Design And Implementation Of A Movie Recommender System

With the rapidly growing popularity of the Internet and mobile Internet, the numberof movie entertainment information on the network has been extremely substantial, moreand more people get interested in movies and entertainment information, personalizedmovie recommendation system has become a hot. However, movie informationrepresentation is quite complex, the existing similarity calculation method recommendedalgorithms have their own advantages, resulting in a single similarity calculation methodrecommended algorithm can not be properly applied to movie recommendation system.Management and operation of a large number of movie data has become increasinglycomplex with the growth of the amount of data, therefore, how to integrate the advantagesof various algorithms to generate reliably movie recommendation results and ensure thatusers can access to the correct recommended data become important issues need to beaddressed in the recommended system design.System module is recommended to use the more realistic movie similarity calculationmethod, combined with collaborative filtering algorithm and content-based filteringalgorithms to effectively solve the problem of the accuracy problem and cold startproblems of the system, recommended algorithm uses mean-centering method to predictuser ratings of movies, to avoid the adverse effects of the user’s personal rating habitsprediction score. Important component such as dataset, recommendation engine, ratedpredictor similarity calculator are of a high degree of separation, with providing a varietyof effective implementation algorithm derived class. In the operator module in the systemis divided into two parts, the test environment and formal environment, the operator testthe system well in the test environment and then synchronize to a formal environment,using the new synchronization algorithm with error detection algorithm to improve theaccuracy and efficiency of the system.The scalability of the recommendation module is very strong, so you can selectsuitable score predicted similarity calculator depending on the data set, you can use theclassic algorithm which comes with the system or you can also define customrecommendation algorithm, this can makes recommendation result accuracy greatly increased. In the operator system module with the synchronization algorithm with errordetection algorithm operators greatly improve the accuracy and efficiency of data using thedatabase module operators.