Understanding how Recommendation Engines are Contributing Towards Self-awareness and Self-Growthby Astha Oriel November 21, 2020 0 comments
A recommendation engine is a system that suggests products and services based on the analysis of the user data.
Internet and Social Media is full of recommended products and services that can potentially benefit the customer. Many organizations employ recommendation engines so that their products can be reviewed and bought by the user. It is an extended way through which companies anticipate creating the brand name, by compelling the customers to choose from a variety of option. A recommendation engine is a system that suggests products and services based on the analysis of the user data. An example of this would be how web-shops like Amazon recommend the beauty products, apparels and accessories based on the customer’s engagement. If an individual is a make-up enthusiast or a bibliophile, the probability of the individual buying recommended beauty product or book increases.
Similarly, YouTube is flooded with recommendations about various educational courses. At many instances, the recommended products can drive to the personal growth of an individual. Personal growth Apps like Headspace and Mindvalley, which are often promoted across social media platforms, has lured many individuals to invest in personality development products. Similarly, courses from Udemy and Coursera have driven individuals to expand their knowledge about data science and artificial intelligence. And while recommendation engines are always viewed under the light of profitable business, it is an often ignored and undermined the fact that recommendation engines are taking the user to a path of self-discovery and growth.
The Netflix documentary “The Social Dilemma”, talked in brief about how recommendation engines along with artificial intelligence are disrupting the world today. It explained many aspects but failed to point out how recommendation engines are accelerating self-growth and discovery.
Humans are driven by recommendations. We seek recommendations for buying a house, selecting a college, starting a business or even before medical procedures. Recommendation engines do just that in a virtual world, where users are looking for options to improve their life. A recommended engine is tailored through the concept of neuroscience, and psychology, hence it promptly understands the intention of the choices made by the customer.
Recommendation engines are data-driven. Powered by machine learning algorithms, recommendation engine collaborates in making individual choices better. It makes individuals more aware of the choices that they make and how they can best benefit from such choices. Certainly, businesses heavily deploy recommendation engines to boost revenue and sales and for diverse customer engagement. The businesses collect, store, analyze and filter the data, based on the log-in and log-out activities of the user. Through predictive analysis, recommendation engines know better what an individual is likely to buy, and recommend the product according to the preference of the user.
For example, if a customer is buying soft pillows from different websites, it can be indicative that the person is looking for a remedy for the ailment in the head and neck region. As often customers are unable to find what they are looking online, recommendation engines prompt them with advanced options that can suit their choices. Furthermore, the recommendation engine offers personalized products to the user. Additionally, it assists users to navigate and discover new products. This is where the concept of discovering preferences and choices get emphasized. An example of this would be if an individual is interested in writing, but has no idea from where to start initially, a simple recommendation of various journals would prompt the user to buy the product, thus venturing into the discovery of writing.
There are ample use cases of recommendation engines, which drive individuals to the pathway of self-discovery. While we acknowledge how recommendation engines collate in business growth, we must also address that they are, in one way or the other, improving human life.
After all, recommendation engines know more than individuals know about themselves.