How to build a simple recommendation system? I have seen some algorithms but it is so difficult to implement I wish their is practical description to implement the most simple algorithm?
i have these three tables
Users userid username 1 aaa 2 bbb and
products productid productname 1 laptop 2 mobile phone 3 car and
users_products userid productid 1 1 1 3 3 2 2 3 so I want to be able recommend items for each of the users depending on the items they purchased and other users’ items
I knew it should something like calculating the similarites between users and then see their prosucts but how can be this done and stored in a database because this will require a table with something like this
1 2 3 4 5 6 << users' ids 1) 1 .4 .2 .3 .8 .4 2) .3 1 .5 .7 .3 .9 3) .4 .4 1 .8 .2 .3 4) .6 .6 .6 1 .4 .2 5) .8 .7 .4 .2 1 .3 6) 1 .4 .6 .7 .9 1 ^ ^ users' ids so how can similarty beween users calculated? and how could this complex data stored in ad database? (it requires a table with column for every user)? thanks
How you want to actually store the recommendations is as a question completely unrelated to how one would actually implement a recommendation engine. I leave that to your database architecture. On to the recommending.
You said ‘simple’, so a Pearson correlation coefficient might be the thing you need to read up on.
Calculating such a thing is dead simple. Concept, example code.