Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A Personalized Movie Recommendation Method Based on Multi-objective Optimization Algorithm Based on Adaptive Orthogonal Crossover

A multi-objective optimization and recommendation method technology, applied in the field of multi-objective optimization algorithm and recommendation algorithm, can solve the problems of inaccuracy and non-accuracy of optimization, so as to avoid uneven distribution of individuals, uniform and accurate population distribution The effect of sex and diversity

Active Publication Date: 2021-07-30
BEIJING UNIV OF TECH
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It is used to solve the problem that traditional recommendation algorithms cannot better optimize the two opposing goals of accuracy and non-accuracy

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
  • A Personalized Movie Recommendation Method Based on Multi-objective Optimization Algorithm Based on Adaptive Orthogonal Crossover
  • A Personalized Movie Recommendation Method Based on Multi-objective Optimization Algorithm Based on Adaptive Orthogonal Crossover
  • A Personalized Movie Recommendation Method Based on Multi-objective Optimization Algorithm Based on Adaptive Orthogonal Crossover

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0074] The present invention will be further described below in conjunction with the accompanying drawings and specific examples.

[0075] The present invention uses Movielens as a data set for movie recommendation, which includes 943 user information, 1682 movie information, and 100,000 user ratings for movies. SMOCDE, NSGA-II and traditional recommendation methods are based on user The collaborative filtering algorithm (UserCF) and the content-based recommendation algorithm (CB) are compared experimentally.

[0076] In the NSGA-II and SMOCDE two multi-objective optimization algorithms, the movie ID number is used as the gene bit, and each chromosome represents N movies. In the experiment, the value of N is (5, 10, 15, 20), and the running algebra gen= 100, the population size is set to pop size = 50, crossover probability p c =0.9, mutation probability p m =0.1, with accuracy and diversity as the two optimization objective functions, the formula is as follows:

[0077] ...

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 personalized movie recommendation method based on the multi-objective optimization algorithm of adaptive orthogonal crossover, in view of the lack of optimization of the two opposing indicators of accuracy and non-accuracy in the traditional recommendation algorithm, the multi-objective optimization algorithm is used to optimize these two objectives, This increases diversity while maintaining accuracy. Aiming at the shortcomings of the NSGA-II multi-objective optimization algorithm, an improved algorithm SMOCDE is proposed. This algorithm designs an adaptive multi-objective orthogonal crossover operator SMOC, which is used to initialize the population and avoid the uneven distribution of the population; The operator performs crossover operation to maintain the convergence and distribution of the population. This algorithm is applied to the practical problem of personalized movie recommendation. The generality and effectiveness of the algorithm are verified by testing and comparing with existing recommendation algorithms, and the accuracy and diversity of recommendation results are improved.

Description

technical field [0001] The invention belongs to the technical field of multi-objective optimization algorithm and recommendation algorithm. The improved multi-objective optimization algorithm SMOCDE (specifically involving adaptive orthogonal crossover initialization population, adaptive multi-objective orthogonal crossover operator) is used for personalized movie recommendation, in order to improve the performance of multi-objective optimization algorithm for personalized movie recommendation. Background technique [0002] With the development of Internet technology, the amount of information on the Internet is increasing exponentially, making it impossible for users to obtain useful information from them when faced with a large amount of information, and the efficiency of using information is reduced instead. This is the so-called information overload problem. . [0003] Personalized recommendation system is a very effective method to solve the problem of information over...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06F16/735G06F16/78G06N3/00
CPCG06N3/006G06F16/735G06F16/7867G06F16/9535
Inventor 杨新武郭西念王芊霓陈晓丹
Owner BEIJING UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com