Recommender System >> Machine Learning with Python
Recommender System
TOTAL POINTS 15
1.What is/are the advantage/s of Recommender Systems ?
3 points
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.
2.What is a content-based recommendation system?
3 points
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.
All of above.
3.What is the meaning of “Cold start” in collaborative filtering?
3 points
The difficulty in recommendation when we do not have enough ratings in the user-item dataset.
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.
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.
4.What is a “Memory-based” recommender system?
3 points
In memory based approach, a model of users is developed in attempt to learn their preferences.
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.
5.What is the shortcoming of content-based recommender systems?
3 points
As it is based on similarity among items and users, it is not easy to find the neighbour users.
It needs to find similar group of users, so suffers from drops in performance, simply due to growth in the similarity computation.
Users will only get recommendations related to their preferences in their profile, and recommender engine may never recommend any item with other characteristics.
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