Abstract
In order to improve the prediction precision of film score, the improved whale algorithm is established to predict the IMDB film score. Firstly, the IMDB scoring mechanism is studied, the reason of inaccurate scoring or network "water army" is analyzed, and the score of the meta score, the number of reviews and the popularity index are discussed. Data of the film in system is updated in real time. Secondly, the improved whale optimization algorithm is constructed, the mathematical model of encircling prey, bubble net attacking and search for prey are discussed. The adaptive weight formula is established and the improved adaptive updating formula is constructed, and the corresponding algorithm procedure is designed. Finally, through the analysis of visual results, the key factors affecting film scoring are found out, and suggestions are given for the film recommendation system. One hundred films are selected to carry out prediction analysis, the top 85% films are used as training samples, and the bottom 15% films are used as testing samples. The prediction mode is trained through training samples, and the testing samples are predicted based on trained prediction model. A prediction simulation analysis is carried out using IMDB samples, and results show that the proposed prediction model has high prediction precision.
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