Commodity recommending method and system based on user behaviors

A product recommendation and behavior technology, applied in business, data processing applications, special data processing applications, etc., can solve problems such as sparse products, low recommendation accuracy, and failure to meet application requirements, and achieve high recommendation accuracy and user experience. good effect

Inactive Publication Date: 2017-04-26
WUHAN FIBERHOME INFORMATION INTEGRATION TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in actual operation, the rating information of products is not easy to obtain and is very sparse compared to the number of products. Therefore, recommending products based solely on user ratings for products has low accuracy and does not conform to Practical application requirements, thus making the user experience poor

Method used

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  • Commodity recommending method and system based on user behaviors
  • Commodity recommending method and system based on user behaviors

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Embodiment Construction

[0019] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0020] see figure 1 As shown, the embodiment of the present invention provides a product recommendation method based on user behavior, including the following steps:

[0021] Step S1: Collect user behavior through APP (Application, application program) or webpage, and the user behavior includes: search behavior, click behavior, behavior of viewing commodity attributes, purchase behavior, evaluation behavior of commodity and scoring behavior, go to step S2.

[0022] Step S2: When the user purchases a product, determine whether the currently purchased product is a durable product or a consumable product. If it is a durable product, go to step S3; if it is a consumable product, go to step S4.

[0023] Step S3: Perform clustering of items according to the click behavior and search behavior in the collected user behaviors; recommend comm...

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Abstract

The invention discloses a commodity recommending method and system based on user behaviors, which relate to commodity recommending technology field. The method comprises the following steps: searching and collecting the user behaviors through an APP or a page; when the user purchases a commodity, determining whether the commodity is an endurable commodity or a consumable commodity; if the commodity is an endurable one, then clustering the items according to the clicking behaviors and the searching behaviors and recommending to the user the commodity; if the commodity is a consumable commodity, determining whether there is any scoring behavior for the commodity; if there is any, according to the scoring behavior, recommending to the user the commodity; if there is no such scoring behavior, transmitting the clicking behavior, the commodity attribute inquiring behavior, the purchase behavior and the commodity evaluating behavior into corresponding hidden score evaluation data; subjecting all the hidden score evaluation data to a layer analyzing method for weighted process so as to obtain a total hidden score; and based on the total hidden score, using a collaborative filtering algorithm to recommend the commodity to the user. According to the invention, not only high recommending efficiency can be achieved; but also the actual application requirement can be met, therefore, bringing excellent user experience to the user.

Description

technical field [0001] The present invention relates to the technical field of commodity recommendation, in particular to a method and system for commodity recommendation based on user behavior. Background technique [0002] With the continuous expansion of the scale of e-commerce, the number and types of goods are increasing rapidly, and customers are trapped in a large amount of information, and it takes a lot of time to find the goods they want to buy. This process of browsing a large amount of irrelevant information and products will undoubtedly lead to the continuous loss of consumers submerged in the problem of information overload, making it difficult to make purchase decisions quickly and effectively. In order to solve these problems, personalized recommendation system came into being. Personalized recommendation is to recommend information and products that users are interested in according to the user's interest characteristics and purchase behavior; it is an adva...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06F17/30
CPCG06Q30/0631G06F16/9535
Inventor 田松陈睿
Owner WUHAN FIBERHOME INFORMATION INTEGRATION TECH CO LTD
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