performed on the purchase record or the browsing record, and the cleaned data is used as the user information of the current user and stored.
  • the performing data cleaning on the browsing record includes:
  • the frequency of occurrence of the same category of SKU attributes is counted, and the highest frequency SKU attribute is used as the user information of the current user.
  • the item with the highest matching degree is recommended to the current user, including:
  • a commodity recommendation system comprising:
  • the obtaining module is set to: obtain an inventory unit SKU attribute of each item of the item currently viewed by the current user;
  • a matching module configured to: match the SKU attribute of each item acquired by the obtaining module with the pre-established user information of the current user to obtain a matching degree
  • the recommendation module is configured to: recommend the item with the highest degree of matching obtained by the matching module to the current user.
  • the system further includes a cleaning module
  • the obtaining module is further configured to: before the acquiring module acquires the inventory unit SKU attribute of each item of the item browsed by the current user, acquiring the purchase record of the current user in the e-commerce system;
  • the data cleaning module is configured to: perform data cleaning on the purchase record acquired by the acquiring module, and store the cleaned data as user information of the current user and store the data.
  • the data cleaning module includes:
  • An obtaining unit configured to: acquire an SKU attribute of each item purchased in the purchase record acquired by the obtaining module;
  • a dividing unit configured to: perform parameter category division on a SKU attribute of each item in the purchase record acquired by the obtaining unit;
  • the statistical unit is configured to: count the frequency of occurrence of the SKU attribute of the same category divided by the dividing unit, and use the SKU attribute with the highest frequency as the user information of the current user.
  • system further includes:
  • the determining module is configured to: before the obtaining module acquires the purchase record of the current user in the e-commerce system, determine whether the current user has a purchase record in the e-commerce system;
  • the obtaining module acquires a purchase record of the current user in the e-commerce system, and is set to:
  • the obtaining module determines, in the determining module, that the current user has the e-commerce system Acquiring the purchase record of the current user in the e-commerce system when purchasing a record;
  • the obtaining module when the determining module determines that the current user does not have a purchase record in the e-commerce system, acquires a browsing record of the current user in the e-commerce system;
  • the data cleaning module is configured to: perform data cleaning on the purchase record or the browsing record acquired by the acquiring module, and store the cleaned data as user information of the current user.
  • the data cleaning module includes: a browsing data cleaning unit, configured to: perform data cleaning on the browsing record;
  • Obtaining a subunit configured to: obtain an SKU attribute of each item browsed in the browsing record acquired by the obtaining module;
  • the sub-unit is configured to: perform parameter category division on the SKU attribute of each item in the browsing record obtained by the obtaining sub-unit;
  • the statistical subunit is configured to: count the frequency of occurrence of the SKU attribute of the same category divided by the divided subunit, and use the SKU attribute with the highest frequency as the user information of the current user.
  • the recommendation module is configured to: set the item with the highest matching degree, and remind the current user.
  • the product recommendation method and system provided by the embodiment of the present invention, by acquiring the SKU attribute of each item of the item currently browsed by the current user, matching the SKU attribute of each item with the pre-established user information of the current user.
  • the item with the highest degree of matching is recommended to the current user; in the embodiment of the present invention, the information of the product purchased by the user can be clearly recommended according to the user information, thereby saving the user from purchasing the product.
  • the plurality of specification parameters sequentially make the time required for selection, and avoids the occurrence of a situation in which the user misses the favorite item because the user leaves the page before finding the appropriate parameter combination; the embodiment of the present invention avoids user selection.
  • the cumbersome operation process of the product improves the shopping efficiency and greatly enhances the user shopping experience.
  • FIG. 1 is a flowchart of a product recommendation method according to an embodiment of the present invention
  • FIG. 2 is a flowchart of another product recommendation method according to an embodiment of the present invention.
  • FIG. 3 is a flowchart of data cleaning of a purchase record in a product recommendation method according to an embodiment of the present invention
  • FIG. 5 is a schematic structural diagram of a product recommendation system according to an embodiment of the present invention.
  • FIG. 6 is a schematic structural diagram of another commodity recommendation system according to an embodiment of the present invention.
  • FIG. 7 is a schematic structural diagram of a data cleaning module in a product recommendation system according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of still another product recommendation system according to an embodiment of the present invention.
  • FIG. 1 is a flowchart of a product recommendation method according to an embodiment of the present invention.
  • the embodiment of the present invention provides a product recommendation method for the problem in the related art.
