ThinkML: Redefining Learning
“Grab your popcorn and let’s get started..”
“Predicting the future isn’t magic, but a combination of artificial intelligence and machine learning.”
In today’s fast-paced world, it’s important to take note of the fact that what has changed in the past years, isn’t just how we perceive change but the rate at which change occurs I.e. the rate at which new technologies filter into our markets and often wittingly in our mind space ( sometimes quite literally uninvited!)
Machine Learning, in addition to Deep Learning and Artificial Intelligence, is quite often heard to be at the forefront of whatever perceived revolution is going to take technology by a storm. In today’s world, we have come to realize that often trends in new technology barely last long enough to make any significant impact on the lives of individuals. Yet in this day of changing fads and reduced shelf life of services and technologies, ML has not only stayed in fashion but is only predicted to grow in the coming years or so.
Having a multitude of applications in today’s time, we now move towards discussing the impact of machine learning in an important area of leisure/procreation and social activities I.e. movies. We live in an era with no dearth of content. Content production is the process of developing and creating visual or written assets, like videos, eBooks, blog posts, white papers, or infographics. You name it, we consume it! And now when we realize just how much content is produced on various mediums vying for our attention, naturally the next question that arises is how we are going to choose the content that appeals the most to us among all of it out there for the world to consume?
The kind of content, generally received well by the audience, goes a long way in acting as a compass for society and determining, not only the way we are headed but also where we stand at the present moment. But what filters can be applied to choose content that shall appeal to our tastes? Really how do we choose? Voila! Machine Learning to the rescue, in the form of a — ‘wait for it’! — smart movie recommendation system. It’s estimated that the world cinema has released more than 500,000 movies — a number beyond individual imagination to say the least. With such an enormous range and variety to choose from, developing and improving recommendation systems with ML was a crucial step to help streamline this selection.
Having established why movie recommendation is important in today’s day and age, we now discuss how it’s done. A movie recommendation is important in our social life due to its significance in providing enhanced entertainment. This system acts on the principle of suggesting a set of movies to users based on their interest, or the popularity enjoyed by the movies in question. Thus recommendation systems are used in suggesting items to purchase or to see, directing users towards content that suits their needs perfectly, simultaneously cutting down a large database of “irrelevant” options saving their time in question by guiding their choices. A recommender system, or a recommendation system (sometimes referred to as a platform or engine), is a subset of information filtering systems that help in predicting the “preference” of a user towards a particular piece of content. MOVREC assists users in finding movies of their choices based on the user experience of many other users on the same platform, efficiently collating the data effectively, saving time often wasted in useless browsing.
Such systems are also utilized in a variety of areas. Their common layman applications happen to be as playlist generators for video and music services like Netflix, YouTube, and Spotify as well as product recommenders for Amazon. The algorithms percolate deeper than you might think and are also used as content recommenders for social media platforms like Facebook and Twitter.
In the field of machine learning, data classification methods use various strategies while organizing and classifying data. Such system classifiers frequently require training data. Certain key methods that are popularly in use today: Collaborative filtering, Content-based filtering, Multi-criteria recommender systems along with Risk-aware recommender systems among others.
Using the above-mentioned Algorithms, one can sort through content faster than it has been ever possible. For example, Matrix factorization is a bunch of collaborative filtering algorithms used in recommender systems. This family of methods became widely popular at the time of the Netflix prize challenge owing in part to how effective it was. Due to this very large-scale effectiveness, Data scientists are all set to explore our behavioral patterns using ML and the ones of the movies allowing them to work on sophisticated predictive systems for die-hard cinema-goers. The bigger the choice, the harder it is to make the final decision. Particularly true for modern movie fans, who face this “dilemma” as they have several options to choose from, not to mention several platforms on which said choices in the content are available.
In such a scenario, ML acts as a Godsend proving to be a vital technological solution making our lives easier, not to mention making our streaming experience a lot smoother. And the more these systems evolve, the more advanced ML techniques we have at our disposal that generate the most accurate content for users in terms of what they are looking for, delivering exactly that to them.
So, the next time you decide to binge-watch a series or grab some popcorn for movie time, you have ML to thank for those quick-time-saving recommendations that let you tune into content, you are most predicted to enjoy using that data stored on the platform in the form of previously watched or your likes and dislikes. So thank ML and enjoy your popcorn!
To better understand how ML is making a major impact in which content we are being open to via a plethora of mediums at our disposal tune in to ThinkML: Learning Redefined on 15th July 2022.
Save the date and till then have fun at the movies!
