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Building a recommendation system with R : learn the art of building robust and powerful recommendation engines using R /
Learn the art of building robust and powerful recommendation engines using R About This Book Learn to exploit various data mining techniques Understand some of the most popular recommendation techniques This is a step-by-step guide full of real-world examples to help you build and optimize recommend...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
[2015]
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- 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.