ML Basics

Question 1

Which Python library is primarily used for numerical operations essential in collaborative filtering?

Answer 1

NumPy.

Question 2

Which Python library should you use for manipulating data tables when working with collaborative filtering?

Answer 2

Pandas.

Question 3

What is the standard Python library for interacting with SQLite databases?

Answer 3

SQLite3.

Question 4

What machine learning library in Python provides tools to implement collaborative filtering?

Answer 4

Scikit-Learn.

Question 5

What are the three basic types of tables you need in a database for collaborative filtering?

Answer 5

User table, item table, and ratings/interactions table.

Question 6

Which technique is often used in collaborative filtering to decompose the user-item interaction matrix?

Answer 6

Matrix factorization, specifically methods like Singular Value Decomposition (SVD) or Alternating Least Squares (ALS).

Question 7

What metric can be used to evaluate the accuracy of a collaborative filtering model’s predictions?

Answer 7

RMSE (Root Mean Square Error).

Question 8

How do you load data from an SQLite database into a Pandas DataFrame for processing in Python?

Answer 8

Use the read_sql_query function from Pandas, providing it with an SQL query and a connection object.

Question 9

What is the purpose of decomposing the interaction matrix in collaborative filtering?

Answer 9

To reduce its dimensionality, enabling the approximation of missing entries and facilitating easier computation of recommendations.

Question 10

What evaluation metrics can be used to measure the performance of a recommender system in terms of relevance and user satisfaction?

Answer 10

Precision and recall.

Question 11

What distinguishes content-based filtering from collaborative filtering?

Answer 11

Content-based filtering recommends items based on the features of the items and a profile of the user’s preferences, unlike collaborative filtering which uses user-item interactions.

Question 12

What are hybrid recommender systems?

Answer 12

Hybrid recommender systems combine collaborative filtering, content-based filtering, and other methods to improve recommendation performance and overcome single approach limitations.

Question 13

What is the cold start problem in recommender systems?

Answer 13

The cold start problem occurs when there is insufficient data to make accurate recommendations for new users or items.

Question 14

What does sparsity mean in the context of collaborative filtering?

Answer 14

Sparsity refers to the condition where the user-item interaction matrix has a large percentage of missing values, posing challenges in learning effective recommendations.

Question 15

How are implicit feedback and explicit feedback different in recommender systems?

Answer 15

Implicit feedback is derived from user actions such as views and clicks, whereas explicit feedback involves direct ratings or preferences provided by the user.

Question 16

What is dimensionality reduction and why is it important in machine learning?

Answer 16

Dimensionality reduction techniques like PCA reduce the number of variables under consideration, simplifying models while retaining essential information, crucial for managing large datasets efficiently.

Question 17

What is regularization and why is it used in machine learning?

Answer 17

Regularization adds a penalty to the loss function to prevent overfitting, maintaining model simplicity and reliability even with complex data.

Question 18

What are neighborhood methods in collaborative filtering?

Answer 18

Neighborhood methods involve computing similarities between users or items to make predictions and recommendations based on nearest neighbors.

Question 19

How does deep learning enhance recommender systems?

Answer 19

Deep learning models complex non-linear relationships using neural networks, potentially improving accuracy in recommendations by capturing intricate patterns in data.

Question 20

What are ensemble methods in machine learning?

Answer 20

Ensemble methods improve prediction accuracy by combining multiple models, using techniques like random forests and gradient boosting machines.

Question 21

Besides RMSE and precision, what are other metrics for evaluating recommender systems?

Answer 21

Additional metrics include F1-score, NDCG (Normalized Discounted Cumulative Gain), and AUC (Area Under Curve), used to measure the effectiveness of recommender systems.

Question 22

What makes a recommender system context-aware?

Answer 22

Context-aware recommender systems consider additional contextual information such as location, time of day, or social context to provide more relevant recommendations.

Question 23

What is a recommender system?

Answer 23

A recommender system is a type of information filtering system that seeks to predict user preferences and recommend items that are likely of interest to the user.

Question 24

What is the primary purpose of using machine learning in recommender systems?

Answer 24

Machine learning in recommender systems is used to predict user preferences based on past data, improving the accuracy of recommendations.

Question 25

What is an interaction matrix in collaborative filtering?

Answer 25

An interaction matrix is a grid used in collaborative filtering that stores values representing interactions between users and items, like ratings or purchases.

Question 26

Why is data sparsity a challenge in collaborative filtering?

Answer 26

Data sparsity is challenging because most of the interactions are not recorded, making it difficult to accurately predict preferences from limited available data.

Question 27

What does PCA stand for and what is its role in machine learning?

Answer 27

PCA stands for Principal Component Analysis. It is a dimensionality reduction technique that simplifies data by reducing the number of variables, while retaining important information.

Question 28

How does regularization help in preventing overfitting?

Answer 28

Regularization helps prevent overfitting by introducing a penalty term to the loss function used to train the model, which discourages overly complex models.

Question 29

What is the difference between explicit and implicit feedback in recommender systems?

Answer 29

Explicit feedback is direct input from users, like ratings, while implicit feedback is derived from user behavior, such as browsing history or purchase records.

Question 30

What does SVD stand for and why is it used in collaborative filtering?

Answer 30

SVD stands for Singular Value Decomposition. It is used in collaborative filtering to decompose the interaction matrix into simpler matrices, making it easier to fill missing values and make predictions.

Question 31

How do neural networks contribute to the performance of recommender systems?

Answer 31

Neural networks can model complex and non-linear relationships, which allows them to capture deeper patterns in the data, potentially improving the accuracy of recommendations.

Question 32

What is a hybrid recommender system and why is it advantageous?

Answer 32

A hybrid recommender system combines multiple recommendation techniques (like collaborative filtering and content-based filtering) to leverage the strengths of each and mitigate their weaknesses, often resulting in better performance.

Question 33

Why is context important in recommender systems?

Answer 33

Contextual information (like time or location) can significantly influence user preferences, and incorporating it into a recommender system can help make more relevant and timely recommendations.

Question 34

What does NDCG stand for and what does it measure in recommender systems?

Answer 34

NDCG stands for Normalized Discounted Cumulative Gain. It measures the effectiveness of a recommender system by evaluating the ranking of recommended items, giving more importance to the relevance of items ranked higher on the list.