Recommendation system and method for improving user role undifferentiated treatment and data sparseness

A user role and data sparse technology, applied in the field of personalized recommendation system for users, to improve the problems caused by data sparsity, improve recommendation efficiency, and improve recommendation accuracy

Active Publication Date: 2017-05-10
CHONGQING UNIV OF POSTS & TELECOMM
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  • Description
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  • Application Information

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Problems solved by technology

A recommendation system and method that can effectively improve the problem of data sparsity and

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  • Recommendation system and method for improving user role undifferentiated treatment and data sparseness
  • Recommendation system and method for improving user role undifferentiated treatment and data sparseness
  • Recommendation system and method for improving user role undifferentiated treatment and data sparseness

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Embodiment Construction

[0033] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0034] The technical scheme that the present invention solves the above-mentioned technical problem is,

[0035] Such as figure 1 Shown is the overall flow chart of the present invention. The process includes data source acquisition module, dynamic role division module, score prediction and recommendation module. The detailed implementation process of the present invention comprises following three steps:

[0036] S1: Get the data source. The acquisition of the data source can be downloaded through the open public API of the existing web-based social networking site or crawled the content of the webpage by using a web crawler tool. The data content includes user rating data and item data on items.

[0...

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Abstract

The invention claims to protect a recommendation system and method for improving user role undifferentiated treatment and data sparseness, and belongs to the fields of data mining and information retrieval. The method comprises the following steps: firstly, in order to solve the problem of user role undifferentiated treatment, a time-slicing method is used to divide scoring time zones based on user scoring density, and at the same time, an information entropy theory is introduced to measure user roles in the time zones and dynamically divide the user roles, achieving hierarchical processing of scoring data; and secondly, for the problem of data sparseness, a tensor decomposition scoring prediction model based on ''user-item-role'' is constructed, missing values of the data are processed with a CP decomposition method to obtain a scoring prediction result and generate a target user recommending list. The invention effectively improves the problem generated by data sparseness, and improves the recommendation efficiency.

Description

technical field [0001] The invention relates to the fields of data mining and information retrieval, in particular to a personalized recommendation system and method for users. Background technique [0002] With the continuous development of the Internet and the explosive growth of network information, the problem of information overload seriously affects people's daily life. In order to solve the problem of information overload, domestic and foreign experts and scholars continue to take new measures, such as optimizing search engines and strengthening recommendation systems. [0003] In recent years, recommendation systems have received more and more attention in the fields of e-commerce and social networks, especially the development of personalized recommendation technology, which has played an important role in enhancing user experience and improving service quality. Existing recommender systems are roughly divided into two categories: prediction and TopN recommendation...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q50/00
CPCG06F16/9535G06Q50/01
Inventor 肖云鹏刘晏驰邝瑶刘雨恬赵金哲李晓娟宋晨光张克毅孙华超
Owner CHONGQING UNIV OF POSTS & TELECOMM
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