# Example video metadata videos = { "video1": [1, 0, 1], # Tags or categories "video2": [0, 1, 1], "video3": [1, 1, 0], } Reporteros 1 Pdf
def recommend(video, num_recommendations=2): similarities = [] for v in videos: if v != video: similarity = cosine_similarity([videos[video]], [videos[v]]) similarities.append((v, similarity[0][0])) similarities.sort(key=lambda x: x[1], reverse=True) return similarities[:num_recommendations] One Pc: Hdking
import numpy as np from sklearn.metrics.pairwise import cosine_similarity