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Personalized movie recommendation method based on self-adaptation orthogonal crossover multi-target optimization algorithm

A multi-objective optimization and recommendation method technology, applied in the field of multi-objective optimization algorithms and recommendation algorithms, can solve problems such as inability to optimize accuracy and inaccuracy, and avoid uneven distribution, uniform distribution, and diversity of individuals rich effect

Active Publication Date: 2018-06-12
BEIJING UNIV OF TECH
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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

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  • Personalized movie recommendation method based on self-adaptation orthogonal crossover multi-target optimization algorithm
  • Personalized movie recommendation method based on self-adaptation orthogonal crossover multi-target optimization algorithm
  • Personalized movie recommendation method based on self-adaptation orthogonal crossover multi-target optimization algorithm

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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] ...

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Abstract

The invention relates to a personalized movie recommendation method based on a self-adaptation orthogonal crossover multi-target optimization algorithm. The personalized movie recommendation method has the advantages that aiming at the optimization insufficiency of a traditional recommendation algorithm to accuracy and non-accuracy which are two opposite indexes, the multi-target optimization algorithm is used to optimize the two targets, and accordingly diversity is increased under the premise that the accuracy is kept; aiming at the defects of the NSGA-II multi-target optimization algorithm,the improved algorithm SMOCDE is provided, the algorithm designs a self-adaptation multi-target orthogonal crossover operator SMOC, and the operator is used to initialize population so as to avoid population distribution unevenness; the operator is used for perform crossover operation, and the convergence performance and distribution performance of population are kept; when the algorithm is applied to the actual problem of personalized movie recommendation, the universality and effectiveness of the algorithm are verified by testing and comparing the algorithm with an existing recommendation algorithm, and the accuracy and diversity of recommendation results are increased.

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...

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Application Information

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IPC IPC(8): G06F17/30G06N3/00
CPCG06N3/006G06F16/735G06F16/7867G06F16/9535
Inventor 杨新武郭西念王芊霓陈晓丹
Owner BEIJING UNIV OF TECH
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