  • the process shown in FIG. 1 includes steps 110 to 130:
  • Step 110 Obtain an inventory quantity unit SKU attribute of each item of the current user's currently viewed item; for an e-commerce, the SKU usually refers to a single item of a product, and each item has a SKU for facilitating e-commerce Brand identification products, such as a multi-color product, there are multiple SKUs, the SKU code is different when the color is different. If the SKU codes of the same product are the same, the confusion will occur, resulting in the wrong product.
  • Step 120 Match the SKU attribute of each item with the pre-established user information of the current user to obtain a matching degree; wherein, analyzing the SKU attribute of each item of the currently viewed item, and the current information in the user information The SKU attributes of the products that the user has purchased in the past match.
  • step 130 the item with the highest matching degree is recommended to the current user.
  • step 103 may include:
  • the item with the highest matching degree is selected, that is, when the current user browses an item, the item with the highest degree of matching with the user information in the currently viewed item of the current user is selected and recommended to the current user by default, thus saving
  • the user selects multiple specification parameters of the product in turn, avoids the cumbersome operation steps of sequentially selecting multiple specification parameters, makes the shopping process simple, and enhances the user's shopping experience; optionally, reminds the current user The way can also be achieved by playing the window.
  • FIG. 2 is a flowchart of another product recommendation method according to an embodiment of the present invention.
  • the method provided in this embodiment may further include the following steps 101 to 102:
  • Step 101 Acquire a purchase record of the current user in the e-commerce system.
  • the purpose of obtaining the purchase record of the current user is to establish user information of the current user, and the purchase record includes the SKU attribute of the item purchased by the current user.
  • Step 102 Perform data cleaning on the purchase record, and store the cleaned data as user information of the current user.
  • the data cleaning of the purchase record is mainly to extract the SKU attribute of the product purchased by the current user from the purchase record.
  • FIG. 3 a flow chart of performing data cleaning on a purchase record in the product recommendation method provided by the embodiment of the present invention.
  • the method for performing data cleaning on the purchase record in this embodiment may include the following steps 210 to 230:
  • Step 210 Obtain a SKU attribute of each item purchased in the purchase record
  • Step 220 Perform parameter category division on the SKU attribute of each item in the purchase record; for example, in the SKU attribute, all color-related attributes are divided into colors, and size-related attributes are divided into sizes.
  • Step 230 the frequency of occurrence of the SKU attribute of the same category is counted, and the frequency of occurrence is the highest.
  • the SKU attribute is used as the user information of the current user.
  • the probability of occurrence of the specific SKU attribute is calculated, and the user information of the current user is established accordingly.
  • FIG. 4 is a flowchart of still another product recommendation method according to an embodiment of the present invention.
  • the method provided in this embodiment may further include the step 100:
  • Step 100 Determine whether the current user has a purchase record in the e-commerce system
  • step 101 in this embodiment may include:
  • Step 1011 When it is determined that the current user has a purchase record in the e-commerce system, obtain a purchase record of the current user in the e-commerce system;
  • Step 1012 When it is determined that the current user does not have a purchase record in the e-commerce system, obtain a browsing record of the current user in the e-commerce system; that is, when the user first purchases in the e-commerce system, Collect data in its browsing history.
  • the step 102 may include: performing data cleaning on the purchase record or the browsing record, and using the cleaned data as the user information of the user and storing the data. That is to say, when the user first collects data from the browsing record when purchasing the e-commerce system, the data is cleaned by the collected data.
  • the method for performing data cleaning on the browsing record in the embodiment of the present invention may include:
  • Parameter classification is performed on the SKU attribute of each item in the browsing record; for example, in the SKU attribute, all color-related attributes are divided into colors, and size-related attributes are divided into sizes.
  • the frequency of occurrence of SKU attributes of the same category is counted, and the SKU attribute with the highest frequency is used as the user information of the current user; in the SKU attribute of the same category, the probability of occurrence of the specific SKU attribute is calculated, thereby establishing the current User's information.
  • the information of the product purchased by the current user is explicitly recommended according to the user information, thereby saving the time required for the user to select a plurality of specification parameters of the product in order to purchase the product. And avoiding the occurrence of the situation that the user misses the favorite product before the user leaves the page before finding the appropriate parameter combination; the embodiment of the invention avoids the cumbersome operation process when the user selects the product, improves the shopping efficiency, and at the same time Big mention Upgrade the user shopping experience.
  • the embodiment of the present invention further provides a product recommendation system.
  • a schematic diagram of a product recommendation system according to an embodiment of the present invention is provided.
