The thing that I focussed on was the topic of recommendations and recommender systems. In a world where content is king, and consumers are always looking for specific deals that would fit their profile best, these systems are going to become critically important.
In my mind, these systems always consist of two parts:
- a Pattern discovery system, where you - somehow - figure out what the patterns are that you are looking for. Could be by asking a domain expert, but could just as well be through some advanced machine learning algorithm.
- a Pattern application system, where you start operationalising the patterns that you have found and applying them in real world applications, either in batch, or in near-real-time.
I believe that graphs and graph databases offer tremendous potential for this domain, and have tried to illustrate that in this talk. The key points being that
- Graphs excel at making these recommendations in real time. No more batch precalculations - just deliver the patterns you uncover as they develop.
- Graphs benefit from some of the most fascinating data analytics techniques out there: triadic closures, centrality measures, betweenness measures, PageRank scoring - all of which help you determine what parts of the graph really matter, or not.
- Graphs benefit from some operational advantages (among which: graph locality in your queries) to make them operationally very efficient.
I am sure I am missing stuff here, but these seem to be the most important pieces, to me.
The slides are on SlideShare, and included below:
The demonstration that goes with this was also recorded below. It's a little long, but I hope it's still interesting:
I hope this was a useful illustration for you of this fascinating use case. Any feedback - as always - greatly appreciated.