Understanding Recommendation Engines

Recommendations are an important aspect of web-based channels, and what was popularized by Amazon ("You may also like this" or "People who bought this, also bought this"), has now become quite a norm. Social media has further added to the importance and relevance of recommendations, and there's no dirth of research that establishes that marketers and retailers are looking to spend big bucks to get people and their connections recommend and rate their products.

With the ever increasing choices for anything on the web today, it is often that suggestions and recommendations are helpful to narrow down and make the best choice. But, what seems like a simple thing (and maybe a tad intrusive to some) showing in a sidebar or below your item of interest, there's a lot of computing and engineering that goes into this whole data crunching and processing to accurately provide "also liked/bought..." suggestions, in a fraction of a second!

The following interactive mindmap provides a quick overview into the workings of recommendation engines.
(Tip: The following is an interactive mindmap - use the options below to navigate or optimize your view).