Coursera Answers

Machine Learning With Python Week 5 Quiz Answer | Recommender System

Machine Learning With Python Week 5 Quiz Answer  Recommender System

Machine Learning With Python Week 5 Quiz Answer | Recommender System

Recommender System



Question 1)
What is the meaning of “Cold start” in collaborative filtering?

  • The difficulty in recommendation when we do not have enough ratings in the user-item dataset.
  • The difficulty in recommendation when the number of users or items increases and the amount of data expands, so algorithms will begin to suffer drops in performance.
  • The difficulty in recommendation when we have new user, and we cannot make a profile for him, or when we have a new item, which has not got any rating yet.




Question 2)
What is a “Memory-based” recommender system?


  • In memory based approach, we use the entire user-item dataset to generate a recommendation system.
  • In memory based approach, a recommender system is created using machine learning techniques such as regression, clustering, classification, etc.
  • In memory based approach, a model of users is developed in attempt to learn their preferences.




Question 3)
What is the shortcoming of content-based recommender systems?

  • As it is based on similarity among items and users, it is not easy to find the neighbour users.
  • Users will only get recommendations related to their preferences in their profile, and recommender engine may never recommend any item with other characteristics.
  • It needs to find similar group of users, so suffers from drops in performance, simply due to growth in the similarity computation.



Question 4)
What is are the advantage ls of Recommender Systems?
  • Recommender Systems provide a better experience for the users by giving them a broader exposure to many different products they might be interested in.
  • Recommender Systems encourage users towards continual usage or purchase of their product
  • Recommender Systems benefit the service provider by increasing potential revenue and better security for its consumers.
  • All of the above
Question 5)
what is a content-based recommendation system?
  • Content-based recommendation System tries to recommend items to the users based on their profile built upon their preferences and taste.
  • Content-based recommendation system tries to recommend items based on similarity among items.
  • Content-based recommendation system tries to recommend items based on the similarity of users when buying, watching, or enjoying something.