Building a recommendation system with R: learn the art of building robust and powerful recommendation engines using R

Description

Cover; Copyright; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with Recommender Systems; Understanding recommender systems; The structure of the book; Collaborative filtering recommender systems; Content-based recommender systems; Knowledge-based recommender systems; Hybrid systems; Evaluation techniques; A case study; The future scope; Summary; Chapter 2: Data Mining Techniques Used in Recommender Systems; Solving a data analysis problem; Data preprocessing techniques; Similarity measures; Euclidian distance. Cover Copyright Credits About the Authors About the Reviewer www.PacktPub.com Table of Contents Preface Chapter 1: Getting Started with Recommender Systems Understanding recommender systems The structure of the book Collaborative filtering recommender systems Content-based recommender systems Knowledge-based recommender systems Hybrid systems Evaluation techniques A case study The future scope Summary Chapter 2: Data Mining Techniques Used in Recommender Systems Solving a data analysis problem Data preprocessing techniques Similarity measures Euclidian distance. Cosine distancePearson correlation Dimensionality reduction Principal component analysis Data mining techniques Cluster analysis Explaining the k-means cluster algorithm Support vector machine Decision trees Ensemble methods Bagging Random forests Boosting Evaluating data-mining algorithms Summary Chapter 3: Recommender Systems R package for recommendation -- recommenderlab Datasets Jester5k, MSWeb, and MovieLense The class for rating matrices Computing the similarity matrix Recommendation models Data exploration Exploring the nature of the data. Exploring the values of the ratingExploring which movies have been viewed Exploring the average ratings Visualizing the matrix Data preparation Selecting the most relevant data Exploring the most relevant data Normalizing the data Binarizing the data Item-based collaborative filtering Defining the training and test sets Building the recommendation model Exploring the recommender model Applying the recommender model on the test set User-based collaborative filtering Building the recommendation model Applying the recommender model on the test set. Collaborative filtering on binary dataData preparation Item-based collaborative filtering on binary data User-based collaborative filtering on binary data Conclusions about collaborative filtering Limitations of collaborative filtering Content-based filtering Hybrid recommender systems Knowledge-based recommender systems Summary Chapter 4: Evaluating the Recommender Systems Preparing the data to evaluate the models Splitting the data Bootstrapping data Using k-fold to validate models Evaluating recommender techniques Evaluating the ratings Evaluating the recommendations. Identifying the most suitable modelComparing models Identifying the most suitable model Optimizing a numeric parameter Summary Chapter 5: Case Study -- Building Your Own Recommendation Engine Preparing the data Description of the data Importing the data Defining a rating matrix Extracting item attributes Building the model Evaluating and optimizing the model Building a function to evaluate the model Optimizing the model parameters Summary Appendix: References Index.