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E-commerce website real-time recommending system and method under big data

An e-commerce website, real-time recommendation technology, applied in the network field, can solve the problems of low transaction conversion rate of recommendation satisfaction, low training efficiency, and lack of improvement.

Inactive Publication Date: 2017-01-04
SHANGHAI MARITIME UNIVERSITY
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AI Technical Summary

Problems solved by technology

[0004] After entering the era of Web 2.0, there are more and more demands for real-time recommendation, while traditional recommendation systems based on Hadoop regularly analyze data, update the model, and then use the new model for personalized recommendation and training. Inefficiency, and because there is no perfect mechanism to cooperate with real-time user feedback, there are problems of recommendation satisfaction and low transaction conversion rate

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  • E-commerce website real-time recommending system and method under big data
  • E-commerce website real-time recommending system and method under big data
  • E-commerce website real-time recommending system and method under big data

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Embodiment Construction

[0066] Specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0067] The present invention provides a method for real-time recommendation of an e-commerce website in a big data environment, the method comprising the following steps:

[0068] Step 1. First, use the implicit behavior log information of e-commerce website users to train the offline recommendation model.

[0069] Offline recommendation model training includes: collecting a large amount of user implicit behavior information from e-commerce websites to form a user implicit behavior matrix, and training the best offline recommendation model through the decomposition of the user implicit behavior matrix

[0070] Step 2. Collect user implicit behavior log information online, and use distributed storage technology and distributed stream processing technology to quickly process the collected massive user implicit behavior log information.

[0071] T...

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Abstract

The invention discloses an e-commerce website real-time recommending method under big data. The method includes the following steps: using user's implicit behavior log information on e-commerce websites to train an offline recommendation model; online acquiring user's implicit behavior log information, and using distributed-storage technology and distributed-streaming technology to rapidly process user's bulk implicit behavior log information; combining the well-trained offline recommending model and user's latest implicit behavior log information that undergo the distributed-streaming and providing the latest commodity recommendation list to the user. According to the invention, the method can real-time analyze user's behaviors under big data and provide real-time recommendation and feedback, and increases user's satisfaction and transaction transfer rate of e-commence websites.

Description

technical field [0001] The invention relates to the field of network technology, in particular to a real-time recommendation system and method for an e-commerce website in a big data environment. Background technique [0002] Taobao, the largest e-commerce platform in China, has 60 million daily visitors and more than 800 million daily online products. Facing the rapidly growing data scale, users are facing the "information overload problem". Without the help of auxiliary technologies such as search engines, recommendation systems, or information classification, it is a difficult task for users to find the information they are really interested in from massive Internet resources. This is a very difficult thing, which reduces the effective utilization of information. Search engines and personalized recommendation systems are two means to solve the "information overload" problem. Search engines, such as Google, Baidu, and Bing, feed back the results of user queries based on ...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06F17/30
CPCG06Q30/0201G06F16/9535
Inventor 岑凯伦韩志德毕坤王军
Owner SHANGHAI MARITIME UNIVERSITY
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