Holistic User Context-Aware Recommender Algorithm

Table 1

Comparison of similarity measure metric functions.

Jaccard similarity function	Euclidean distance function	Pearson correlation function
Measuring similarity of finite sets	Measure distance between data points	Measure linear correlation
Can work with strings	Work with vectors of real numbers	Corelational real numbers
Easy to work with social media profiles	Computationally expensive, when dealing with social media data	Weak on complex relationships
Best for content-based recommendations	Good for collaborative filtering algorithms	Good for collaborative filtering