Content-based filtering in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is content-based filtering in recommendation systems?

Content-based filtering recommends items to a user based on the features of items the user liked before. It looks at item details and matches similar items to the user's preferences.

beginner

How does content-based filtering use item features?

It creates a profile of the user's preferences by analyzing features of items they liked, such as genre, keywords, or attributes, then finds new items with similar features to recommend.

intermediate

What is a common way to measure similarity between items in content-based filtering?

Cosine similarity is often used to measure how close two items are based on their feature vectors. It calculates the angle between two vectors to find similarity.

intermediate

What is a limitation of content-based filtering?

It can only recommend items similar to what the user already knows and likes, so it may not suggest diverse or novel items outside the user's past preferences.

intermediate

Explain how a user profile is built in content-based filtering.

A user profile is built by collecting features from items the user liked and combining them, often by averaging or weighting features, to represent the user's taste for future recommendations.

What does content-based filtering primarily rely on to make recommendations?

A Features of items the user liked

B Ratings from other users

C Random item selection

D Popularity of items

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

Which similarity measure is commonly used in content-based filtering?

A Euclidean distance

B Manhattan distance

C Jaccard index

D Cosine similarity

Cosine similarity measures the angle between feature vectors and is commonly used to find similar items.

What is a key limitation of content-based filtering?

A It ignores item features

B It requires many users' data

C It cannot recommend new or diverse items

D It always recommends popular items

Content-based filtering tends to recommend items similar to what the user already liked, limiting diversity.

How is a user profile created in content-based filtering?

A By averaging features of liked items

B By counting user clicks

C By using other users' preferences

D By random sampling

User profiles are built by combining features of items the user liked, often by averaging.

Content-based filtering is best described as:

A Using social network data

B Using item features to recommend similar items

C Recommending items based on popularity

D Using collaborative user ratings

Content-based filtering recommends items similar to those the user liked based on item features.

Describe how content-based filtering creates recommendations for a user.

Think about how the system learns what the user likes and finds items like those.

What are the main advantages and disadvantages of content-based filtering?

Consider what it does well and where it struggles.

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