November 2017
Beginner to intermediate
366 pages
7h 59m
English
Content preview from R Data Analysis Projects
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Summary
We started the chapter by introducing content-based filtering. We discussed how content based filtering methods can help with cold-start problems in recommendation systems. We then explained the new aggregator use case. We explored the data provided by the customer--various news articles from different publishers belonging to different categories. Based on the data, we came up with a design for our content-based recommendation system.
We implemented a similarity dictionary; given a news article, this dictionary would be able to provide the top N matching articles. The similarity was calculated based on the words present in the article. We leveraged the vector space model for text and ultimately used the cosine distance to find the ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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.