Common Applications of Jaccard Similarity

Jaccard Similarity is used in multiple data science and machine learning applications. Some of the frequent use cases encountered in real life include :

• Text mining: finding the similarity between two text documents based on the number of terms used in both documents.
• E-Commerce: finding similar customers via their purchase history from a sales database of thousands of customers and millions of items.
• Recommendation Systems: Finding similar customers based on ratings and reviews e.g., Movie recommendation algorithms, Product recommendation, diet recommendation, matrimony recommendations, etc.

Jaccard Similarity Formula and Concepts:

Jaccard Similarity value ranges from 0 to 1. The higher the number, the more similar are the datasets with each other. Although it is easy to interpret but is extremely sensitive to smaller sample datasets and can give erroneous results hence one needs to be careful while comprehending results.

Jaccard Similarity also called as Jaccard Index or Jaccard Coefficient is a simple measure to represent the similarity between data samples. The similarity is computed as the ratio of the length of the intersection within data samples to the length of the union of the data samples. 

It is represented as – 

J(A, B) =  |A Ո B| / |A U B|

It is used to find the similarity or overlap between the two binary vectors or numeric vectors or strings. It can be represented as J. There is also a closely related term associated with Jaccard Similarity which is called Jaccard Dissimilarity or Jaccard Distance. Jaccard Distance is a measure of dissimilarity between data samples and can be represented as (1 – J)  where J is Jaccard Similarity.