Building a Recommendation Engine With PyTorch

Building a Recommendation Engine With PyTorch

Building a Recommendation Engine With PyTorch. Understanding the internals of recommendation engines

Why a Recommendation Engine?

As a developer who barely knows anything about ML (machine learning), I find building a recommendation engine one of the easiest projects to get started with ML. It is practical and not that difficult to understand for beginners with no machine learning background to jump right into.

Getting Started

Before we get started with the actual implementation, I’ll briefly go over some concepts that you might find helpful to build a recommendation engine.

There are essentially three types of algorithms that your recommendation engine could use when recommending an item to a user:

1. Demographic filtering

This type of filtering looks at the general trends and popularity of an item based on users with similar demographics. This means that users with similar demographics are recommended the same items and personalized recommendations are very limited.

2. Content-based filtering

The underlying algorithm for this type of filtering looks at the similarity of items based on their metadata. For example, for games, the metadata would be things like platforms, genres, and publisher. Therefore, if a user liked a PC action RPG game that is published by Valve, then most likely they would like another game that has similar metadata (i.e. games that are published by Valve and are action RPG PC games). This means that personalized recommendations are now involved since games that the user liked are used to determine games that the user would probably like too.

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Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

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Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.

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Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.