An open science approach to exploring time-accuracy trade-offs in recommender systems
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Recommender Systems have become an integral part of our consumer dominated world. With the evolution of Big Data and the exponential expansion of consumerism it is imperative that we design efficient recommender systems. Collaborative Filtering techniques have been popularly adopted by researchers for developing robust recommender systems. In this paper we discuss some of the techniques that fall under the Collaborative Filtering umbrella and have been implemented within the ScalaTion big data analytics framework. Apart from discussing the implementation we analyze the execution time and accuracy of these techniques. This analysis has been performed to explore trade-offs between time and accuracy that occur while performing predictions using these techniques.