Content-based filtering in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a TF-IDF matrix from the item descriptions.

ML Python

from sklearn.feature_extraction.text import TfidfVectorizer

items = ['Action movie with explosions', 'Romantic comedy with love', 'Sci-fi thriller with aliens']
tfidf = TfidfVectorizer()
item_matrix = tfidf.[1](items)

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

Afit

Btransform

Cfit_transform

Dfit_predict

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform before fitting the vectorizer.

Using fit without transforming the data.

We use fit_transform to learn the vocabulary and transform the items into TF-IDF vectors in one step.

2fill in blank

medium

Complete the code to compute cosine similarity between item vectors.

ML Python

from sklearn.metrics.pairwise import [1]

similarity_matrix = cosine_similarity(item_matrix)

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

Acosine_similarity

Beuclidean_distances

Cmanhattan_distances

Dpairwise_distances

Attempts:

3 left

💡 Hint

Common Mistakes

Using distance functions instead of similarity.

Confusing cosine_similarity with pairwise_distances.

cosine_similarity computes similarity scores between vectors, which is used in content-based filtering.

3fill in blank

hard

Fix the error in the code to get the top 2 similar items for the first item.

ML Python

import numpy as np

similarities = similarity_matrix[0]
top_indices = np.argsort(similarities)[[1]][:2]

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

A:

B-1:

C1:

D::-1

Attempts:

3 left

💡 Hint

Common Mistakes

Not reversing the sorted indices, resulting in lowest scores first.

Using incorrect slice syntax causing errors.

We reverse the sorted indices with [::-1] to get the highest similarity scores first.

4fill in blank

hard

Fill both blanks to create a dictionary of item indices and their similarity scores above 0.5.

ML Python

similar_items = {i: similarities[i] for i in range(len(similarities)) if similarities[i][1] 0.5 and i != [2]

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

A>

B0

C<

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > causing wrong filtering.

Not excluding the item itself leading to self-similarity.

We want similarities greater than 0.5 and exclude the item itself at index 0.

5fill in blank

hard

Fill all three blanks to sort the similar items by score descending and get their indices.

ML Python

sorted_items = sorted(similar_items.items(), key=lambda x: x[1], reverse=[2])
top_similar_indices = [item[[3]] for item in sorted_items]

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

A[1]

BTrue

C0

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Sorting ascending by mistake with reverse=False.

Extracting wrong tuple element for indices.

We sort by the similarity score at index 1 descending (reverse=True) and extract the item indices at position 0.

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