  • the product recommendation system provided in this embodiment includes:
  • the obtaining module 11 is configured to: obtain an inventory unit SKU attribute of each item of the item currently browsed by the current user;
  • the matching module 12 is configured to: match the SKU attribute of each item acquired by the obtaining module 11 with the pre-established user information of the current user to obtain a matching degree;
  • the recommendation module 13 is configured to: recommend the item with the highest matching degree obtained by the matching module 12 to the current user.
  • FIG. 6 is a schematic structural diagram of another product recommendation system according to an embodiment of the present invention. Based on the structure of the system shown in FIG. 5, the system provided in this embodiment may further include a data cleaning module 14.
  • the obtaining module 11 in this embodiment is further configured to acquire the current user's purchase in the e-commerce system before the acquiring module 11 acquires the inventory unit SKU attribute of each item of the item browsed by the current user. recording;
  • the data cleaning module 14 is configured to: perform data cleaning on the purchase record acquired by the acquisition module 11, and store the cleaned data as user information of the current user.
  • FIG. 7 is a schematic structural diagram of a data cleaning module in a product recommendation system according to an embodiment of the present invention.
  • the data cleaning module 14 in this embodiment may include:
  • the obtaining unit 41 is configured to: acquire the SKU attribute of each item purchased in the purchase record acquired by the obtaining module 11;
  • the dividing unit 42 is configured to: perform parameter category division on the SKU attribute of each item in the purchase record acquired by the obtaining unit 41;
  • the statistic unit 43 is configured to count the frequency of occurrence of the SKU attribute of the same category divided by the dividing unit 42 and use the SKU attribute with the highest frequency as the user information of the current user.
  • FIG. 8 a schematic structural diagram of another commodity recommendation system according to an embodiment of the present invention is provided. Based on the structure of the system shown in FIG. 6, the system provided in this embodiment may further include:
  • the determining module 15 is configured to: before the obtaining module 11 obtains the current user's purchase record in the e-commerce system, determine whether the current user has a purchase record in the e-commerce system;
  • the obtaining module 11 obtains the purchase record of the current user in the e-commerce system, and is set to:
  • the obtaining module 11 obtains the purchase record of the current user in the e-commerce system when the determining module 15 determines that the current user has a purchase record in the e-commerce system;
  • the obtaining module 11 obtains the browsing record of the current user in the e-commerce system when the determining module 15 determines that the current user does not have a purchase record in the e-commerce system;
  • the data cleaning module 14 is configured to: perform data cleaning on the purchase record or the browsing record acquired by the obtaining module 11, and store the cleaned data as the user information of the current user.
  • the data cleaning module 14 in this embodiment may include: a browsing data cleaning unit, configured to: perform data cleaning on the browsing record;
  • the browsing data cleaning unit may include:
  • Obtaining a subunit configured to: obtain a SKU attribute of each item browsed in the browsing record acquired by the obtaining module 11;
  • Dividing the subunits setting: classifying the SKU attributes of each item in the browsing record obtained by the obtaining subunit;
  • the statistical subunit is set to: count the frequency of occurrence of the SKU attribute of the same category divided by the divided subunit, and use the SKU attribute with the highest frequency as the user information of the current user.
  • the recommendation module in this embodiment is configured to: select the item with the highest matching degree, and remind the current user.
  • the product recommendation system provided by the embodiment of the present invention is a system applying the above method, that is, all the embodiments of the foregoing methods are applicable to the device, and all of the same or similar beneficial effects can be achieved.
  • all or part of the steps of the above embodiments may also be implemented by using an integrated circuit. These steps may be separately fabricated into individual integrated circuit modules, or multiple modules or steps may be fabricated into a single integrated circuit module. achieve.
  • the devices/function modules/functional units in the above embodiments may be implemented by a general-purpose computing device, which may be centralized on a single computing device or distributed over a network of multiple computing devices.
  • the device/function module/functional unit in the above embodiment When the device/function module/functional unit in the above embodiment is implemented in the form of a software function module and sold or used as a stand-alone product, it can be stored in a computer readable storage medium.
  • the above mentioned computer readable storage medium may be a read only memory, a magnetic disk or an optical disk or the like.
  • the embodiment of the present invention acquires the SKU attribute of each item of the item currently browsed by the current user, and matches the SKU attribute of each item with the pre-established user information of the current user to obtain a matching degree, thereby The item with the highest matching degree is recommended to the current user; in the embodiment of the present invention, the information of the product purchased by the user can be explicitly recommended according to the user information, thereby saving the user from having to make multiple specifications of the product in order to purchase the product.
  • the embodiment of the present invention avoids the cumbersome operation process when the user selects the product, and improves Shopping efficiency, while greatly improving the user shopping experience.

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Abstract

一种商品推荐方法及系统，其中，该方法包括：获取当前用户当前浏览的商品的每个单品的库存量单位SKU属性；将每个单品的SKU属性与预先建立的当前用户的用户信息进行匹配，获取匹配程度；将匹配程度最高的单品推荐给该当前用户。

Description

一种商品推荐方法及系统 技术领域
本申请涉及但不限于电商技术领域。
背景技术
互联网时代、尤其是移动互联网时代，网上购物已经成为越来越多人的首选购物方式。然而在纷繁芜杂的电商系统中，想要找出一款称心如意的宝贝是需要花费大量的时间和精力的，因此很多电商系统里有商品推荐系统，然而相关技术中的商品推荐系统多是基于商品种类做出的推荐，没有进一步根据商品的规格，例如尺码、颜色和型号等作出具体的推荐，而在实际的购物过程中，用户必须根据自己的喜欢和实际情况对商品的规格做出选择，例如有些商品需要同时选择其颜色、大小和型号等各种参数，也称为库存量单位(Stock Keeping Unit，简称为：SKU)，面对这么多参数的组合选择，用户有时候会因为各种原因，在没有找到合适的参数组合之前就匆匆离开了页面，导致用户与自己心仪的商品擦肩而过。
发明内容
以下是对本文详细描述的主题的概述。本概述并非是为了限制权利要求的保护范围。
本文提供了一种商品推荐方法及系统，以解决相关技术中的商品推荐系统无法根据商品的规格作出具体推荐的问题。
一种商品推荐方法，包括：
获取当前用户当前浏览的商品的每个单品的库存量单位SKU属性；
将所述每个单品的SKU属性与预先建立的所述当前用户的用户信息进行匹配，获取匹配程度；
将所述匹配程度最高的单品推荐给所述当前用户。
可选地，所述获取当前用户所浏览的商品的每个单品的库存量单位SKU 属性之前，所述方法还包括：
获取所述当前用户在电商系统中的购买记录；
对购买记录进行数据清洗，将清洗后的数据作为所述当前用户的用户信息并存储。
可选地，所述对购买记录进行数据清洗，包括：
获取所述购买记录中所购买的每个单品的SKU属性；
对所述购买记录中每个单品的SKU属性进行参数类别划分；
对相同类别的SKU属性出现的频率进行统计，将出现频率最高的SKU属性作为所述当前用户的用户信息。
可选地，所述获取所述当前用户在电商系统中的购买记录之前，所述方法还包括：
判断所述当前用户在所述电商系统中是否有购买记录；
所述获取所述当前用户在电商系统中的购买记录，包括：
当判断出所述当前用户在所述电商系统中有购买记录时，获取所述当前用户在所述电商系统中的所述购买记录；
当判断出所述当前用户在所述电商系统中没有购买记录时，获取所述当前用户在所述电商系统中的浏览记录；
所述对所述购买记录进行数据清洗，将清洗后的数据作为所述当前用户的用户信息并存储，包括：
对所述购买记录或浏览记录进行数据清洗，将清洗后的数据作为所述当前用户的用户信息并存储。
可选地，所述对所述浏览记录进行数据清洗，包括：
获取所述浏览记录中所浏览的每个单品的SKU属性；
对所述浏览记录中每个单品的SKU属性进行参数类别划分；
对相同类别的SKU属性出现的频率进行统计，将出现频率最高的SKU属性作为所述当前用户的用户信息。
可选地，所述将匹配程度最高的单品推荐给所述当前用户，包括：
选中所述匹配程度最高的单品，并提醒所述当前用户。
一种商品推荐系统，所述系统包括：
获取模块，设置为：获取当前用户当前浏览的商品的每个单品的库存量单位SKU属性；
匹配模块，设置为：将所述获取模块获取的所述每个单品的SKU属性与预先建立的所述当前用户的用户信息进行匹配，获取匹配程度；
推荐模块，设置为：将所述匹配模块获取的所述匹配程度最高的单品推荐给所述当前用户。
可选地，所述系统还包括据清洗模块；
其中，所述获取模块，还设置为：在所述获取模块获取当前用户所浏览的商品的每个单品的库存量单位SKU属性之前，获取所述当前用户在电商系统中的购买记录；
所述数据清洗模块，设置为：对所述获取模块获取的所述购买记录进行数据清洗，将清洗后的数据作为所述当前用户的用户信息并存储。
可选地，所述数据清洗模块包括：
获取单元，设置为：获取所述获取模块获取的所述购买记录中所购买的每个单品的SKU属性；
划分单元，设置为：对所述获取单元获取的所述购买记录中每个单品的SKU属性进行参数类别划分；
