0We want to recommend things people might like by looking at either similar users or similar items.
User-based Collaborative Filtering: 1. Find users similar to the target user. 2. Recommend items liked by those similar users. Item-based Collaborative Filtering: 1. Find items similar to the items the user liked. 2. Recommend those similar items to the user.
User-based looks at people like you.
Item-based looks at things like what you liked.
User-based: User A likes items 1, 2 User B likes items 2, 3 User C likes items 1, 3 To recommend for User A, find users like B or C and suggest their liked items.
Item-based: User A likes item 1 Find items similar to item 1 (like item 2) Recommend item 2 to User A.
This code shows how to compute user-based and item-based recommendations using cosine similarity on a small rating matrix.
import numpy as np from sklearn.metrics.pairwise import cosine_similarity # User-item rating matrix (rows: users, cols: items) ratings = np.array([ [5, 3, 0, 1], # User 0 [4, 0, 0, 1], # User 1 [1, 1, 0, 5], # User 2 [1, 0, 0, 4], # User 3 [0, 1, 5, 4], # User 4 ]) # User-based Collaborative Filtering user_sim = cosine_similarity(ratings) # Recommend for User 0 user_id = 0 similar_users = np.argsort(-user_sim[user_id]) # descending order print(f"User similarities for User {user_id}: {user_sim[user_id]}") print(f"Users sorted by similarity: {similar_users}") # Find items liked by similar users but not by User 0 user_0_items = ratings[user_id] > 0 recommendations = np.zeros(ratings.shape[1]) for other_user in similar_users[1:]: # skip self other_items = ratings[other_user] > 0 new_items = other_items & ~user_0_items recommendations += ratings[other_user] * new_items print(f"User-based recommendation scores for User {user_id}: {recommendations}") # Item-based Collaborative Filtering item_sim = cosine_similarity(ratings.T) # Items User 0 liked liked_items = np.where(user_0_items)[0] # Sum similarity scores for items similar to liked items item_recommendation_scores = np.zeros(ratings.shape[1]) for item in liked_items: item_recommendation_scores += item_sim[item] # Remove items already liked item_recommendation_scores[liked_items] = 0 print(f"Item similarities matrix:\n{item_sim}") print(f"Item-based recommendation scores for User {user_id}: {item_recommendation_scores}")
User-based can be slow if many users exist.
Item-based is often faster and more stable.
Both need enough data to find good similarities.
User-based recommends items liked by similar users.
Item-based recommends items similar to what the user liked.
Both help suggest new things in recommendation systems.
recommend_items = find_similar_items(user_liked_items) calls find_similar_items(user_liked_items), which fits item-based logic. Others do not.user_similarities = {'User1': 0.9, 'User2': 0.8}
user_likes = {'User1': ['ItemA', 'ItemB'], 'User2': ['ItemB', 'ItemC']}
recommend = set()
for user, sim in user_similarities.items():
if sim > 0.85:
recommend.update(user_likes[user])
print(sorted(recommend))user_liked = ['ItemA', 'ItemB']
def find_similar_items(items):
return ['ItemC', 'ItemD']
recommend = find_similar_items(user_liked)
print(recommend)
find_similar_items ignores its input and returns fixed items.Pass the test and this pattern moves unknown strong while earning +1 Grip toward your certificate.