In this module, you will learn about recommender systems. First, you will get introduced with main idea behind recommendation engines, then you understand two main types of recommendation engines, namely, content-based and collaborative filtering.

Learning Objectives

• Explain how the different recommender systems work.
• Describe the advantages of using recommendation systems.
• Explain the difference between memory-based and model-based implementations of recommender systems.
• Explain how content-based recommender systems work.
• Explain how collaborative filtering systems work.
• Implement a recommender system on a real dataset.

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• Content-based Recommendation Engines

  • Lab 5-1: Content-based Recommendation Systems

• Collaborative Filtering

  • Lab 5-2: Collaborative Filtering on Movies

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Quiz: Recommender System

Recommender System

Total points 9

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 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.

Correct

Question 2

What is a "Memory-based" recommender system?

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

Correct

Question 3

What is the shortcoming of content-based recommender systems?

• It needs to find similar group of users, so suffers from drops in performance, simply due to growth in the similarity computation.
• 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.

Correct