Movie Recommendation to Friends Using Whale Optimization Algorithm

Background: Recent advances in the World Wide Web and semantic networks have amplified social networking platforms, where the users share their photos, hobbies, location, interests, and experiences such as movie or restaurants. Social media platforms such as Facebook, twitter and LinkedIn are used to recommend the users the things of their interests such as movie, food, locations and friends. Objective: A novel method for the recommendation of movies to a friend using whale optimization has been introduced. Ratings given by friends of various movies are employed to recommend movies. Methods: Different evolutionary-based optimization methods have been applied for movie recommendation. The proposed method has been tested on movie-lense dataset and results are compared with 5 other methods namely, K-means, PCA K- means, SOM, PCA-SOM, PSO and ABC in terms of mean absolute error, precision and recall. Results: The experimental results demonstrate that proposed method outperformed all considered methods for 88.5% clusters centers in terms of precision, recall and mean absolute error. Conclusion: A novel recommendation system based on users rating has been designed to recommend movies to friends. It leverages the strengths of whale optimization to find the optimal solution.