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

Item-based and user-based recommenders

Within the field of collaborative filtering, we can usefully make the distinction between two types of filtering—item-based and user-based recommenders. With item-based recommenders, we take a set of items that a user has already rated highly and look for other items that are similar. The process is visualized in the next diagram:

Item-based and user-based recommenders

A recommender might recommend item B, based on the information presented in the diagram, since it's similar to two items that are already highly rated.

We can contrast this approach to the process of a user-based recommendation shown in the following diagram. A user-based recommendation aims to identify users with similar tastes to the user in question to recommend items that they have rated highly, but which the user has not already rated.

Item-based and user-based recommenders

The user-based recommender is likely to recommend item B, because it has been rated highly by two other users with similar taste. We'll be implementing both kinds of recommenders...