How are recommendation engines built?
From the post:
The success of Amazon and Netflix has made recommendation systems not only common but also extremely popular. For many people, the recommendation system seems to be one of the easiest applications to understand; and a majority of us use them daily.
Haven’t you ever marveled at the ingenuity of a website offering the HDMI cable that goes with a television? Never been tempted by the latest trendy book about vampires? Been irritated by suggestions for diapers or baby powder though your child has been potty-trained for 3 months? Been annoyed to see flat screen TVs pop up on your browser every year with the approach of summer? The answer is, at least to me: “Yes, I have.”
But before cursing, every user should be aware of the difficulty of building an effective recommendation system! Below are some elements on how these systems are built (and ideas for how you can build your own).
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A high level view of some of the strategies that underlie recommendation engines. This won’t help you will the nuts-n-bolts of building a recommendation engine but can serve as a brief introduction.
Recommendation engines could be used with topic maps to either annoy users with guesses as to what they would like to see next or perhaps more usefully in a topic map authoring context. To alert an author of closely similar material already in the topic map.
I first saw this in a tweet by Christophe Lalanne.