Why recommendations drive engagement in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for building recommendation models.

ML Python

import [1]

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

Anumpy

Bsklearn

Ctensorflow

Dpandas

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing sklearn which is mainly for classical ML, not deep learning.

Choosing numpy or pandas which are for data manipulation, not modeling.

TensorFlow is commonly used to build recommendation models because it supports deep learning architectures.

2fill in blank

medium

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

ML Python

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

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

A'item_id'

B'rating'

C'user_id'

D'interaction'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'user_id' as columns which would not create the correct matrix.

Using 'rating' which is the values, not columns.

The pivot uses 'item_id' as columns to create the user-item matrix.

3fill in blank

hard

Fix the error in the code that trains a simple recommendation model using TensorFlow.

ML Python

import tensorflow as tf
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(1)
])
model.compile(optimizer='adam', loss=[1])
model.fit(x_train, y_train, epochs=5)

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

A'binary_crossentropy'

B'mse'

C'categorical_crossentropy'

D'accuracy'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'accuracy' which is for classification, not regression.

Using 'categorical_crossentropy' which is for multi-class classification.

Mean squared error ('mse') is appropriate for regression tasks like rating prediction.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps items to their average ratings above 3.

ML Python

average_ratings = {item: [1] for item, ratings in ratings_dict.items() if [2] > 3}

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

Asum(ratings)/len(ratings)

Bmax(ratings)

Csum(ratings)

Dlen(ratings)

Attempts:

3 left

💡 Hint

Common Mistakes

Using max or sum alone which do not give average.

Using length alone which is count, not average.

We calculate the average rating with sum divided by length and filter items with average rating above 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of recommended items with predicted scores above 4.

ML Python

recommendations = [1] for item, score in predicted_scores.items() if score [2] 4}
sorted_recs = sorted(recommendations.items(), key=[3], reverse=True)

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

A{item: score

B>

Clambda x: x[1]

D[item, score

Attempts:

3 left

💡 Hint

Common Mistakes

Using list brackets instead of dictionary braces.

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

Sorting by key instead of value.

The dictionary comprehension collects items with scores greater than 4, then sorts by score descending.

Practice

1. Why do recommendation systems increase user engagement on websites or apps?

easy

A. They show users content or products that match their interests

B. They randomly display items without any user data

C. They reduce the number of items users can see

D. They force users to see ads only

Solution

1. Step 1: Understand user engagement

   User engagement means users spend more time and interact more with the platform.

2. Step 2: Role of recommendations

   Recommendations show users items they like, making them more interested and active.

3. Final Answer:

   They show users content or products that match their interests -> Option A

4. Quick Check:

   Relevant content = higher engagement [OK]

Hint: Recommendations match user interests to boost engagement [OK]

Common Mistakes:

• Thinking recommendations show random items
• Believing recommendations limit user choices
• Confusing ads with recommendations

2. Which of the following is the correct way to describe a recommendation system in simple terms?

easy

A. A system that shows the same items to all users

B. A system that guesses what users might like based on their past behavior

C. A system that deletes user data after each session

D. A system that only shows the newest items regardless of user interest

Solution

1. Step 1: Define recommendation system

   It uses past user data to predict what they might like next.

2. Step 2: Check options

   Only the system that guesses what users might like based on their past behavior describes this behavior correctly; others ignore personalization.

3. Final Answer:

   A system that guesses what users might like based on their past behavior -> Option B

4. Quick Check:

   Personalization = guessing user likes [OK]

Hint: Recommendations use past data to guess user preferences [OK]

Common Mistakes:

• Thinking recommendations show same items to everyone
• Confusing recommendation with data deletion
• Ignoring user history in recommendations

3. Consider this Python code snippet for a simple recommendation score calculation:

user_preferences = {'sports': 5, 'music': 3, 'movies': 4}
item_features = {'sports': 1, 'music': 0, 'movies': 1}
score = sum(user_preferences[cat] * item_features[cat] for cat in item_features)
print(score)

What will be the output of this code?

medium

A. 8

B. 10

C. 7

D. 9

Solution

1. Step 1: Calculate weighted scores

   Multiply user preference by item feature for each category: sports=5*1=5, music=3*0=0, movies=4*1=4.

2. Step 2: Sum the results

   Sum = 5 + 0 + 4 = 9.

3. Final Answer:

   9 -> Option D

4. Quick Check:

   5+0+4 = 9 [OK]

Hint: Multiply and sum matching categories for score [OK]

Common Mistakes:

• Adding all preference values without weights
• Ignoring zero feature values
• Multiplying wrong pairs of categories

4. The following code is intended to calculate a recommendation score but has a bug:

user_prefs = {'books': 4, 'games': 2}
item_feats = {'books': 1, 'games': 1}
score = 0
for cat in user_prefs:
    score += user_prefs[cat] * item_feats[cat]
print(score)

What is the error and how to fix it?

medium

A. KeyError if item_feats lacks a category; fix by iterating item_feats keys

B. SyntaxError due to missing colon; fix by adding colon after for loop

C. TypeError because score is int; fix by converting score to float

D. No error; code runs correctly

Solution

1. Step 1: Identify potential KeyError

   The loop uses keys from user_prefs but accesses item_feats; if item_feats misses a key, error occurs.

2. Step 2: Fix iteration to avoid missing keys

   Iterate over keys in item_feats or check key existence before access.

3. Final Answer:

   KeyError if item_feats lacks a category; fix by iterating item_feats keys -> Option A

4. Quick Check:

   Key mismatch causes error [OK]

Hint: Check keys exist before accessing dict values [OK]

Common Mistakes:

• Assuming all keys match in both dicts
• Ignoring possible KeyError exceptions
• Confusing syntax errors with runtime errors

5. A streaming service wants to improve engagement by recommending movies. They have user watch history and movie genres. Which approach best uses machine learning to drive engagement?

hard

A. Display only movies with the highest global ratings ignoring user data

B. Recommend only the newest movies regardless of user preferences

C. Build a model that predicts user ratings for unseen movies based on past watched genres

D. Show random movies from the entire catalog to all users

Solution

1. Step 1: Understand personalized recommendations

   Using user watch history and genres helps predict what each user likes.

2. Step 2: Evaluate options

   Build a model that predicts user ratings for unseen movies based on past watched genres uses machine learning to personalize, increasing engagement; others ignore personalization.

3. Final Answer:

   Build a model that predicts user ratings for unseen movies based on past watched genres -> Option C

4. Quick Check:

   Personalized prediction = better engagement [OK]

Hint: Use user history to predict preferences for recommendations [OK]

Common Mistakes:

• Ignoring user preferences in recommendations
• Assuming newest or highest rated always engage users
• Confusing random recommendations with personalized ones