Mobile Web service recommendation method and collaborative recommendation system based on user behavior analysis

A behavior analysis and business recommendation technology, applied in the field of recommendation, can solve the problems of sample noise, inability to provide users with business experience, and low prediction accuracy.

Active Publication Date: 2017-02-01
NANJING UNIV OF POSTS & TELECOMM
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

However, there are still some problems. This type of method generally weights and fuses the prediction results of all associated users, which will inevit

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  • Mobile Web service recommendation method and collaborative recommendation system based on user behavior analysis
  • Mobile Web service recommendation method and collaborative recommendation system based on user behavior analysis
  • Mobile Web service recommendation method and collaborative recommendation system based on user behavior analysis

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Embodiment Construction

[0045] Below in conjunction with accompanying drawing and specific embodiment the technical solution of the present invention is described in further detail:

[0046] The present invention provides a mobile Web service recommendation method based on user behavior analysis, specifically as follows:

[0047] First, design the Web feature vector, extract the URL information of the web page, and establish a mapping table between the Web URL and the Web feature vector;

[0048] 1.1, analyze the domain name of the Web URL, extract the first and second feature values ​​corresponding to the Web, where the first feature value is the name of the enterprise or organization, and the second feature value is the business type; the resource path of the Web URL Analyze the name and extract the third eigenvalue corresponding to the Web, the third eigenvalue is the specific business form under the business type; thus obtain the Web eigenvector, and its elements are the first to third eigenvalue...

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Abstract

The invention discloses a mobile Web service recommendation method and a collaborative recommendation system based on user behavior analysis. The method comprises the steps of first constructing a Web service prediction model, and searching two optimal associated users who are the most similar to the a target user in long-term habit and short-term mood through intelligent terminal side user browsing data and wearable device side user physiological data. Therefore, data of the two optimal associated users are used to enrich a sample library of the target user prediction model, so as to achieve the introduction of a sample enrichment mechanism with the minimized noise. A Web resource storage mechanism based on a feature vector is designed, and according to a model prediction result, accurate recommendation of the mobile Web service is realized. According to the scheme of the invention, through the interactive cooperation between an intelligent terminal and a wearable device, application reconfiguration is performed on the wearable device side physiological data, and a user behavior is analyzed from multiple angles of view and multiple dimensions, so that accurate prediction and recommendation of services are realized, thereby improving user experience.

Description

technical field [0001] The invention relates to a mobile Web service recommendation method and a collaborative recommendation system based on user behavior analysis, and belongs to the technical field of recommendation. Background technique [0002] In response to the explosive growth trend of mobile Internet services, the service recommendation engine, as an effective tool to solve the problems of user information overload and information confusion, can filter interested services for users from massive information by acquiring and predicting the potential preferences of mobile users, and Realize service pre-download, achieve the effect of balancing network load and reducing service delay. The premise of making accurate predictions for business recommendation engines requires a large number of user behavior samples as support. However, the number of samples limited to a single user is insufficient and the types are limited, resulting in low accuracy of business predictions a...

Claims

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Application Information

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IPC IPC(8): H04L29/08H04L12/24
CPCH04L41/145H04L41/147H04L67/02H04L67/51
Inventor 张晖毛小旺刘宝
Owner NANJING UNIV OF POSTS & TELECOMM
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