Recommendation Engines:

Neighborhood Based Collaborative Filtering: common ways to measure the similarity between two users (or two items) including:

• Measures of Similarity

• Pearson’s correlation coefficient

• Spearman’s correlation coefficient

• Kendall’s Tau

• Euclidean Distance

• Manhattan Distance

Recommendation System:

• Collaborative Filtering

• Item Weighting in Collaborative

Techniques invovled :

1. Dimensionality reduction

   • Singular value Decomposition

     1. SVD non-negative propery

     2. python SVD from scratch

     3. Missing values SVD

Articles to read :

• Evaluation recommender systems

• AirBnB recommendation

• Location based recommendation systems

• Deep learning

• Objective setting of recommendation systems

• Scales for response collection

• SVD system introduction

• Funk SVD working

Handling Issues :

• Cold User Problem

• Comparison of user vs item collaborative filtering

• Recommendation using matrix Factorization