统计单元，设置为：对所述划分单元划分出的相同类别的SKU属性出现的频率进行统计，将出现频率最高的SKU属性作为所述当前用户的用户信息。
可选地，所述系统还包括：
判断模块，设置为：在所述获取模块获取所述当前用户在所述电商系统中的购买记录之前，判断所述当前用户在所述电商系统中是否有购买记录；
所述获取模块获取所述当前用户在电商系统中的购买记录，是设置为：
所述获取模块在所述判断模块判断出所述当前用户在所述电商系统中有 购买记录时，获取所述当前用户在所述电商系统中的所述购买记录；
所述获取模块在所述判断模块判断出所述当前用户在所述电商系统中没有购买记录时，获取所述当前用户在所述电商系统中的浏览记录；
所述数据清洗模块，是设置为：对所述获取模块获取的所述购买记录或所述浏览记录进行数据清洗，将清洗后的数据作为所述当前用户的用户信息并存储。
可选地，所述数据清洗模块包括：浏览数据清洗单元，设置为：对浏览记录进行数据清洗；
浏览数据清洗单元，包括：
获取子单元，设置为：获取所述获取模块获取的所述浏览记录中所浏览的每个单品的SKU属性；
划分子单元，设置为：对所述获取子单元获取的所述浏览记录中每个单品的SKU属性进行参数类别划分；
统计子单元，设置为：对所述划分子单元划分出的相同类别的SKU属性出现的频率进行统计，将出现频率最高的SKU属性作为所述当前用户的用户信息。
可选地，所述推荐模块是设置为：设置为：选中所述匹配程度最高的单品，并提醒所述当前用户。
本发明实施例提供的商品推荐方法及系统，通过获取当前用户当前浏览的商品的每个单品的SKU属性，将该每个单品的SKU属性与预先建立的该当前用户的用户信息进行匹配，以获取匹配程度，从而将匹配程度最高的单品推荐给当前用户；本发明实施例可以依据用户信息对用户所购买商品的规格作出信息明确的推荐，节省了用户在购买商品时需对商品的多个规格参数依次做出选择所需要的时间，且避免了由于用户在未找到合适的参数组合之前离开页面，导致用户错过自己心仪的商品的情况的发生；本发明实施例避免了用户选择商品时繁琐的操作过程，提高购物效率，同时极大的提升用户购物体验。
在阅读并理解了附图和详细描述后，可以明白其他方面。
附图概述
图1为本发明实施例提供的一种商品推荐方法的流程图；
图2为本发明实施例提供的另一种商品推荐方法的流程图
图3为本发明实施例提供的商品推荐方法中一种对购买记录进行数据清洗的流程图；
图4为本发明实施例提供的又一种商品推荐方法的流程图；
图5为本发明实施例提供的一种商品推荐系统的结构示意图；
图6为本发明实施例提供的另一种商品推荐系统的结构示意图；
图7为本发明实施例提供的商品推荐系统中一种数据清洗模块的结构示意图；
图8为本发明实施例提供的又一种商品推荐系统的结构示意图。
本发明的实施方式
下文中将结合附图对本发明的实施方式进行详细说明。需要说明的是，在不冲突的情况下，本文中的实施例及实施例中的特征可以相互任意组合。
在附图的流程图示出的步骤可以在诸根据一组计算机可执行指令的计算机系统中执行。并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
参见图1，为本发明实施例提供的一种商品推荐方法的流程图。本发明实施例针对相关技术中的问题，提供了一种商品推荐方法，图1所示流程包括步骤110～步骤130：
步骤110，获取当前用户当前浏览的商品的每个单品的库存量单位SKU属性；对于电商而言，SKU通常指一款商品的单品，每款单品都有一个SKU，便于电商品牌识别商品，例如一款商品多色，则是有多个SKU，颜色不同则SKU编码也不相同，如果同一款商品不同颜色的SKU编码相同，则会出现混淆，导致发错货。
步骤120，将每个单品的SKU属性与预先建立的当前用户的用户信息进行匹配，获取匹配程度；其中，分析当前用户所浏览商品的每个单品的SKU属性，并与用户信息中当前用户以往所购买商品的SKU属性相匹配。
步骤130，将匹配程度最高的单品推荐给当前用户。
在本实施例中，可选地，步骤103可以包括：
选中匹配程度最高的单品，并提醒当前用户。其中，选中匹配程度最高的单品，即在当前用户浏览某商品时，将该当前用户当前所浏览商品中与用户信息匹配程度最高的单品默认选中并推荐给该当前用户，这样就节省了用户对商品的多个规格参数依次做出选择的时间，避免了对多个规格参数依次进行选择的繁琐操作步骤，使购物过程变得简单，增强用户的购物体验；可选地，提醒当前用户的方式还可以以弹窗口的方式来实现。
