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Big data processing method of recommended system

A technology for big data processing and recommendation systems, applied in the field of recommendation systems, can solve problems such as difficult development and low efficiency, achieve algorithm optimization, solve insufficient memory, and improve computing efficiency

Inactive Publication Date: 2016-11-16
COMMUNICATION UNIVERSITY OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a big data processing method for a recommendation system to solve the technical problems that the traditional big data processing method for a recommendation system is difficult to develop and has low efficiency

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  • Big data processing method of recommended system
  • Big data processing method of recommended system
  • Big data processing method of recommended system

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Embodiment Construction

[0063] The implementation of the present invention will be described in detail below in conjunction with the accompanying drawings and examples, so as to fully understand and implement the process of how to apply technical means to solve technical problems and achieve technical effects in the present invention. It should be noted that, as long as there is no conflict, each embodiment and each feature in each embodiment of the present invention can be combined with each other, and the formed technical solutions are all within the protection scope of the present invention.

[0064] An embodiment of the present invention provides a big data processing method for a recommendation system, which is implemented based on the big data processing platform Hadoop, including a big data processing design scheme based on content recommendation, a big data processing design scheme based on item collaborative recommendation, and cold start optimization The big data processing design of the sch...

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Abstract

The invention discloses a dig data processing method of a recommended system, pertaining to the technical field of the recommended system. Therefore, technical problems such as great development difficulty and poor efficiency of a conventional dig data processing method of a recommended system are solved. The method comprises following steps: obtaining users' preference efficiency matrixes and item data; obtaining item lists evaluated by users in efficiency matrixes by distributive data processing based on efficiency matrixes as well as evaluation users corresponding to all items and scoring value lists and obtaining similar item lists of each item based on item data by distributive data processing; obtaining recommended items and prediction weight lists of users based on evaluation users corresponding to all items and scoring value lists as well as similar item lists of all items; and obtaining item recommended results of users based on item lists of users' evaluation and recommended items of users as well as prediction weight lists by distributive data processing.

Description

technical field [0001] The present invention relates to the technical field of recommendation systems, in particular to a big data processing method for recommendation systems. Background technique [0002] The recommendation system, that is, personalized recommendation, its fundamental essence is to recommend products or project information that the user is interested in based on the user's interest characteristics and historical behavior records. With the continuous expansion of the scale of the Internet and the ubiquity of the mobile Internet, the information that users display on the Internet is also developing in diversification, from basic demographic information to dynamically changing geographic location information, from simple content browsers to content users. provider. Individuals in reality will also become real "virtual people" on the Internet. How to effectively analyze a large amount of unstructured heterogeneous information of users and obtain reliable and...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/35G06F16/9535
Inventor 杨成李晨李星
Owner COMMUNICATION UNIVERSITY OF CHINA