Content-based filtering in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does content-based filtering recommend items?

Which of the following best describes how content-based filtering recommends items to a user?

A It recommends items similar to those the user liked before based on item features.

B It recommends items liked by other users with similar tastes.

C It randomly recommends popular items regardless of user preferences.

D It recommends items based on the time of day and user location.

💡 Hint

Think about how the system uses the user's past preferences and item details.

Content-based filtering uses the features of items the user liked to find similar items to recommend.

❓ Predict Output

intermediate

Output of cosine similarity calculation

What is the output of this Python code that calculates cosine similarity between two item feature vectors?

ML Python

from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

item1 = np.array([[1, 0, 1, 0]])
item2 = np.array([[0, 1, 1, 0]])
similarity = cosine_similarity(item1, item2)
print(round(similarity[0][0], 2))

A 0.33

B 0.50

C 0.67

D 0.75

💡 Hint

Recall cosine similarity formula and count overlapping features.

Cosine similarity is the dot product divided by the product of vector lengths. Here it equals 0.5.

❓ Model Choice

advanced

Best model for content-based filtering with sparse user-item data

Which model is most suitable for content-based filtering when user-item interaction data is sparse but item features are rich?

A Collaborative filtering using user similarity

B Matrix factorization using only user-item ratings

C K-Nearest Neighbors using item feature vectors

D Random forest classifier on user demographic data

💡 Hint

Focus on models that use item features directly.

K-Nearest Neighbors on item features works well when user-item data is sparse but item features are available.

❓ Hyperparameter

advanced

Choosing the number of neighbors in content-based KNN

In a content-based filtering system using K-Nearest Neighbors on item features, what is the effect of increasing the number of neighbors (k)?

A It increases recommendation diversity but may reduce relevance.

B It always improves recommendation accuracy.

C It decreases the number of recommended items.

D It causes the model to ignore item features.

💡 Hint

Think about how more neighbors affect similarity and variety.

Increasing k includes more items that are less similar, increasing diversity but possibly lowering relevance.

🔧 Debug

expert

Why does this content-based filtering code produce identical recommendations for all users?

Given this simplified content-based filtering code, why do all users get the same recommended items? ```python import numpy as np user_profiles = { 'user1': np.array([1, 0, 1]), 'user2': np.array([1, 0, 1]), 'user3': np.array([1, 0, 1]) } item_features = { 'itemA': np.array([1, 0, 0]), 'itemB': np.array([0, 1, 1]), 'itemC': np.array([1, 0, 1]) } recommendations = {} for user, profile in user_profiles.items(): scores = {} for item, features in item_features.items(): scores[item] = np.dot(profile, features) recommended = sorted(scores, key=scores.get, reverse=True)[:2] recommendations[user] = recommended print(recommendations) ```

A The code uses Euclidean distance instead of dot product.

B The item features are normalized incorrectly causing identical scores.

C The sorting function is not stable, causing random recommendations.

D All user profiles are identical, so dot products and recommendations are the same.

💡 Hint

Check the user profiles carefully.

Since all user profiles are the same vector, their dot products with items are identical, leading to identical recommendations.

Practice

1. What is the main idea behind content-based filtering in recommendation systems?

easy

A. It recommends items based on what other users liked.

B. It recommends items similar to what the user liked before based on item features.

C. It randomly suggests items without any user data.

D. It recommends items based on the most popular items overall.

Solution

1. Step 1: Understand content-based filtering concept

   Content-based filtering uses the features of items a user liked to find similar items.

2. Step 2: Compare with other recommendation methods

   Unlike collaborative filtering, it does not rely on other users' preferences but on item features.

3. Final Answer:

   It recommends items similar to what the user liked before based on item features. -> Option B

4. Quick Check:

   Content-based filtering = Recommend similar items [OK]

Hint: Focus on item features and user history for content-based filtering [OK]

Common Mistakes:

• Confusing content-based with collaborative filtering
• Thinking recommendations are random
• Assuming popularity drives recommendations

2. Which of the following is the correct way to represent item features for content-based filtering in Python?

easy

A. item_features = [1, 2, 3, 4]

B. item_features = ['item1', 'item2', 'item3']

C. item_features = {'item1': 'popular', 'item2': 'new'}

D. item_features = {'item1': [0.2, 0.8], 'item2': [0.5, 0.5]}

Solution

1. Step 1: Identify correct data structure for features

   Features should be numeric vectors representing item properties, stored in a dictionary keyed by item IDs.

