12 October 2017

Instantaneously Trained Neural Networks

Instantaneously Trained Neural Networks

Types of Recommendation Engines

Neighborhood-Based Recommendations:
  • User-Based Collaborative Filtering
  • Item-Based Collaborative Filtering
Personalized Recommendations:
  • Context-Aware
  • Context-Based
Model-Based Recommendations:
  • ML-Based
  • SVM/KNN Classification
  • Matrix Factorization
  • Singular Value Decomposition
  • Alternating Least Squares
  • Hybrid (Methods: Weighted, Mixed, Switching, Cascade, Feature Combination, Feature Augmentation, Meta-Level, etc)
ML Techniques Applied to Recommendations:
  • Euclidean Distance
  • Cosine Similarity
  • Jaccard Similarity
  • Pearson Correlation Coefficient
  • Matrix Factorization
  • Alternating Least Squares
  • Singular Value Decomposition
  • Linear Regression
  • Classification Models
  • K-Means Clustering
  • Principal Component Analysis
  • Term Frequency
  • Term Frequency-Inverse Document Frequency
Evaluations:
  • Root-Mean-Square Error
  • Mean Absolute Error
  • Precision and Recall