Research And System Design Of Movie Recommendation Algorithm Based On User Interest

With the development of information technology,the problem of "information overload" is becoming more and more serious.The same thing is true for movie information.With the continuous expansion of the movie library,it is difficult for users to screen the movies they are interested in from the movie resource pool quickly and accurately.The recommendation system has alleviated the above problems to some extent.The collaborative filtering recommendation algorithm is widely used in the recommended movie areas and has significant effects,but it also has its shortcomings.For example,when the user's viewing score is sparse,the algorithm is difficult to mine users accurately.It is difficult to track the interest of the user when time shift.Therefore,the research and improvement of this method are of great significance for the film recommend areas.This paper improves on the basis of the algorithm as following:This paper proposes a collaborative filtering algorithm based on the project class and scoring reliability,introduces movie categories and user reliability,reduces data sparse impact,and improves recommendation accuracy.The main idea is to integrate user score preference,user movie category preference,assigns different fusion parameters to the two,obtain the optimal value by experiment,and then integrate the reliability of user neighbor score,and make the final movie score prediction and the movie recommendation.The time factor has a great influence on people's hobbies and interests.This paper proposes a collaborative filtering algorithm based on natural forgetting and user interest.The forgetting function is introduced to solve the problem of user's interest in watching offset,and the interest in watching after user offset is tracked in time.The main idea is to use the forgetting function to assign different weights to users' interest in different time periods.It increased the user's recent the interest in watching weight,calculated the user's viewing interest retention,and comprehensive user interest history behavior and calculation of interest retention to obtain the user's time-lapse.Watching interest,thereby recommending movies that may be of interest to users after their interest has been offset.The above-mentioned optimized and improved the recommendation algorithm as the core is implemented,a movie recommendation system based on user interest is constructed,the user's viewing interest is accurately mined,the user's viewing interest offset is tracked,and the movie of interest to the user is accurately recommended and the movie resource is solved.It is difficult to screen and other issues,and meet the needs of users to quickly and accurately screen their favorite movies from a large number of movie resources,saving user time and improving user experience.