Method for movie recommendation on basis of orthogonal and cluster pruning based improved multi-objective genetic algorithm

Abstract

The invention relates to a method for movie recommendation on the basis of an orthogonal and cluster pruning based improved multi-objective genetic algorithm. An improved algorithm OTNSGA-II is provided aiming at defects in distributivity and convergence of NSGA-II (non-dominated sorting genetic algorithm-II) and can be used for solving various multi-objective function optimization problems. By design of fault multi-objective orthogonal experiment initialization population, distributive deficiency caused by individual nonuniformity is avoided; by application of self-adaptive cluster pruning strategies, a population evolution process is maintained, and inferior individuals in an appropriate quantity are removed to keep convergence and distributivity of the population. By combination with information mining of user behaviors and movie properties, the algorithm is applied to solving of a practical problem of personalized movie recommendation, universality and effectiveness of the algorithm are explained by test comparison with existing algorithms, better recommendation results are obtained, recommendation accuracy rate, recall rate and coverage rate are increased, rich recommendation scheme combinations are provided, and interest points of users can be mined beneficially to provide more reliable recommendation services.

Description

technical field [0001] The invention belongs to the technical field of personalized recommendation. Using the improved multi-objective genetic algorithm OTNSGA-II algorithm for the shortcomings of NSGA-II algorithm (specifically involving NSGA-II algorithm, fault multi-objective orthogonal experiment and adaptive cluster pruning strategy) to realize the personalized recommendation of movies. Background technique [0002] With the popularization of Internet technology and the rapid development of modern e-commerce, the amount of resources flooding the Internet is increasing exponentially. The simultaneous presentation of a large amount of information often makes users feel at a loss, and it is difficult to find the resources they are really interested in, resulting in the so-called "information explosion" and "information overload" phenomena. Search engines and information retrieval technologies emerged to alleviate the problem of information overload. In today's informatio...

AI Technical Summary

Problems solved by technology

NSGA-II is the most widely used multi-objective evolutionary algorithm. The characteristic of this algorithm is to determine the individual fitness value according to the Pareto dominance relationship and density information among individuals. However, there are distribution and convergence maintenance in such a fitness calculation method. improper defect

Wen Shihua et al. improved NSGA-II by retaining some representative individuals based on the distance measurement method. This method only considered the impact of crowding distance on maintaining the distribution of the population, and did not fully consider the characteristics from the perspective of similar individuals. Convergence and lack of distribution problems caused by the existence of similar individuals and inferior individuals

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