Database Reference
In-Depth Information
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Large number of available options for users
: When there are a very large num-
ber of available items, it becomes increasingly difficult for the user to find
something they want. Searching can help when the user knows what they are
looking for, but often, the right item might be something previously unknown to
them. In this case, being recommended relevant items, that the user may not
already know about, can help them discover new items.
•
A significant degree of personal taste involved
: When personal taste plays a
large role in selection, recommendation models, which often utilize a wisdom of
the crowd approach, can be helpful in discovering items based on the behavior of
others that have similar taste profiles.
In this chapter, we will:
• Introduce the various types of recommendation engines
• Build a recommendation model using data about user preferences
• Use the trained model to compute recommendations for a given user as well com-
pute similar items for a given item (that is, related items)
• Apply standard evaluation metrics to the model that we created to measure how
well it performs in terms of predictive capability