可选地，图2为本发明实施例提供的另一种商品推荐方法的流程图。在上述图1所示实施例的基础上，在步骤110之前，本实施例提供的方法还可以包括以下步骤101～步骤102：
步骤101，获取当前用户在电商系统中的购买记录；其中，获取当前用户的购买记录目的是为了建立当前用户的用户信息，购买记录中包含了当前用户以往所购买商品的SKU属性。
步骤102，对购买记录进行数据清洗，将清洗后的数据作为当前用户的用户信息并存储。其中，对购买记录进行数据清洗主要是从购买记录中提取当前用户以往所购买商品的SKU属性。
可选地，如图3所示，为本发明实施例提供的商品推荐方法中一种对购买记录进行数据清洗的流程图。本实施例中对购买记录进行数据清洗的方式，可以包括以下步骤210～步骤230：
步骤210，获取购买记录中所购买的每个单品的SKU属性；
步骤220，对购买记录中每个单品的SKU属性进行参数类别划分；比如将SKU属性中，所有与颜色有关的属性划分为颜色，与尺寸有关的属性划分为尺寸等。
步骤230，对相同类别的SKU属性出现的频率进行统计，将出现频率最高 的SKU属性作为当前用户的用户信息。在相同类别的SKU属性中，再对具体的SKU属性统计出现的概率，依此来建立当前用户的用户信息。
可选地，图4为本发明实施例提供的又一种商品推荐方法的流程图。在上述图2所示实施例的基础上，在步骤101之前，本实施例提供的方法还可以包括步骤100：
步骤100，判断当前用户在电商系统中是否有购买记录；
相应地，本实施例中的步骤101，可以包括：
步骤1011，当判断出所述当前用户在所述电商系统中有购买记录时，获取当前用户在电商系统中的购买记录；
步骤1012，当判断出所述当前用户在所述电商系统中没有购买记录时，获取当前用户在电商系统中的浏览记录；也就是说，当用户首次在电商系统购物时，便从其浏览记录中采集数据。
本实施例在实际应用中，步骤102可以包括：对购买记录或浏览记录进行数据清洗，将清洗后的数据作为该用户的用户信息并存储。也就是说，当用户首次在电商系统购物时，从其浏览记录中采集数据后，对采集的数据进行数据清洗。
可选地，本发明实施例对浏览记录进行数据清洗的方式，可以包括：
获取浏览记录中所浏览的每个单品的SKU属性；
对浏览记录中每个单品的SKU属性进行参数类别划分；比如将SKU属性中，所有与颜色有关的属性划分为颜色，与尺寸有关的属性划分为尺寸等。
对相同类别的SKU属性出现的频率进行统计，将出现频率最高的SKU属性作为当前用户的用户信息；在相同类别的SKU属性中，再对具体的SKU属性统计出现的概率，依此来建立当前用户的信息。
本发明实施例提供的商品推荐方法，依据用户信息对当前用户所购买商品的规格作出信息明确的推荐，节省了用户在购买商品时需对商品的多个规格参数依次做出选择所需要的时间，且避免了由于用户在未找到合适的参数组合之前离开页面，导致用户错过自己心仪的商品的情况的发生；本发明实施例避免了用户选择商品时繁琐的操作过程，提高购物效率，同时极大的提 升用户购物体验。
为达上述目的，本发明实施例还提供了一种商品推荐系统，如图5所示，为本发明实施例提供的一种商品推荐系统的结构示意图，本实施例提供的商品推荐系统包括：
获取模块11，设置为：获取当前用户当前浏览的商品的每个单品的库存量单位SKU属性；
匹配模块12，设置为：将获取模块11获取的每个单品的SKU属性与预先建立的当前用户的用户信息进行匹配，获取匹配程度；
推荐模块13，设置为：将匹配模块12获取的匹配程度最高的单品推荐给当前用户。
可选地，如图6所示，为本发明实施例提供的另一种商品推荐系统的结构示意图。在上述图5所示系统的结构基础上，本实施例提供的系统还可以包括数据清洗模块14。
其中，本实施例中的获取模块11，还设置为：在该获取模块11获取当前用户所浏览的商品的每个单品的库存量单位SKU属性之前，获取当前用户在电商系统中的购买记录；
数据清洗模块14，设置为：对获取模块11获取的购买记录进行数据清洗，将清洗后的数据作为当前用户的用户信息并存储。
可选地，如图7所示，为本发明实施例提供的商品推荐系统中一种数据清洗模块的结构示意图。本实施例中的数据清洗模块14可以包括：
获取单元41，设置为：获取获取模块11获取的购买记录中所购买的每个单品的SKU属性；
