Medium recommendation system coding problem

Design and implement a recommendation system function to recommend items to users that they might be interested in. Requirements: Input: user behavior history and item features. Output: a list of recommended items. Ensure bug-free code within the time limit and handle various edge cases. Sample Input user_behaviors = [(1, 2), (1, 3), (2, 1)] item_features = {1: [0.3, 0.6], 2: [0.1, 0.9], 3: [0.5, 0.4]} Sample Output recommended_items = [3, 2] Data Size Up to 1000 users and items each. Each item feature has up to 10 dimensions. Example Input {'user_behaviors': [(1, 2), (1, 3), (2, 1)], 'item_features': {1: [0.3, 0.6], 2: [0.1, 0.9], 3: [0.5, 0.4]}}