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Module 5 - Recommender Systems.md

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Module Introduction

In this module, you will learn about recommender systems. You will be introduced to the main idea behind recommendation engines, then you will gain an understanding of two main types of recommendation engines, namely, content-based and collaborative filtering.

Learning Objectives

  • To understand the purpose and mechanism of recommendation systems.
  • To understand the different types of recommender systems.
  • To implement recommender systems on a real data set.

Content-based Recommendation Systems

Content-based Recommendation Systems

Collaborative Filtering

Collaborative Filtering

Model 5 - Graded Quiz

Question 1: What is/are the advantage/s of Recommender Systems?

  • A. [ ] Recommender Systems encourage users towards continual usage or purchase of their product.
  • B. [ ] Recommender Systems benefit the service provider by increasing potential revenue and better security for its consumers.
  • C. [ ] Recommender Systems provide a better experience for the users by giving them a broader exposure to many different products they might be interested in.
  • D. [X] All of the above.

Question 2: What is a content-based recommendation system?

  • A. [X] Content-based recommendation system tries to recommend items to the users based on their profile built upon their preferences and taste.
  • B. [ ] Content-based recommendation system tries to recommend items based on the similarity of users when buying, watching, or enjoying something.
  • C. [ ] Content-based recommendation system tries to recommend items based on similarity among items.
  • D. [ ] All of above.

Question 3: What is the meaning of "Cold start" in collaborative filtering?

  • A. [ ] 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.
  • B. [ ] The difficulty in recommendation when we do not have enough ratings in the user-item data set.
  • C. [X] The difficulty in recommendation when we have a new user, and we cannot make a profile for them, or when we have a new item, which has not yet received a rating.

Question 4: What is a "Memory-based" recommender system?

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

Question 5: What is the shortcoming of content-based recommender systems?

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