划分单元42，设置为：对获取单元41获取的购买记录中每个单品的SKU属性进行参数类别划分；
统计单元43，设置为：对划分单元42划分出的相同类别的SKU属性出现的频率进行统计，将出现频率最高的SKU属性作为当前用户的用户信息。
可选地，如图8所示，为本发明实施例提供的又一种商品推荐系统的结构示意图。在上述图6所示系统的结构基础上，本实施例提供的系统还可以包括：
判断模块15，设置为：在获取模块11获取当前用户在电商系统中的购买记录之前，判断当前用户在电商系统中是否有购买记录；
相应地，获取模块11获取当前用户在电商系统中的购买记录，是设置为：
获取模块11在判断模块15判断出当前用户在电商系统中有购买记录时，获取当前用户在电商系统中的购买记录；
获取模块11在判断模块15判断出当前用户在电商系统中没有购买记录时，获取当前用户在电商系统中的浏览记录；
在本实施例中，数据清洗模块14是设置为：对获取模块11获取的购买记录或浏览记录进行数据清洗，将清洗后的数据作为当前用户的用户信息并存储。
可选地，本实施例中的数据清洗模块14可以包括：浏览数据清洗单元，设置为：对浏览记录进行数据清洗；
其中，浏览数据清洗单元，可以包括：
获取子单元，设置为：获取获取模块11获取的浏览记录中所浏览的每个单品的SKU属性；
划分子单元，设置为：对获取子单元获取的浏览记录中每个单品的SKU属性进行参数类别划分；
统计子单元，设置为：对划分子单元划分出的相同类别的SKU属性出现的频率进行统计，将出现频率最高的SKU属性作为当前用户的用户信息。
可选地，本实施例中的推荐模块是设置为：选中匹配程度最高的单品，并提醒当前用户。
在实际应用中，本发明实施例提供的商品推荐系统是应用上述方法的系统，即上述方法的所有实施例均适用于该装置，且均能达到相同或相似的有益效果。
本领域普通技术人员可以理解上述实施例的全部或部分步骤可以使用计算机程序流程来实现，所述计算机程序可以存储于一计算机可读存储介质中，所述计算机程序在相应的硬件平台上(根据系统、设备、装置、器件等)执行，在执行时，包括方法实施例的步骤之一或其组合。
可选地，上述实施例的全部或部分步骤也可以使用集成电路来实现，这些步骤可以被分别制作成一个个集成电路模块，或者将它们中的多个模块或步骤制作成单个集成电路模块来实现。
上述实施例中的装置/功能模块/功能单元可以采用通用的计算装置来实现，它们可以集中在单个的计算装置上，也可以分布在多个计算装置所组成的网络上。
上述实施例中的装置/功能模块/功能单元以软件功能模块的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。上述提到的计算机可读取存储介质可以是只读存储器，磁盘或光盘等。
工业实用性
本发明实施例通过获取当前用户当前浏览的商品的每个单品的SKU属性，将该每个单品的SKU属性与预先建立的该当前用户的用户信息进行匹配，以获取匹配程度，从而将匹配程度最高的单品推荐给当前用户；本发明实施例可以依据用户信息对用户所购买商品的规格作出信息明确的推荐，节省了用户在购买商品时需对商品的多个规格参数依次做出选择所需要的时间，且避免了由于用户在未找到合适的参数组合之前离开页面，导致用户错过自己心仪的商品的情况的发生；本发明实施例避免了用户选择商品时繁琐的操作过程，提高购物效率，同时极大的提升用户购物体验。

Claims (12)

  1. 一种商品推荐方法，包括：
    获取当前用户当前浏览的商品的每个单品的库存量单位SKU属性；
    将所述每个单品的SKU属性与预先建立的所述当前用户的用户信息进行匹配，获取匹配程度；
    将所述匹配程度最高的单品推荐给所述当前用户。
  2. 根据权利要求1所述的商品推荐方法，其中，所述获取当前用户所浏览的商品的每个单品的库存量单位SKU属性之前，所述方法还包括：
    获取所述当前用户在电商系统中的购买记录；
    对所述购买记录进行数据清洗，将清洗后的数据作为所述当前用户的用户信息并存储。
  3. 根据权利要求2所述的商品推荐方法，其中，所述对所述购买记录进行数据清洗，包括：
    获取所述购买记录中所购买的每个单品的SKU属性；
    对所述购买记录中每个单品的SKU属性进行参数类别划分；
    对相同类别的SKU属性出现的频率进行统计，将出现频率最高的SKU属性作为所述当前用户的用户信息。
