Book Image

Clojure for Data Science

By : Garner
Book Image

Clojure for Data Science

By: Garner

Overview of this book

The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a scripting language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs. Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility! You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models. Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future.
Table of Contents (12 chapters)
11
Index

Types of recommender systems

There are typically two approaches taken to the problem of recommendation. Both make use of the notion of similarity between things, as we encountered it in the previous chapter.

One approach is to start with an item we know the user likes and recommend the other items that have similar attributes. For example, if a user is interested in action adventure movies, we might present to them a list of all the action adventure movies that we can offer. Or, if we have more data available than simply the genre—perhaps a list of tags—then we could recommend movies that have the most tags in common. This approach is called content-based filtering, because we're using the attributes of the items themselves to generate recommendations for similar items.

Another approach to recommendation is to take as input some measure of the user's preferences. This may be in the form of numeric ratings for movies, or of movies bought or previously viewed. Once we...