Technology continues to enhance our everyday experiences and this has especially been achieved through the emergent fields of Data Science, Artificial Intelligence and more specialized subsets of these such as Machine Learning. From leisure experiences that we encounter more generally via Netflix, Spotify or even e-commerce, to more specialized applications such as figuring out a direction for legal policies – Data science and Machine Learning have allowed organizations to move towards more effective results by acting on data-driven insights.
The science of recommendation systems exemplifies how technology greatly streamlines these various decision-making processes for us. In fact, as Prof. Devavrat Shah (Massachusetts Institute of Technology) suggests, Recommendation systems take into account several statistics and machine learning topics and allow you to build on them. Recommendation System algorithms, simply put, suggest relevant items to users – explaining the trends of their usage across a range of industries and their central role in revenue generation. As organizations increasingly lean towards data-driven approaches, an understanding of recommendation systems can help not only data science experts but also professionals in other areas such as marketing who, too, are expected to be data literate today.
Recommendation Systems of Today
From traditional methods that have served as the basis for recommendation systems, we now see a shift towards complex deep learning systems that have exponentially caused greater success of recommendation systems. The efficiency of Deep Learning lies in the hierarchical layers of artificial neural networks that enable machine learning with a nonlinear approach, as opposed to traditional approaches that analyse data in a linear manner.
Parallel to the increased reliance on deep learning systems is the usage of implicit feedback instead of explicit feedback in development of modern recommendation systems. The difference between the two approaches can be easily grasped as recommendation systems are perhaps among the most common technologies that we interact with on a daily basis. While explicit feedback draws insights from you actively indulging in giving feedback for something, for example, rating or a ‘like’, implicit feedback draws insights from the more frequent interactions that a user undertakes with products such as which products you browsed for or what kind of TV series do you generally watch. Recommendation systems, this way, predict your future choices for you on the basis of your past choices – without you having to spend time on searching for them.
Recommendation systems, thus, make online experiences easier for the user and are an efficient way to increase revenue generation for organizations.
Learn about Recommendation Systems with Prof. Devavrat Shah
If these highlights on Recommendation Systems have garnered your interest, you can now hear more about this fascinating technology from a world-renowned MIT Professor, Devavrat Shah.
Learn Recommendation Systems and become a data-driven decision maker with live virtual teaching from MIT faculty, hands-on projects, and mentorship from industry practitioners with the Applied Data Science Program by MIT Professional Education
The 12-week program has a curriculum carefully crafted by MIT faculty to provide you with the skills, knowledge, and confidence you need to flourish in the industry. The program not only focuses on Recommendation Systems but also other most business-relevant technologies, such as Machine Learning, Deep Learning, and more. The top rated data science program prepares you to be an important part of data science efforts at any organization.0