Index

A

• accuracy metrics
  • precision / Evaluating the recommendations
  • recall / Evaluating the recommendations
• Akaike information criteria (AIC) / Evaluating data-mining algorithms
• area under the curve (AUC)
  • about / Identifying the most suitable model

B

• bagging (Bootstrap aggregating) / Bagging
• Bayesian information criteria (BIC) / Evaluating data-mining algorithms
• binary data
  • collaborative filtering / Collaborative filtering on binary data
  • item-based collaborative filtering / Item-based collaborative filtering on binary data
  • user-based collaborative filtering / User-based collaborative filtering on binary data

C

• case study
  • about / A case study
• cluster analysis
  • about / Cluster analysis
  • K-means cluster algorithm / Explaining the k-means cluster algorithm
• collaborative filtering
  • conclusions / Conclusions about collaborative filtering
  • limitations / Limitations of collaborative filtering
• collaborative filtering recommender systems
  • about / Collaborative filtering recommender systems
• Comprehensive R Archive Network (CRAN) / Decision trees
• confusion matrix / Evaluating data-mining algorithms
• content-based filtering
  • about / Content-based filtering
• content-based recommender systems
  • about / Content-based recommender systems
• cosine distance / Cosine distance

D

• data
  • preparing / Preparing the data
  • describing / Description of the data
  • record, types / Description of the data
  • importing / Importing the data
  • rating matrix, defining / Defining a rating matrix
  • item attributes, extracting / Extracting item attributes
• data-mining algorithms
  • evaluating / Evaluating data-mining algorithms
  • cross validation / Evaluating data-mining algorithms
  • regularization / Evaluating data-mining algorithms
  • confusion matrix / Evaluating data-mining algorithms
  • precision / Evaluating data-mining algorithms
  • Recall/Sensitivity / Evaluating data-mining algorithms
  • specificity / Evaluating data-mining algorithms
  • model comparison / Evaluating data-mining algorithms
• data analysis problem
  • solving / Solving a data analysis problem
• data exploration
  • about / Data exploration
  • nature of data, exploring / Exploring the nature of the data
  • values of rating, exploring / Exploring the values of the rating
  • viewed movies, exploring / Exploring which movies have been viewed
  • average ratings, exploring / Exploring the average ratings
  • matrix, visualizing / Visualizing the matrix
• data mining
  • techniques / Data mining techniques
• data preparation
  • about / Data preparation
  • most relevant data, selecting / Selecting the most relevant data
  • most relevant data, exploring / Exploring the most relevant data
  • data, normalizing / Normalizing the data
  • data, binarizing / Binarizing the data
  • for evaluating models / Preparing the data to evaluate the models
  • data, splitting / Splitting the data
  • data, bootstrapping / Bootstrapping data
  • k-fold, using / Using k-fold to validate models
• data preprocessing techniques
  • about / Data preprocessing techniques
  • similarity measures / Similarity measures
  • dimensionality reduction / Dimensionality reduction
• datasets
  • training set / Preparing the data to evaluate the models
  • testing set / Preparing the data to evaluate the models
• decision trees
  • about / Decision trees
  • using / Decision trees
• dimensionality reduction
  • about / Dimensionality reduction
  • Principal component analysis (PCA) / Principal component analysis

E

• ensemble methods
  • about / Ensemble methods
  • bagging (Bootstrap aggregating) / Bagging
  • random forests / Random forests
  • boosting / Boosting
• Euclidian distance / Euclidian distance
• evaluation techniques
  • about / Evaluation techniques

F

• False Positive Rate (FPR)
  • about / Evaluating the recommendations
• function
  • defining / Building a function to evaluate the model
  • data / Building a function to evaluate the model
  • k-fold parameters / Building a function to evaluate the model
  • model parameters / Building a function to evaluate the model

H

• hybrid recommender systems
  • about / Hybrid recommender systems
• hybrid systems
  • about / Hybrid systems

I

• item-based collaborative filtering
  • about / Item-based collaborative filtering
  • test set, defining / Defining the training and test sets
  • training set, defining / Defining the training and test sets
  • recommendation model, building / Building the recommendation model
  • recommendation model, exploring / Exploring the recommender model
  • recommender model, applying on test set / Applying the recommender model on the test set
  • on binary data / Item-based collaborative filtering on binary data

K

• k-fold
  • used, for validating models / Using k-fold to validate models
• K-means cluster algorithm
  • about / Explaining the k-means cluster algorithm
  • cluster assignment step / Explaining the k-means cluster algorithm
  • move centroid step / Explaining the k-means cluster algorithm
  • support vector machines (SVM) / Support vector machine
• knowledge-based recommender systems
  • about / Knowledge-based recommender systems, Knowledge-based recommender systems

M

• mean absolute error (MAE)
  • about / Evaluating the ratings
• mean squared error (MSE)
  • about / Evaluating the ratings
• monolithic hybrid systems
  • feature combination / Hybrid recommender systems
  • feature augmentation / Hybrid recommender systems

P

• Pearson correlation / Pearson correlation
• Principal component analysis (PCA) / Data preprocessing techniques, Principal component analysis
• proper model
  • identifying / Identifying the most suitable model
  • models, comparing / Comparing models
  • most proper model, identifying / Identifying the most suitable model
  • numeric parameter, optimizing / Optimizing a numeric parameter

R

• recommendation model
  • building / Building the model
  • optimizing / Evaluating and optimizing the model
  • evaluating / Evaluating and optimizing the model
  • evaluating, by building function / Building a function to evaluate the model
  • parameters, optimizing / Optimizing the model parameters
• recommenderlab
  • used, for building recommender systems / R package for recommendation – recommenderlab
  • datasets / Datasets
  • MovieLense dataset / Jester5k, MSWeb, and MovieLense
  • MSWeb dataset / Jester5k, MSWeb, and MovieLense
  • Jester5k dataset / Jester5k, MSWeb, and MovieLense
  • realRatingMatrix class, defining / The class for rating matrices
  • similarity matrix, computing / Computing the similarity matrix
  • recommendation models, displaying / Recommendation models
• recommender systems
  • about / Understanding recommender systems
  • case study / A case study
  • future scope / The future scope
  • building, recommenderlab used / R package for recommendation – recommenderlab
• recommender techniques
  • evaluating / Evaluating recommender techniques
  • ratings, evaluating / Evaluating the ratings
  • recommendations, evaluating / Evaluating the recommendations
• root mean square error (RMSE)
  • about / Evaluating the ratings
• R package
  • recommenderlab / R package for recommendation – recommenderlab

S

• similarity measures
  • about / Similarity measures
  • Euclidian distance / Euclidian distance
  • Cosine distance / Cosine distance
  • Pearson correlation / Pearson correlation
• singular value decomposition (SVD) / Data preprocessing techniques
• support vector machines (SVM) / Support vector machine

T

• test set
  • about / Defining the training and test sets
• training set
  • about / Defining the training and test sets
• True Positive Rate (TPR)
  • about / Evaluating the recommendations

U

• user-based collaborative filtering
  • about / User-based collaborative filtering
  • recommendation model, building / Building the recommendation model
  • recommender model, applying on test set / Applying the recommender model on the test set
  • on binary data / Collaborative filtering on binary data, User-based collaborative filtering on binary data
  • data preparation / Data preparation