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