| Description |
1 online resource : illustrations. |
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text file |
| Series |
Community experience distilled
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Community experience distilled.
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| Note |
Includes index. |
| Contents |
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. |
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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. |
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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. |
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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. |
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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. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Recommender systems (Information filtering)
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Recommender systems (Information filtering) |
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Machine learning.
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Machine learning. |
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R (Computer program language)
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R (Computer program language) |
| Genre/Form |
Electronic books.
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| Added Author |
Usuelli, Michele, author.
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| Other Form: |
Print version: Gorakala, Suresh K. Building a Recommendation System with R. Birmingham : Packt Publishing Ltd, ©2015 9781783554492 |
| ISBN |
9781783554508 (electronic book) |
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1783554509 (electronic book) |
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1783554495 |
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9781783554492 |
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