2. Step 2: Check each option

   item_features = {'item1': [0.2, 0.8], 'item2': [0.5, 0.5]} uses a dictionary with lists of numbers, suitable for feature vectors. Others are lists or strings, not suitable for numeric features.

3. Final Answer:

   item_features = {'item1': [0.2, 0.8], 'item2': [0.5, 0.5]} -> Option D

4. Quick Check:

   Features as numeric vectors = item_features = {'item1': [0.2, 0.8], 'item2': [0.5, 0.5]} [OK]

Hint: Use dictionaries with numeric lists for item features [OK]

Common Mistakes:

• Using strings instead of numeric vectors
• Using simple lists without keys
• Confusing item labels with features

3. Given the following Python code for content-based filtering, what will be the output?

import numpy as np

item_features = {'A': np.array([1, 0]), 'B': np.array([0, 1]), 'C': np.array([1, 1])}
user_likes = np.array([1, 0])
similarities = {item: np.dot(user_likes, features) for item, features in item_features.items()}
recommended = max(similarities, key=similarities.get)
print(recommended)

medium

A. B

B. C

C. A

D. Error

Solution

1. Step 1: Calculate dot products for similarity

   Dot product with user_likes [1,0]: - A: [1,0]·[1,0] = 1 - B: [1,0]·[0,1] = 0 - C: [1,0]·[1,1] = 1

2. Step 2: Find item with max similarity

   Items A and C both have similarity 1, but max() returns the first max key, which is 'A'.

3. Final Answer:

   A -> Option C

4. Quick Check:

   Max dot product = 'A' [OK]

Hint: Dot product similarity picks highest matching feature vector [OK]

Common Mistakes:

• Assuming 'C' is chosen because it has more features
• Confusing dot product with sum
• Expecting an error due to dictionary comprehension

4. Identify the error in this content-based filtering code snippet and select the fix:

item_features = {'X': [1, 0], 'Y': [0, 1]}
user_profile = [1, 0]
similarities = {}
for item in item_features:
    similarities[item] = user_profile * item_features[item]
print(similarities)

medium

A. Replace '*' with a dot product function like numpy.dot

B. Change user_profile and item_features values to strings

C. Use '+' instead of '*' to combine lists

D. No error, code runs fine

Solution

1. Step 1: Understand the operation on lists

   Multiplying two Python lists with '*' is invalid and causes a TypeError.

2. Step 2: Correct the operation to compute similarity

   Use numpy.dot or equivalent to compute dot product between vectors for similarity.

3. Final Answer:

   Replace '*' with a dot product function like numpy.dot -> Option A

4. Quick Check:

   List multiplication invalid, use dot product [OK]

Hint: Use dot product for vector similarity, not '*' on lists [OK]

Common Mistakes:

• Trying to multiply lists directly
• Using '+' which concatenates lists, not similarity
• Ignoring error messages

5. You want to recommend movies to a user using content-based filtering. The user liked movies with genres ['Action', 'Adventure']. You have a movie database with genre tags. Which approach best personalizes recommendations?

hard

A. Calculate similarity by counting overlapping genres between user liked movies and candidate movies.

B. Recommend the most popular movies regardless of genre.

C. Recommend movies liked by other users who watched 'Action' movies.

D. Randomly select movies from the database.

Solution

1. Step 1: Understand content-based filtering with genre tags

   Content-based filtering uses item features (genres) to find similar items to what the user liked.

2. Step 2: Choose method that compares genre overlap

   Counting overlapping genres directly measures similarity between user preferences and candidate movies.

3. Final Answer:

   Calculate similarity by counting overlapping genres between user liked movies and candidate movies. -> Option A

4. Quick Check:

   Genre overlap similarity = Calculate similarity by counting overlapping genres between user liked movies and candidate movies. [OK]

Hint: Match user liked genres with movie genres for recommendations [OK]

Common Mistakes:

• Using popularity instead of user preferences
• Confusing collaborative filtering with content-based
• Random recommendations ignoring user data