Improved NSGA-II based individuation film recommendation method

A recommendation method, film technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of not being able to take into account accuracy and diversity, and achieve the goal of maintaining convergence, maintaining distribution, and high accuracy Effect

Active Publication Date: 2017-09-26
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 balance accuracy and diversity

Method used

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  • Improved NSGA-II based individuation film recommendation method
  • Improved NSGA-II based individuation film recommendation method
  • Improved NSGA-II based individuation film recommendation method

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Embodiment Construction

[0050] The present invention will be further described below in conjunction with the accompanying drawings and specific examples.

[0051] 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. FFNSGA-II and 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.

[0052] In the NSGA-II and FFNSGA-II 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 number of runs gen=100, the population size is set to pop size = 50, crossover probability p c =0.9, mutation probability p m =0.1, taking accuracy and diversity as the two optimization objective functions, the formula is as follows:

[0053]...

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Abstract

The invention discloses an improved NSGA-II based individuation film recommendation method. In order to overcome the defect that a traditional recommendation algorithm only has accuracy and does not have diversity, a multi-objective optimization algorithm is adopted for optimizing two objectives, and therefore the diversity is improved on the condition that the accuracy is kept. In order to overcome the defects existing in the NSGA-II multi-objective optimization algorithm, the improved algorithm FFNSGA-II is put forward, the comprehensive relative entropy is designed for filtering initialized population, and population maldistribution is avoided; the self-adaptation noninferior solution filling is applied for maintaining the population evolution process, and the population astringency and distributivity are kept. On the basis of combination of user behaviors and film attribute information mining, the algorithm is applied to the actual individuation film recommendation, the universality and effectiveness of the algorithm are illustrated through testing comparison of the improved NSGA-II based individuation film recommendation method and an existing recommendation method, the better recommendation result is obtained, and the recommendation accuracy and diversity are improved.

Description

technical field [0001] The invention belongs to the technical field of evolutionary algorithm and personalized recommendation. Using the improved multi-objective genetic algorithm FFNSGA-II (specifically involving comprehensive relative entropy filtering initialization population, adaptive non-inferior solution filling) for personalized movie recommendation, in order to improve the performance of multi-objective genetic algorithm for personalized movie recommendation. Background technique [0002] With the advent of the era of big data, the explosive growth of data makes Internet users unable to obtain the part of information that is really useful to 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. question. [0003] Recommender system is a very promising method to solve the problem of information overload. The recommendation system analyzes the user's interests and h...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/735
Inventor 杨新武郭西念赵崇
Owner BEIJING UNIV OF TECH
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