A video recommendation method based on particle swarm algorithm

Abstract

The invention discloses a video recommending method based on a particle swarm algorithm. The method includes the steps of S1, determining the evaluation indexes of video files; S2, extracting the evaluation indexes of all video files; S3, randomly selecting three video files to serve as the initial swarm, and calculating the matching rate of the three video files; S4, calculating the speed vector of each video and updating; S5, using the updating formula xi(t+1)=xi(t)+vi(t+1) to update the position of each video file according to the current position and speed vector of each video file; S6, calculating dimensionality, and recommending the video file with the smallest dimensionality in a dimensionality table to a user. The method has the advantages that user preferences and video features are expressed through simple vector and weight manners, the similarity matching process is optimized through the modified particle swarm algorithm, and simple and fast video recommendation is achieved.

Description

technical field [0001] The present invention relates to a video recommendation method, more specifically, to a video recommendation method based on particle swarm optimization algorithm. Background technique [0002] With the increasing amount of information on the Internet, the problem of information overload occurs, and people need to spend a lot of time to find the information they need from the Internet. Although the search engine simplifies the information search process to a certain extent, due to its general nature, it cannot satisfy the differentiated query requests of different users. In order to solve this problem, recommendation system came into being. The recommendation system is divided into a general recommendation system and a personalized recommendation system. The general recommendation system does not recommend individual users, such as product sales rankings; the personalized recommendation system can provide customers with recommendation services that me...

AI Technical Summary

Problems solved by technology

This method can update and increase its own knowledge based on the continuous accumulation of user behavior data, but at the same time it also has its own flaws: when there is little or no user history data, the recommendation algorithm is poor in accuracy, and this algorithm will bring a lot of computational overhead

[0005] In order to achieve higher recommendation accuracy, the current video recommendation system mostly uses complex collaborative filtering algorithms, and the calculation of user characteristics, movie characteristics and similarity is complex, which will bring a lot of system overhead and delay

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