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I am looking for a simple suggestion algorithm to implement in to my Web App. Much like Netflix, Amazon, etc... But simpler. I don't need teams of Phd's working to get a better suggestion metric.
So say I have:
I want to suggest to User1 they might also like Object2.
I can obviously come up with something naive. I'm looking for something vetted and easily implemented.
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Collaborative filtering (CF) and its modifications is one of the most commonly used recommendation algorithms. Even data scientist beginners can use it to build their personal movie recommender system, for example, for a resume project.
Easiest way to build a recommendation system is popularity based, simply over all the products that are popular, So how to identify popular products, which could be identified by which are all the products that are bought most, Example, In shopping store we can suggest popular dresses by purchase count.
Singular value decomposition also known as the SVD algorithm is used as a collaborative filtering method in recommendation systems.
There are many simple and not so simple examples of suggestion algorithms in the excellent Programming Collective Intelligence
The Pearson correlation coefficient (a little dry Wikipedia article) can give pretty good results. Here's an implementation in Python and another in TSQL along with an interesting explanation of the algorithm.
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