Machine Learning

Recommender Systems: Content-Based Filtering

Content Based Filtering (CBR) is a type of recommender system that uses  description of the item and a profile of the user’s interests to make recommendations to users. CBR uses descriptions and attributes of items to match to user’s selected interests/preferences. CBR uses ML algorithms such as logistic regression or decision trees to make these matches, and results are converted to a ranked recommendation. Content based filtering systems rely on the item in question, and not on other user preferences. The benefit of this method is it does not require and historic data/training data/data from other users.

Machine learning is used to build user profiles if they do not already exist.

ContentFlowThe user profile is typically based on historic user data to build a functional relationship representing how a user would rate a certain item.

Decision tree classification of items based on features

In some cases this taste profile is already known, for example, a survey filled out when signing up for the website could build a user profile.

ContentDiag

Pros

  • Does not depend on data from other users
  • Can recommend unpopular or new items
  • Easy to explain why items were recommended because have feature

Cons

  • Is not predictive (Cannot predict new types of items to users)
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