July 2018
Beginner to intermediate
146 pages
3h 39m
English
Content preview from Hands-On Recommendation Systems with Python
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Summary
In this chapter, we built a simple recommender, which was a clone of the IMDB Top 250 chart. We then proceeded to build an improved knowledge-based recommender, which asked the user for their preferred genres, duration, and time. In the process of building these models, we also learned to perform some advanced data wrangling with the Pandas library.
In the next chapter, we will use more advanced features and techniques to build a content-based recommender. This model will be able to detect similar movies based on their plots and recommend movies by identifying similarities in genre, cast, crew, plot, and so on.
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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.