Top K Movie Recommendations

The problem is about recommending the top K movies. Assume you have a database of movies with ratings and user preferences. Write an algorithm to recommend the top K suitable movies, optimizing for performance with BFS and heaps. Input: A list of movie ratings, each containing a movie ID, rating, and genre. A list of user-preferred genres. An integer K indicating the number of movies to return. Output: A list containing only the top K recommended movie IDs. Constraints: If multiple movies have the same rating, any order of return is fine. The dataset may contain up to 10^6 movies. Test case: recommend_movies([{'id': 1, 'rating': 9.0, 'genre': 'action'}, {'id': 2, 'rating': 8.5, 'genre': 'action'}, {'id': 3, 'rating': 8.5, 'genre': 'comedy'}], ['action', 'comedy'], 2) # Output: [1, 2] or [2, 1] Example Input [{'id': 1, 'rating': 9.0, 'genre': 'action'}, {'id': 2, 'rating': 8.5, 'genre': 'action'}, {'id': 3, 'rating': 8.5, 'genre': 'comedy'}] ['action', 'comedy'] 2