Modeling recommendation method based on user behavior data fragmentation cluster

A technology of data sharding and recommendation method, applied in the Internet field, can solve the problems that are not conducive to improving the processing performance and recommendation accuracy of the recommendation system, and it is difficult to focus on the range of interest points of user behavior data.

Inactive Publication Date: 2017-01-18
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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AI Technical Summary

Problems solved by technology

If the recommendation system does not perform cluster analysis on the search keywords and their browsing data, it will be difficult to focus on the scope of

Method used

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  • Modeling recommendation method based on user behavior data fragmentation cluster
  • Modeling recommendation method based on user behavior data fragmentation cluster
  • Modeling recommendation method based on user behavior data fragmentation cluster

Examples

Experimental program
Comparison scheme
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Embodiment

[0073] 1. User behavior data collection

[0074] Unlike traditional personalized recommendation systems that only collect user purchase and rating data, this example also needs to collect user search behavior and browsing behavior data. Among them, each piece of behavior data collected not only needs to include information such as session ID, user ID, product ID, behavior type, and behavior content, but also needs to include attribute information such as time stamp, browsing terminal, and location. These basic attributes will provide support for the next step of user behavior data sharding. The specific collection process is as follows: Figure 6 shown.

[0075] Such as Figure 6 As shown, the user behavior data acquisition module first collects user login, retrieval, browsing, purchase, rating and other behavioral data from the user database, commodity database and log system of the e-commerce platform. Each behavior data contains basic attribute information (such as sess...

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Abstract

The invention relates to an internet personalized recommendation technology and particularly relates to a modeling recommendation method based on user behavior data fragmentation cluster. According to the modeling recommendation method, the user behavior data is subjected to fragmentation cluster treatment, a user dynamic interest model is established, so that the personalized recommendation is realized. Compared with the existing personalized recommendation method, the modeling recommendation method has the following differences: the existing personalized recommendation method only considers user interest dynamic time-varying characteristics, while the modeling recommendation method not only considers user interest time-varying characteristics, and also excavates multi-dimensional discrete interest points from behavior data, so that a user interest model is depicted more accurately. According to the modeling recommendation method, aiming at the multi-dimensional discrete interest theme of target users, the concurrence of interest points of users is preliminarily recommended, and finally, the weight, memory and preliminary recommendation result of the interest points of the target user are predicted and scored to be finally recommended, so that the accuracy and the processing capability of the personalized recommendation result are improved.

Description

technical field [0001] The invention relates to Internet technology, in particular to a modeling recommendation method based on user behavior data slice clustering. Background technique [0002] With the development of Internet applications, the problem of information overload is becoming more and more prominent in today's world. It is very difficult for users to find the information they are interested in from the massive amount of information. Personalized recommendation technology mines users' interests, preferences and potential needs by analyzing a large amount of user behavior data, and processes them through a personalized recommendation system to recommend services, commodities, or information content that users are interested in. At present, personalized recommendation technology has been widely used in e-commerce, social networking, location services, search services, advertising services and other fields. Among them, the most famous ones are e-commerce platforms...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q30/06G06K9/62
CPCG06F16/9535G06Q30/0631G06F18/2321
Inventor 陆鑫邓玉林
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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