In today’s fast-evolving hi-tech era, it can be said without doubt that people of all ages are keen to be tech-savvy. The Internet has boosted that momentum. Technology helps us decide our consumerism choices and what not. Whenever we use the Net to search e-commerce sites like Amazon, Flipkart, etc., you must have seen that as per your preferences, pop-up ads show up. Have you ever pondered to realize what this is? How does an ecommerce site know what is your choice? This is machine learning.
Machine learning is one of the significant learning concepts in the present times. This machine learning keyword is always in and around us and especially those who are using it. Machine learning is a problem solver technique as it solves everything just as a leader would and helps in solving problems of our businesses. Now, with these kinds of qualities that the has to offer, how about making a career in this? You can make a career as a data scientist and keep knowledge having very good accuracy results in the test data set & very soon you would like to use it in your daily operations, and it has just started as a predictive algorithm to make life easy.
Just think about the real-time application of self-driving cars that minimize chances of collision. This is what is called quality of life which is always in demand. You want to enhance features of machine learning, use recommendation systems. Recommendation Systems are widely used in the e-commerce field to recommend items to users based on analysis, predictions, and buying history of their interest.
Recommendation Systems technique can be considered as part of personalization because it helps each customer to buy products of his interest. If the recommendation is appropriate the system can establish a good relationship with customers and can increase the product selection and sale rate such as Amazon’s use of the approach of Recommendation Systems to suggest the books & other things frequently purchased by customers. Various companies are already using machine learning to their advantage by Recommendation Systems based algorithms. The choice of career-making in machine learning is the most favorable area of data science. The students can build a Machine Learning-based recommendation system that is used for making content-based; collaboration-based filtering techniques and decision trees for classification, item similarity, and user similarity classification for a particular decision. For classification of the dataset, the various algorithms are used in the recommendation system but one of the best algorithms is the user-based algorithm it considers those who have similar attributes will be interested in the similar item(x) using three steps:
(1) for every item user A has no preference still.
(2) for other user B that has a preference for the item (x) compute the similarity between A and B.
(3) join user B preference for item(x), weighted by select S into the running average, finally return on top item ranked by weight average.
We realize that the inner workings of machine learning fulfill the business need and create a good decision model with open and clearly explain the situation of the business and help in making selection and decision.
The applications of recommendation systems in various fields like tourism, health, eLearning, e-commerce, etc. Most of the Recommendation Systems available today are based on the collected information of user purchase history, playlist generator on Netflix, YouTube, Spotify, and contribute to tourism. It guides and helps tourists to manage huge amounts of information and allows them to make appropriate travel decisions. This system is one of the most successful applications of machine learning technologies in business to take quick decisions making in their online transactions and also improves the quality of their shopping experience.
The recommendation technique in the e-Learning environment plays a vital role in providing accurate and right information to the learners. In the current scenario, e-Learning is completely transforming the learning habits of adults & even students. Intended learners are normally confused with the enormous number of online courses and keeping track of the relevance of these courses is also difficult. Since a lot of information is available on the internet, learners face the problem of searching for the right information and online recommendation systems can solve the problem of information overload efficiently.
The applications of recommendation systems have achieved great growth; there are still some problems that need more research with the advent of new applications for e-services. Traditional Recommendation System makes suggestions only for individual users, so it is suggested that community recommendation systems combine and match the individual preferences of group members to provide the group with satisfactory recommendations. In these situations, individuals need online decision support for a whole community.
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