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.
Limits of Recommendation systems
For all their efficiencies, Recommendation Systems are not a full proof system. Recommenders have been known to suffer from the following limitations:
- Recommenders depend totally on data and their hirers must constantly supply them with large volumes of data. That is why; smaller firms are more disadvantaged then the bigger firms such as Google and Amazon.
- Recommenders may find it difficult to exactly identify user choice patterns if the user preferences tend to vary quickly, as in fashion. Recommenders depend a lot on historic data but that may not be suitable for certain product niches.
- Recommenders face problems with unpredictable items. For example, there are certain movie types that evoke extreme reactions such as love or hate. It is extremely difficult to provide recommendations for such items.
While big data and Recommendation engines have already proved an extremely useful combination for big corporations, it raises a question of whether companies with smaller budgets can afford such investments. It is encouraging for such companies that big data tools and technologies are relatively more affordable. Product recommendations are extremely important to provide a good user experience from the customer’s viewpoint. Also, from the company’s viewpoint, it takes into account unknown factors that can make a customer buy products which might seem unlikely. As the above image shows, the power of Recommenders is getting bigger.
Bio: Kaushik Pal (www.techalpine.com) has 16 years of experience as a technical architect and software consultant in enterprise application and product development. He has interest in new technology and innovation area along with technical writing. His main focuses are on web architecture, web technologies, java/j2ee, Open source, big data and semantic technologies.