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)
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
- Comprehensive R Archive Network (CRAN) / Decision trees
- confusion matrix / Evaluating data-mining algorithms
- content-based filtering
- about / Content-based filtering
- 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
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
- training set
- 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