Building Practical Recommendation Engines – Part 1 скачать в хорошем качестве

Building Practical Recommendation Engines – Part 1

Make Intelligent predictions with real-world projects A step-by-step guide to building recommendation engines in no time Get to grips with the best tools available on the market to create efficient recommendation systems This hands-on tutorial shows you how to implement different tools for recommendation engines, when to use a particular recommendation engine, and how Learning Discover the tools needed to build recommendation engines Dive into the various techniques of recommender systems such as collaborative, content-based, and cross-recommendations Create efficient decision-making systems that will ease your work About A recommendation engine (sometimes referred to as a recommender system) is a tool that lets algorithm developers predict what a user may or may not like among a list of given items. Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are movies, music, news, books, research articles, search queries, social tags, and products in general. This video starts with an introduction to recommendation systems and its applications. You will then start building recommendation engines straight away from the very basics. As you move along, you will learn to build recommender systems with popular frameworks such as R, Python, and more. You will get an insight into the pros and cons of different recommendation engines and when to use which recommendation. With the help of this course, you will quickly get up and running with Recommender systems. You will create recommendation engines of varying complexities, ranging from a simple recommendation engine to real-time recommendation engines. Introduction to recommendation engines The Course Overview Recommendation engine definition Types of recommender systems Evolution of recommender systems with technology Building your first recommendation engine Loading and formatting data Calculating similarity between users Predicting the unknown ratings for users Recommendation engines explained Nearest neighborhood-based recommendation engines Content-based recommender system Context-aware recommender system Hybrid recommender systems Model-based recommender systems Convolutional neural networks Neighborhood-based techniques Mathematical model techniques Machine learning techniques Classification models Clustering techniques and dimensionality reduction. Vector space models Evaluation techniques Building Collaborative Filtering Recommendation Engines Installing the recommenderlab package in RStudio Datasets available in the recommenderlab package Exploring the dataset andbuilding user-based collaborative filtering Building an item-based recommender model Collaborative filtering using Python Data exploration User-based collaborative filtering with the k-nearest neighbors Item-based recommendations