  4. 根据权利要求2所述的商品推荐方法，其中，所述获取所述当前用户在电商系统中的购买记录之前，所述方法还包括：
    判断所述当前用户在所述电商系统中是否有购买记录；
    所述获取所述当前用户在电商系统中的购买记录，包括：
    当判断出所述当前用户在所述电商系统中有购买记录时，获取所述当前用户在所述电商系统中的所述购买记录；
    当判断出所述当前用户在所述电商系统中没有购买记录时，获取所述当前用户在所述电商系统中的浏览记录；
    所述对所述购买记录进行数据清洗，将清洗后的数据作为所述当前用户 的用户信息并存储，包括：
    对所述购买记录或所述浏览记录进行数据清洗，将清洗后的数据作为所述当前用户的用户信息并存储。
  5. 根据权利要求4所述的商品推荐方法，其中，所述对所述浏览记录进行数据清洗，包括：
    获取所述浏览记录中所浏览的每个单品的SKU属性；
    对所述浏览记录中每个单品的SKU属性进行参数类别划分；
    对相同类别的SKU属性出现的频率进行统计，将出现频率最高的SKU属性作为所述当前用户的用户信息。
  6. 根据权利要求1所述的商品推荐方法，其中，所述将所述匹配程度最高的单品推荐给所述当前用户，包括：
    选中所述匹配程度最高的单品，并提醒所述当前用户。
  7. 一种商品推荐系统，包括：
    获取模块，设置为：获取当前用户当前浏览的商品的每个单品的库存量单位SKU属性；
    匹配模块，设置为：将所述获取模块获取的所述每个单品的SKU属性与预先建立的所述当前用户的用户信息进行匹配，获取匹配程度；
    推荐模块，设置为：将所述匹配模块获取的所述匹配程度最高的单品推荐给所述当前用户。
  8. 根据权利要求7所述的商品推荐系统，所述系统还包括据清洗模块；
    其中，所述获取模块，还设置为：在所述获取模块获取当前用户所浏览的商品的每个单品的库存量单位SKU属性之前，获取所述当前用户在电商系统中的购买记录；
    所述数据清洗模块，设置为：对所述获取模块获取的所述购买记录进行数据清洗，将清洗后的数据作为所述当前用户的用户信息并存储。
  9. 根据权利要求8所述的商品推荐系统，其中，所述数据清洗模块包括：
    获取单元，设置为：获取所述获取模块获取的所述购买记录中所购买的 每个单品的SKU属性；
    划分单元，设置为：对所述获取单元获取的所述购买记录中每个单品的SKU属性进行参数类别划分；
    统计单元，设置为：对所述划分单元划分出的相同类别的SKU属性出现的频率进行统计，将出现频率最高的SKU属性作为所述当前用户的用户信息。
  10. 根据权利要求8所述的商品推荐系统，所述系统还包括：
    判断模块，设置为：在所述获取模块获取所述当前用户在所述电商系统中的购买记录之前，判断所述当前用户在所述电商系统中是否有购买记录；
    所述获取模块获取所述当前用户在电商系统中的购买记录，是设置为：
    所述获取模块在所述判断模块判断出所述当前用户在所述电商系统中有购买记录时，获取所述当前用户在所述电商系统中的所述购买记录；
    所述获取模块在所述判断模块判断出所述当前用户在所述电商系统中没有购买记录时，获取所述当前用户在所述电商系统中的浏览记录；
    所述数据清洗模块，是设置为：对所述获取模块获取的所述购买记录或所述浏览记录进行数据清洗，将清洗后的数据作为所述当前用户的用户信息并存储。
  11. 根据权利要求10所述的商品推荐系统，其中，所述数据清洗模块包括：浏览数据清洗单元，设置为：对所述浏览记录进行数据清洗；
    所述浏览数据清洗单元，包括：
    获取子单元，设置为：获取所述获取模块获取的所述浏览记录中所浏览的每个单品的SKU属性；
    划分子单元，设置为：对所述获取子单元获取的所述浏览记录中每个单品的SKU属性进行参数类别划分；
    统计子单元，设置为：对所述划分子单元划分出的相同类别的SKU属性出现的频率进行统计，将出现频率最高的SKU属性作为所述当前用户的用户信息。
  12. 根据权利要求7所述的商品推荐系统，其中，所述推荐模块是设置为：选中所述匹配程度最高的单品，并提醒所述当前用户。
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