Mimic a Mahout like system I have a data set, in excel format, with account names, reported symptoms, a determined root cause and a date in month year format for each row. I am trying to implement a mahout like system with a purpose of determining the likelihood symptoms an account can report by doing a user based similarity kind of a thing. Technically, I am just hoping to tweak the recommendation system into a deterministic system to spot out the probable symptoms an account can report on. Instead of ratings, I can get the frequency of symptoms by accounts. Is it possible to use a programming language or any other software to build such system?

Here is an example:

Account : X Symptoms : AB, AD, AB, AB

Account : Y  Symptoms : AE, AE, AB, AB, EA

For the sake of this example, let's assume that all the dates are this month. 

O/P: Account : X Symptom: AE

Here both of them have reported AB 2 or more times. I could fix such number as a threshold to look for probable symptoms.
This seems to me as the plain old recommendation problem. The Accounts are the USERS and the Symptoms are the ITEMS. Each time an Account shows a particular Symptom your system will increment a count value.

Creating the following dataset:

ACCOUNT, SYMPTOM, COUNT

Now you can use/implement any sort of recommender system (Mahout is only an option, have you seen MyMediaLite) or you can even implement yours. 

Let's reuse your ideas:
* You'd like to use a user-based similarity 
* If an Account has shown 2 or more times the same symptom it seems to be important

So you can filter out the Account, Symptom pairs with less than 2 counts, and with the rest you create the following datasets:


User, Item dataset:


ACCOUNT, SYMPTOM


Table with a unique column containing all Users:


ACCOUNT


Table with a unique column containing all Items:


SYMPTOM

Now you can use directly the User-KNN algorithm from MyMediaLite.

With the recommender model already trained you can pass any ACCOUNT as input and it will give you a ranked list of the most probable SYMPTOMS that might appear.

Obs.: Initially ignore the time, then you could use it to partition your data in past/future and evaluate the recommendation in a more realistic way. ;-)