July 2018
Beginner to intermediate
146 pages
3h 39m
English
Content preview from Hands-On Recommendation Systems with Python
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Building Content-Based Recommenders
In the previous chapter, we built an IMDB Top 250 clone (a type of simple recommender) and a knowledge-based recommender that suggested movies based on timeline, genre, and duration. However, these systems were extremely primitive. The simple recommender did not take into consideration an individual user's preferences. The knowledge-based recommender did take account of the user's preference for genres, timelines, and duration, but the model and its recommendations still remained very generic.
Imagine that Alice likes the movies The Dark Knight, Iron Man, and Man of Steel. It is pretty evident that Alice has a taste for superhero movies. However, our models from the previous chapter would not be able to ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.