Hi Hussein, Apologies in advance but the answer is: it depends.   Most people think the go-to algorithm for recommendation engines should always be collaborative filtering. This algorithm is very famous becuase it was used to solve the Netflix challenge. This means that if you are trying to solve a similar problem, you should probably start with collaborative filtering, which is not available in Enterprise Miner.   However, that does not mean that you cannot build a recommendation engine based on some other algorithm. Several customers build very successful recommendation engines based on measures such as simmilarity, nearest neighbor, or SVDs (who would think Text Mining nodes calculate those!?). If you need to update your model constantly, you may not want to use EM, unless you are realy good at programming and running Batch code.   The painful part is that you might have to try several algorithms and there is not a "Model Comparison" node for which one suits best your recommendation purposes. But as often happens in data mining, you learn as much from what does not work as you do from what actually works.   If I had to start somewhere, I would try the association node (if my items are in a transaction format), or the link analysis node. Here a paper that vouches for using other algorithms and other methods: www.springer.com/cda/content/document/cda_downloaddocument/9780387858197-c1.pdf   Good luck! -Miguel ... View more