Why Recommendation System Is Big Data's Shining Star?
In the big data world, recommendation system is inreasingly becoming popular. The reasons is that while other big data products such as the data used to benchmarch and those used for predictions require a decision maker, the data used for recommendation and filter systems is an automated one and does not need an analyst.
Due to their nature of work, these systems are becoming an integral part of online shops or e-commerce in order to generate an excellent user experience, and enable consumers to seamlessly browse through customized product offerings. These systems connect the consumers with potential products to purchase by correlating the product contents and the expressed opinion.
Recommender systems are blue printed and custom designed based on fundamental algorithms. Most of the blue prints are grouped around data analysis of vast amounts of historical choices and or purchase patterns of existing customer profiles and are designed to suggest products based on past choices.
Consumer profiles: These are usually a composite data set including demographic information and a collection of answers for well structured questionnaire. Such planned profiles get associated to matching products through well designed customer interaction models.
Collaborative filtering: this system recommends products based on statistics driven purchase patterns of similarly profiled customers built around supervised or unsupervised techniques. Collaborative filtering built around neighborhood methods and latent factor models are designed to investigate the affinity between consumers’ profiles and product interdependencies to diagnose new user-item associations.
Crowd-sourced intelligence: It is as an equivalent term is used to generalize the user’s interaction on a purchase activity, and recommend products to other profiles based on crowd sourced responses. This is evident from the active presence of leading brands in social network forums inviting customers to share their experience on their product portfolio.
Content-based filtering: Here the blue printed system uses a detailed profile of the individual user based on their previous purchase, likes, searches, tweets, and blogs. Such data collections are essential in profile building activities which are evident from the various social media feedback systems rating systems deployed by various online products and free web space provided by organizations.
Customers have a unique attribute of peer consultation to take decisions and make purchases. Such decisions are mostly network peer driven and is centered on group dynamics and expressed opinions on social sites. Hence a recommender engine built around collaborative filtering with collated and summarized social datasets influences the purchase behavior of customers leading to business.
These recommender engines can be normalized for customer management systems, hospitals, doctors, movies, insurance plans, transport systems, banking systems, educational programs, tourist programs etc. Mobile centric recommender systems have started surfacing based on huge volumes of targeted mobile apps giving a new dimension.
When the social sites are web scrapped, the collected data is drilled through a cleaning process in order to push them through the recommender system. The system is designed to work on statistical designs in order to address the fuzziness and vagueness inherent in the collated data sets.
Recommender engines are establishing domain level leadership. They have emerged as an alternative for word of mouth feedback system to anywhere feedback permitting individual mobility coupled with a spectrum of choices. Some of the experimental works were built around it are provider quality, recommending doctors, Marketing and CRM call return on investment and text analytics built on open source technology stack and in-house customized stacks. A couple of interesting prototypes have been built around call management analytics, fraud analytics, sales force analytics and provider quality analytics.
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