User-based vs item-based in ML Python - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate user similarity using cosine similarity.

ML Python

from sklearn.metrics.pairwise import [1]
user_similarity = [1](user_item_matrix)

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

Apairwise_distances

Bcosine_similarity

Cmanhattan_distances

Deuclidean_distances

Attempts:

3 left

💡 Hint

Common Mistakes

Using distance metrics instead of similarity metrics.

Confusing cosine similarity with Euclidean distance.

The cosine similarity function measures similarity between users based on their item ratings.

2fill in blank

medium

Complete the code to find the top 3 similar items for a given item index.

ML Python

import numpy as np
item_similarities = item_similarity_matrix[item_index]
top_items = np.argsort(item_similarities)[[1]][:3]

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

A-3:

B:3

C::-1

D3:

Attempts:

3 left

💡 Hint

Common Mistakes

Not reversing the sorted indices, resulting in least similar items.

Using incorrect slicing that excludes top items.

Using [::-1] reverses the sorted indices to get the most similar items first.

3fill in blank

hard

Fix the error in the code to compute predicted rating using user-based collaborative filtering.

ML Python

predicted_rating = sum(user_similarity[user_id, [1]] * ratings[[1], item_id]) / sum(abs(user_similarity[user_id, [1]]))

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

Aneighbor_id

Buser_id

Citem_id

Drating_id

Attempts:

3 left

💡 Hint

Common Mistakes

Using user_id instead of neighbor_id causing self-similarity errors.

Using item_id which is unrelated to user similarity indexing.

The neighbor_id represents other users whose similarity and ratings are used to predict the rating.

4fill in blank

hard

Fill both blanks to create an item-based collaborative filtering prediction formula.

ML Python

predicted_rating = sum(item_similarity[item_id, [1]] * ratings[user_id, [1]]) / sum(abs(item_similarity[item_id, [2]]))

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

Aneighbor_item

Buser_id

Ditem_id

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing user_id and item_id in similarity indexing.

Using inconsistent variable names for neighbor items.

Both blanks refer to neighbor items to weight the user's ratings for prediction.

5fill in blank

hard

Fill all three blanks to complete the code that builds a user-item rating matrix from raw data.

ML Python

import pandas as pd
ratings_df = pd.DataFrame(data, columns=[[1], [2], [3]])
rating_matrix = ratings_df.pivot(index=[1], columns=[2], values='rating').fillna(0)

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

A'user_id'

B'item_id'

C'rating'

D'timestamp'

Attempts:

3 left

💡 Hint

Common Mistakes

Using timestamp instead of rating for values.

Swapping user_id and item_id in pivot parameters.

The DataFrame columns must be user_id, item_id, and rating to build the matrix correctly.

Practice

1. Which of the following best describes user-based recommendation?

easy

A. Suggest items liked by users similar to you

B. Suggest items similar to what you liked

C. Suggest random popular items

D. Suggest items based on item price

Solution

1. Step 1: Understand user-based recommendation

   User-based recommendation finds users similar to you and suggests what they liked.

2. Step 2: Compare options

   Suggest items liked by users similar to you matches this definition exactly, while others describe different or unrelated methods.

3. Final Answer:

   Suggest items liked by users similar to you -> Option A

4. Quick Check:

   User-based = Suggest items liked by similar users [OK]

Hint: User-based = find similar users, recommend their items [OK]

Common Mistakes:

• Confusing user-based with item-based
• Thinking user-based recommends similar items
• Choosing popular or random items as user-based

2. Which code snippet correctly represents item-based recommendation logic?

easy

A. recommend_items = find_similar_users(user).liked_items()

B. recommend_items = find_similar_items(user_liked_items)

C. recommend_items = random_popular_items()

D. recommend_items = items_sorted_by_price()

Solution

1. Step 1: Identify item-based recommendation code

   Item-based recommends items similar to those the user liked, so code should find similar items.

2. Step 2: Match code to logic

   recommend_items = find_similar_items(user_liked_items) calls find_similar_items(user_liked_items), which fits item-based logic. Others do not.

3. Final Answer:

   <code>recommend_items = find_similar_items(user_liked_items)</code> -> Option B

4. Quick Check:

   Item-based = find similar items [OK]

Hint: Item-based code finds similar items to user's liked items [OK]

Common Mistakes:

• Choosing user-based code for item-based question
• Confusing random or price sorting with item similarity
• Ignoring the input to similarity function

3. Given this code snippet for user-based recommendation:

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))

What is the output?

medium

A. ['ItemA', 'ItemB', 'ItemC']

B. ['ItemB', 'ItemC']

C. ['ItemA', 'ItemB']

D. []

Solution

1. Step 1: Identify users with similarity > 0.85

   Only 'User1' has similarity 0.9 > 0.85, so only their liked items are added.

2. Step 2: Collect items liked by 'User1'

   'User1' likes ['ItemA', 'ItemB'], so these are added to the recommendation set.

3. Final Answer:

   ['ItemA', 'ItemB'] -> Option C

4. Quick Check:

   Similarity filter picks User1's items [OK]

Hint: Only users with similarity > 0.85 contribute items [OK]

Common Mistakes:

• Including items from User2 who has similarity 0.8
• Not sorting output before printing
• Confusing set update with append

4. Find the error in this item-based recommendation code:

user_liked = ['ItemA', 'ItemB']
def find_similar_items(items):
    return ['ItemC', 'ItemD']
recommend = find_similar_items(user_liked)
print(recommend)

What is the main problem?

medium

A. Syntax error in function definition

B. Print statement missing parentheses

C. Variable user_liked is not defined

D. Function returns unrelated items, ignoring input

Solution

1. Step 1: Check function behavior

   The function find_similar_items ignores its input and returns fixed items.

2. Step 2: Identify why this is a problem

   Item-based recommendation should return items similar to input, but this function does not use input at all.

3. Final Answer:

   Function returns unrelated items, ignoring input -> Option D

4. Quick Check:

   Function must use input to find similar items [OK]

Hint: Check if function uses input parameter correctly [OK]

Common Mistakes:

• Thinking code has syntax errors when it doesn't
• Ignoring that function ignores input
• Confusing variable names or print syntax

5. You want to build a hybrid recommendation system combining user-based and item-based methods. Which approach best fits this goal?

hard

A. Combine scores from similar users and similar items for each candidate

B. Use only user-based recommendations to simplify model

C. Use only item-based recommendations to reduce computation

D. Randomly pick recommendations from either user or item-based

Solution

1. Step 1: Understand hybrid recommendation

   Hybrid systems combine strengths of user-based and item-based methods to improve accuracy.

2. Step 2: Identify best combination method

   Combining scores from both similar users and similar items for each candidate item leverages both perspectives effectively.

3. Final Answer:

   Combine scores from similar users and similar items for each candidate -> Option A

4. Quick Check:

   Hybrid = combine user and item scores [OK]

Hint: Hybrid = combine user and item recommendation scores [OK]

Common Mistakes:

• Choosing only one method loses hybrid benefits
• Random picking reduces recommendation quality
• Ignoring score combination logic