Monday, 2 December 2019

Part 4/4: Playing with the Carrefour shopping receipts

Alright here goes part 4 of 4 of my work on the Carrefour shopping receipts dataset. I realize we have come quite a way - and for me too there has been a lot to talk about and explore in these blogposts. Even then I feel like there's a ton of other interesting questions that we could ask and answer - but that would lead us too far.
Just to recap:

Now, in this final part of this series, I want to see if we can do some more analytical work with this dataset, for example by applying some algorithms to it. More specifically, I want to use some of our graph similarity algorithms to figure out which products are supposedly similar to one another - and do that along multiple axes. 

People have written long and complicated doctorates about the best way to calculate and establish similarities in graphs - and most of it is very much beyond me and my reptile math brain. But one thing is clear: many of the algorithms have very different approaches to doing this, and there are good reasons for wanting to choose or abandon one or the other. However, in our daily Neo4j work, we have seen some particularly interesting results with the Jaccard similarity algorithm, which is part of the algos plugin to Neo4j.

Jaccard similarity

The simple explanation of what Jaccard similarity does, is that it calculates a coefficient that compares members of two sets to see which members are shared and which are very different. So it's a measure of similarity for two sets of data - with a range from 0% (not similar at all) to 100% (identical). Higher scores mean higher similarity between the two populations. Jaccard similarity is sometimes referred to as "Intersection over Union", as explained like this:

I borrowed most of this explanation from the inevitable Wikipedia of course. You can find the Neo4j algo library that contains this algorithm over here.