Recommender Systems with Machine Learning - Making Recommendations-1

The video tutorial covers the development of a content-based recommender system for songs. It begins with an introduction to the function and its components, such as songs_likes and distance_score. The tutorial then delves into the logic for finding similar songs, using a similarity matrix and filtering techniques. It also addresses error handling and the importance of using a TF-IDF matrix. The session concludes with a transition to collaborative filtering, setting the stage for the next project.

What is the primary purpose of the content-based recommender function discussed in the video?

• To create a playlist of random songs

• To filter out unwanted songs

• To suggest songs based on user likes

• To recommend movies based on user preferences

What are the two key outputs obtained from the function find_closest?

• Genre and mood

• Closest title and distance score

• Song duration and artist name

• Album name and release year

How is the distance score used in determining similar songs?

• It is used to find songs with a score of 50

• It is used to find songs with a score of 0

• It is used to find songs with a score of 100

• It is ignored if it is less than 50

What is the role of the similarity matrix in the recommender system?

• To store user preferences

• To calculate song popularity

• To list all available songs

• To find similar songs based on indexes

Which function is used to filter and sort the list of similar songs?

• Map function

• Reduce function

• Lambda function

• Filter function

What was the small error mentioned in the video related to?

• Incorrect album year

• Incorrect song title

• Mismatch in score naming

• Wrong artist name

What is the next type of filtering discussed after content-based filtering?

• Genre-based filtering

• Artist-based filtering

• Mood-based filtering

• Collaborative filtering