Hi @Venki, can you elaborate on what you are trying to do and which part you are struggling with?
Generally speaking you'll want to have an application that consumes recommendations (makes queries to a hosted recommender model over the internet) and has application logic to present those recommendations to the user. Within your application code, you can know which user is currently logged in, and thus which user ID to query the recommender with.
To get started building recommenders with GraphLab Create, take a look at this video or the gallery items related to recommenders. Many of these examples could get you up and running with a Recommender model that can be queried for a user.
Once you have a recommender, to integrate it with an application you'll probably want to deploy it to a Predictive Service. See the Predictive Services chapter of the userguide for more info on this; but the basic idea is that you are taking the trained Recommender (a Python object) and putting on a reliable, fault-tolerant hosted service, so that it can be queried from a production application. The hosted model is queryable using a REST-like interface over HTTP, so any application that can make HTTP requests should be able to consume it.