the whole point of it is to help you discover things you might not know about beforeso it' pretty cool you knowit gives individual moviesor booksor musicor whatevera chance to be discovered by people who might not have heard about them before soyou knownot only is it cool technologyit also kind of levels the playing field little bitand helps new items get discovered by the masses soit plays very important role in today' societyat least ' like to think sothere are few ways of doing thisand we'll look at the main ones in this user-based collaborative filtering firstlet' talk about recommending stuff based on your past behavior one technique is called user-based collaborative filteringand here' how it workscollaborative filteringby the wayis just fancy name for saying recommending stuff based on the combination of what you did and what everybody else didokaysoit' looking at your behavior and comparing that to everyone else' behaviorto arrive at the things that might be interesting to you that you haven' heard of yet the idea here is we build up matrix of everything that every user has ever boughtor viewedor ratedor whatever signal of interest that you want to base the system on so basicallywe end up with row for every user in our systemand that row contains all the things they did that might indicate some sort of interest in given product sopicture tablei have users for the rowsand each column is an itemokaythat might be moviea producta web pagewhateveryou can use this for many different things then use that matrix to compute the similarity between different users soi basically treat each row of this as vector and can compute the similarity between each vector of usersbased on their behavior two users who liked mostly the same things would be very similar to each other and can then sort this by those similarity scores if can find all the users similar to you based on their past behaviori can then find the users most similar to meand recommend stuff that they liked that didn' look at yet