Collaborative filtering recommendation method based on association rule prediction

A collaborative filtering recommendation and rule technology, applied in special data processing applications, instruments, business, etc., can solve the problems of large calculation amount and lower accuracy rate of association rules, so as to improve calculation accuracy, high readiness, and high recommendation quality Effect

Inactive Publication Date: 2014-02-19
ANHUI EDUCATION NETWORK PUBLISHING
View PDF3 Cites 24 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the calculation of association rules for mining item sets is relatively large, and th

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Collaborative filtering recommendation method based on association rule prediction
  • Collaborative filtering recommendation method based on association rule prediction
  • Collaborative filtering recommendation method based on association rule prediction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0018] see Figure 1-2 , the inventive method comprises the following steps:

[0019] 101 According to the user's search, browsing behavior and user's actual feedback, mine and obtain the user's rating data on the project (or product) on the website; specifically include:

[0020] 101-1 Extract the actual evaluation feedback score data of a specific registered user on the website after reading, purchasing or using a certain project (or product), and map the data to the user rating matrix;

[0021] 101-2 For items (or products) for which users have not actually given rating data, analyze and mine website log files to predict user rating data such as user search frequency and browsing time for the item.

[0022] 102. For items that cannot be mined or are not rated by users, predictions are made by mining the association rules between user features and item features; Ability to discover hidden connections;

[0023] The features of users and items are abstracted into two sets o...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a collaborative filtering recommendation method based on association rule prediction, and relates to the field of personalized recommendation of the Internet. According to the feedback of searching and browsing behaviors of users, item coring data by the users are mined and obtained; the items which can not be mined are predicted by mining association rules between user characteristics and item characteristics; a scoring preference matrix of the users is constructed through the association rules between the users and the items; according to the preference matrix of the users, a sparse scoring preference matrix of the users is predicted; a traditional similarity measuring method is improved, the similarity between the user ui and the user uj is calculated, the most adjacent user BNS of a target user is obtained through the user similarity method, and recommended scores are obtained finally through the BNS; the collaborative filtering recommendation method based on association rule prediction is evaluated. According to the method, the accuracy for calculating the similarity between the users is effectively improved, high recommending quality is kept under the condition of sparse data, and the recommending quality for new users is also high in readiness.

Description

Technical field [0001] The invention relates to the field of Internet personalized recommendation, in particular to a collaborative filtering recommendation method based on association rules. Background technique [0002] Information overload is an important feature and main challenge of Internet applications. A large amount of information is released to the Internet every day, making traditional search technologies unable to meet users' needs for information discovery. The emergence of recommendation systems can better help users discover and obtain more information. Information tailored to individual needs. Personalized recommendation is based on user characteristics and personal preferences. Its main task is to push content of interest to target users from massive amounts of information. With the development of Internet technology and e-commerce, Amazon, Taobao, eBay, etc. have adopted different forms According to the requirements of different recommendation systems, res...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/30G06Q30/00
CPCG06F16/9535
Inventor 吴雷阮怀伟昌磊
Owner ANHUI EDUCATION NETWORK PUBLISHING
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap