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Today's web commercial applications demand more powerful recommendation systems due to the rapid increase in the number of both consumers and available products. Searching for the best algorithm with the highest accuracy and realistic complexity is, most of the time, a very costly process in terms of both time and resources. In this paper we suggest an alternative framework called Hydra which enables the virtual fusion of any and as many currently available recommendation algorithms in such a distributed manner that algorithms' complexities are not summarized but parallelized. Therefore, we utilize the available algorithms and technologies aiming to achieve better accuracy in order to surpass even the most state of the art algorithms. In addition, Hydra can be used to find how algorithms interact with each other in order to estimate the resulting accuracy towards inventing a more precise algorithm diminishing the risk of a failed investment. Hydra can be adjusted and integrated in any recommendation application while it is also open to new functionalities which can be embedded easily and in a transparent manner.

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