Content Based Recommender System

The video tutorial explains how to build a content-based recommender system using the nearest neighbor algorithm to recommend cars based on user-specified features. It covers data exploration, feature selection, and the implementation of the algorithm in Python. The tutorial also demonstrates how to input user data and output the most similar car from the inventory, with a focus on improving the program to display multiple recommendations.

What type of recommender system is used when user preferences are not considered?

• Matrix factorization

• Hybrid

• Content-based

• Collaborative filtering

Which algorithm is suggested for finding the closest car match in the dataset?

• Linear regression

• Support vector machine

• Decision tree

• Nearest neighbor

What is the first step in building a content-based recommender system in Python?

• Importing the dataset

• Evaluating the model

• Deploying the system

• Training the model

Which features are selected for the recommender system in the tutorial?

• MPG, number of doors, color, and price

• MPG, engine size, brand, and year

• MPG, horsepower, weight, and transmission

• MPG, cylinder, horsepower, and weight

What is the purpose of using the 'iloc' command in the tutorial?

• To select specific columns

• To visualize the data

• To train the model

• To split the dataset

What does the distance value indicate in the nearest neighbor algorithm?

• The speed of the algorithm

• The accuracy of the model

• The similarity between cars

• The number of features used

What improvement is suggested for the recommender program?

• Increasing the dataset size

• Using a different algorithm

• Displaying the top three recommendations

• Adding more features