Collaborative filtering in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a user-item rating matrix using pandas.

ML Python

import pandas as pd
ratings = pd.DataFrame({
    'user_id': [1, 2, 1, 3],
    'item_id': [101, 101, 102, 103],
    'rating': [5, 4, 3, 2]
})
rating_matrix = ratings.pivot(index='user_id', columns=[1], values='rating')

Drag options to blanks, or click blank then click option'

A'user_id'

B'rating'

C'score'

D'item_id'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'rating' instead of 'item_id' for columns in pivot.

Using 'user_id' for columns which is already the index.

The pivot function needs the column name that represents items to create the user-item matrix. Here, 'item_id' is the correct column.

2fill in blank

medium

Complete the code to calculate cosine similarity between users using sklearn.

ML Python

from sklearn.metrics.pairwise import [1]
import numpy as np
user_ratings = np.array([[5, 3, 0], [4, 0, 0], [1, 1, 0]])
similarity = [1](user_ratings)

Drag options to blanks, or click blank then click option'

Apairwise_distances

Beuclidean_distances

Ccosine_similarity

Dmanhattan_distances

Attempts:

3 left

💡 Hint

Common Mistakes

Using distance functions instead of similarity.

Not importing the correct function from sklearn.

Cosine similarity measures how similar two users are based on their rating vectors. sklearn's cosine_similarity function computes this.

3fill in blank

hard

Fix the error in the code to predict a user's rating for an item using weighted average of neighbors' ratings.

ML Python

import numpy as np
user_similarities = np.array([0.9, 0.8, 0.4])
neighbor_ratings = np.array([5, 3, 4])
predicted_rating = np.sum(user_similarities * [1]) / np.sum(user_similarities)

Drag options to blanks, or click blank then click option'

Aneighbor_ratings

Buser_similarities

Cpredicted_rating

Dnp.mean(neighbor_ratings)

Attempts:

3 left

💡 Hint

Common Mistakes

Multiplying similarities by themselves instead of ratings.

Using mean instead of weighted average.

To predict the rating, multiply each neighbor's rating by their similarity and sum these, then divide by sum of similarities.

4fill in blank

hard

Fill both blanks to create a dictionary of item recommendations for a user based on predicted ratings above 3.

ML Python

predicted_ratings = {'item1': 4.5, 'item2': 2.0, 'item3': 3.5, 'item4': 1.0}
recommendations = {item: rating for item, rating in predicted_ratings.items() if rating [1] [2]

Drag options to blanks, or click blank then click option'

A>

B3

C<

D4

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' causing wrong filtering.

Using wrong threshold number.

We want to keep items with ratings greater than 3, so use '>' and 3 in the condition.

5fill in blank

hard

Fill all three blanks to compute user similarity matrix and get top 2 similar users for user 0.

ML Python

from sklearn.metrics.pairwise import [1]
import numpy as np
ratings = np.array([[5, 3, 0], [4, 0, 0], [1, 1, 0], [5, 0, 4]])
sim_matrix = [1](ratings)
top_users = sim_matrix[0].argsort()[::-1][[3]:[2]]

Drag options to blanks, or click blank then click option'

Acosine_similarity

B1

C3

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong similarity function.

Incorrect slicing indices for top users.

Use cosine_similarity to compute similarity matrix. argsort()[::-1] sorts descending. To get top 2 excluding self (index 0), slice from 1 to 3.

Practice

1. What is the main idea behind collaborative filtering in recommendation systems?

easy

A. It recommends items based on the user's location.

B. It recommends items based on similar users' preferences.

C. It randomly suggests items without user data.

D. It uses item descriptions to recommend similar items.

Solution

1. Step 1: Understand collaborative filtering concept

   Collaborative filtering finds users with similar tastes and recommends items they liked.

2. Step 2: Compare options to definition

   Only It recommends items based on similar users' preferences. matches this idea; others describe different methods or unrelated ideas.

