I am working on building a recommendation system.
And I found that neo4j and mahout are the best available solution for building recommendation system.
But I am unable to clearly distinguish between the advantage and issues related to these two.
Could anyone provide me a comparison between neo4j and mahout ?
Which is best for building a recommendation system ?
This is a deep question that can't be specifically answered without a lot more information about what kind of recommendation system you want, what kind of scalability you need, what your hardware/software/platform configuration looks like, and so on.
But let me give you this to consider: the issue isn't a comparison of neo4j and mahout, it's a comparison between neo4j and hadoop. Mahout would generally run on top of a hadoop infrastructure, so all of the assumptions and implementation constraints that come with hadoop are for the most part more important to consider than just the raw graph layer implementation differences.
You should read up more on hadoop - the approach it takes to data storage and management is completely different than what neo4j does. Both have their pros and cons, and entire books can and have been written about the pros and cons of the approach hadoop takes. The brief version is that if you need massive scalability and you have huge datasets, hadoop is set up to work well in those environments; neo4j doesn't promise scalability beyond billions or tens of billions of nodes, and the kinds of scalability that it does promise are quite different than hadoop.
You should do some more reading on this topic, or further refine your question to prevent people from getting into opinionated flamewars about what is better. Maybe there is a better choice for you in consideration of all of your details, but we don't have those details.