There are a LOT of algorithms for recommender systems. In this post, I will try to list a part of them (and hopefully a large part). I will not introduce what are
recommender system, what they are for, etc ... I have a post
introduce the concept a little, but the goal here is to go straight to the point. Thus I will present each algorithm in few lines in order to just to have the big
picture of each algorithm is solving and what they model. In a second part of this post, I'll try to compared them on different
datasets and give the code on my github
, but this will be a very painful work !
I'll try to have a dozen of algorithm codes before moving to experiments parts.
There are :
- 11 algorithms named so far
- 5 algorithms detailed so far
- 0 algorithms tested experimentaly so far
- 5 codes available