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How Big Data is used in Recommendation Systems to change our lives


A Recommendation systems have impacted or even redefined our lives in many ways. It works in well-defined, logical phases which are data collection, ratings, and filtering.



Role of big data

As stated earlier, big data drives what Recommenders do primarily. Recommenders cannot do a thing without the constant supply of data. However, the role of big data goes beyond just data. It is clear that the above operations require a high-capacity CPU which can work for hours. To realize this, Hadoop can be used. To reduce the manual work needed to code, identify right algorithms, similarity methods and other tasks, Mahout could be used.

Mahout is a library that comprises machine learning algorithms. It provides a set of options to choose recommendation algorithm, choosing n-nearest neighbors and similarity methods. Though it is a standard Java class, it operates purely on Hadoop.

To make your tasks even easier, you can use a tool known as PredictionIO which bundles both Mahout and Hadoop and what more, it provides a nice user interface.

So, the role of big data can be summed in providing meaningful, actionable data fast and providing necessary setup to quickly process the data. It is obvious that traditional technologies are not meant to process such large volumes of data so quickly. So, it will not suffice to just have big data in order to provide strong recommendations.

The Amazon use case

How Amazon uses the powerful duo of big data and Recommendation System is worth a study. Amazon has been in certain ways a pioneer of ecommerce but more important than that accolade is how it is driving its revenue up by providing more and more effective recommendations.

Buying can be both impulsive and planned and Amazon is smartly tapping into the impulsive shopper’s mind by providing relevant and useful product recommendations. For that, it is relentlessly working on making its Recommendation engine more powerful. Shopping has a connection with psychology. Shoppers buy for instant gratification, instant mood uplift, social esteem and reasons not even known to them clearly.

Amazon is smart enough to take these factors into account. And now, it is working on a system called predictive dispatch which means that its Recommendation engine can predict what the customer is going to buy and make arrangements for a speedy dispatch.

What makes Amazon’s achievements more creditable is the fact that unlike Facebook — which also relies a lot on big data — which knows a lot of details about its subscribers, all Amazon knows about its customers are the spending patterns.

Amazon has been cashing on this knowledge smartly in an attempt to get more out of your pockets. It is a difficult job to analyze spending patterns, likes, product preferences and provide effective recommendations just on that basis. And now, Amazon is trying to make available its tools and technologies that use big data and Recommendation systems so effectively for sale to other corporations that use big data. So, Amazon’s product ads will start to appear more frequently on other websites as well and that is going to drive up sales.

The following image shows how big companies have been using the power of big data and Recommendation engines.

recommendation-engine-applications


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