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Electronic commerce goods recommendation method based on large data multiple labels

A technology for e-commerce and product recommendation, which is applied in the field of e-commerce product recommendation to achieve the effect of satisfying the shopping experience and improving the click-through rate and popularity

Inactive Publication Date: 2016-12-07
广州泰沃技术有限公司
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Problems solved by technology

However, while Internet users enjoy convenient consumption, they also fall into an unprecedented embarrassing situation to a certain extent.

Method used

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Embodiment Construction

[0020] In order to make the technical means, creative features, objectives and effects of the present invention easy to understand, the present invention will be further explained below in conjunction with specific embodiments.

[0021] The present invention provides a technical solution: an e-commerce product recommendation method based on big data and multiple tags, including the following steps:

[0022] Step 1. User data collection: used to collect user browsing behaviors, and aggregate all user browsing data;

[0023] Step 2. Construction of purchasing behavior model: through the big data cloud computing platform, according to the user’s past historical browsing, purchase records and the corresponding relationship between the browsing time of different pages, establish training samples of purchasing behavior models with different user characteristic information, according to the purchase Behavior model training samples to establish a regression model between the user and the pur...

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Abstract

The invention provides an electronic commerce goods recommendation method based on large data multiple labels. The method comprises the steps of (1) collecting user data, (2) constructing a purchase behavior model, (3) analyzing the purchase tendency of the user through data, (4) pushing goods, (5) carrying out secondary collection of user data, through a large data cloud calculating platform, recording the click and purchase information of a user after pushing and the browse time information of different goods pages, and carrying out characteristic acquisition and data acquisition of the data, and (6) carrying out the secondary pushing of the goods, and pushing corresponding goods to the user according to the click and purchase information of the user after pushing and the browse time information of different goods pages. Compared with the prior art, the method has the advantages of user shopping can be guided, a user shopping experience is satisfied, and the method has an important effect in improving the clicking rate and popularity of an electronic commerce website.

Description

Technical field [0001] The invention is an e-commerce product recommendation method based on big data and multiple tags, and relates to the technical field of computer data processing. Background technique [0002] In recent years, with the continuous and rapid development of information technology and the Internet, e-commerce has become more and more prominent in society and life, and e-commerce systems provide users with more and more choices. At the same time, the rapid expansion of e-commerce has caused users to spend a lot of time browsing irrelevant products. For sellers, it is their urgent desire to recommend products to users in the most appropriate way. With the advent of the era of big data, the products of e-commerce websites are growing at an exponential rate, which is unimaginable in terms of quantity and variety, which makes it more difficult to quickly and accurately obtain the products you want. The Internet is like a double-edged sword, although to a large exten...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06Q30/02G06F17/30
CPCG06Q30/0201G06F16/9535G06F2216/03G06Q30/0255G06Q30/0277G06Q30/0631
Inventor 李易春
Owner 广州泰沃技术有限公司
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