3. Final Answer:

   It recommends items based on similar users' preferences. -> Option B

4. Quick Check:

   Collaborative filtering = similar users' preferences [OK]

Hint: Focus on user similarity for collaborative filtering [OK]

Common Mistakes:

• Confusing collaborative filtering with content-based filtering
• Thinking it uses item descriptions only
• Assuming it recommends randomly

2. Which of the following is the correct way to compute cosine similarity between two user vectors u and v in Python using NumPy?

easy

A. np.dot(u, v) * (np.linalg.norm(u) + np.linalg.norm(v))

B. np.dot(u, v) * (np.linalg.norm(u) * np.linalg.norm(v))

C. np.dot(u, v) / (np.linalg.norm(u) + np.linalg.norm(v))

D. np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v))

Solution

1. Step 1: Recall cosine similarity formula

   Cosine similarity = dot product of vectors divided by product of their norms.

2. Step 2: Match formula to code

   np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v)) correctly implements this formula using NumPy functions.

3. Final Answer:

   np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v)) -> Option D

4. Quick Check:

   Cosine similarity formula = np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v)) [OK]

Hint: Cosine similarity = dot product ÷ product of norms [OK]

Common Mistakes:

• Adding norms instead of multiplying
• Multiplying dot product by norms
• Dividing by sum of norms

3. Given the user-item rating matrix below, what is the predicted rating for User 1 on Item C using user-based collaborative filtering with cosine similarity?

User 1: [5, 3, ?]
User 2: [4, 2, 1]
User 3: [1, 5, 4]

Assume similarity weights are:
User 1 & User 2: 0.9
User 1 & User 3: 0.1

Use weighted average: (0.9*1 + 0.1*4) / (0.9 + 0.1)

medium

A. 2.5

B. 1.9

C. 1.3

D. 3.0

Solution

1. Step 1: Calculate weighted sum of ratings

   Weighted sum = (0.9 * 1) + (0.1 * 4) = 0.9 + 0.4 = 1.3

2. Step 2: Calculate sum of weights and divide

   Sum weights = 0.9 + 0.1 = 1.0; Predicted rating = 1.3 / 1.0 = 1.3

3. Final Answer:

   1.3 -> Option C

4. Quick Check:

   Weighted average = 1.3 [OK]

Hint: Multiply ratings by similarity, sum, then divide by total similarity [OK]

Common Mistakes:

• Forgetting to divide by sum of weights
• Mixing up user similarities
• Using unweighted average

4. You have this Python code snippet for item-based collaborative filtering similarity calculation:

import numpy as np

item1 = np.array([5, 3, 0])
item2 = np.array([4, 0, 1])
similarity = np.dot(item1, item2) / (np.linalg.norm(item1) + np.linalg.norm(item2))
print(similarity)

What is the error in this code?

medium

A. Using addition instead of multiplication in denominator for cosine similarity.

B. Using np.dot instead of np.cross for similarity.

C. Missing normalization of item vectors before dot product.

D. Using zero values in vectors causes division by zero.

Solution

1. Step 1: Review cosine similarity formula

   Cosine similarity divides dot product by product of norms, not sum.

2. Step 2: Identify code mistake

   Code uses addition in denominator instead of multiplication, causing wrong similarity value.

3. Final Answer:

   Using addition instead of multiplication in denominator for cosine similarity. -> Option A

4. Quick Check:

   Denominator must multiply norms, not add [OK]

Hint: Denominator in cosine similarity = product of norms, not sum [OK]

Common Mistakes:

• Adding norms instead of multiplying
• Confusing dot and cross product
• Assuming zero values cause errors

5. You want to improve a movie recommendation system using collaborative filtering. The user-item matrix is very sparse (many missing ratings). Which approach can best handle this sparsity to improve recommendations?

hard

A. Use matrix factorization techniques like Singular Value Decomposition (SVD).

B. Fill missing ratings with zeros before computing similarities.

C. Ignore users with missing ratings and use only complete data.

D. Use random recommendations to cover missing data.

Solution

1. Step 1: Understand sparsity problem in collaborative filtering

   Sparse matrices have many missing values, making similarity calculations unreliable.

2. Step 2: Identify best method to handle sparsity

   Matrix factorization like SVD reduces dimensionality and predicts missing ratings effectively.

3. Final Answer:

   Use matrix factorization techniques like Singular Value Decomposition (SVD). -> Option A

4. Quick Check:

   SVD handles sparsity well [OK]

Hint: Use matrix factorization to predict missing ratings [OK]

Common Mistakes:

• Filling missing ratings with zeros biases similarity
• Ignoring incomplete users loses valuable data
• Random recommendations ignore user preferences