Tailoring Content with Keyword-Based Recommendation Engine Using Machine Learning

It is the information age thus; it is very important to understand how to personalize content in accordance with what appeals to a user. This work is conceptualized to be labeled "Tailoring Content with Keyword-Based Recommendation Engine Using Machine Learning". This work provides the applicative system catering for the experience of users by proposing personalization in terms of content. The core of the work is keyword-based recommendation engine chosen by the user to recommend posts as well as other contents believed to be of the user’s interest. A recommendation system uses various types of algorithms related to machine learning and a combination of data analysis technologies while considering the interaction of the user with posts, created, liked, or participated in. From this, it will then understand keen interests by a user and make recommendations based on those grounds. This will keep users interesting about the relevant content and help develop skills in areas they are interested in. Some scope includes designing a recommendation engine, developing it into an application, and measuring performance. This would be in the ability of doing so, with accuracies at all high degrees, when recommending this content, scalability, and also privacy for the users. It will be used to prove how machine learning techniques can be applied towards developing personalized user experience and, as a by-product, contribute to this field known as content recommendation systems.