Content Based Recommender System: Methodologies, Performance, Evaluation and Application

Content based recommendation system is an effective online information filtering tool, widely popularized by changing computer user habits, personalization trends, and the emergence of Internet access. Although current content-based recommender systems are well-known for providing correct recommendations, they nevertheless face a number of limits and issues such as serendipity, scalability, cold start, technical analysis, and so on. Because of the variety of strategies available, selecting one becomes a difficult issue when developing an application - targeted recommendation systems. Furthermore, each technique has its own set of traits, benefits, and drawbacks, raising even more questions that must be answered. This study aims to provide a comprehensive assessment of current developments in the field of content-based recommendation systems, with a focus on a variety of applications such as research papers, novels, entertainment, the health sector, products, and so on. First, each recommendation system's many uses are examined. Following that, an algorithmic study of several recommendation systems is undertaken, and taxonomy is constructed to account for the many components required to develop an efficient recommendation system. Furthermore, data sets are gathered, and the simulation platform and performance metrics specific to each contribution are reviewed and scored. Finally, this analysis provides much-needed insight into the field's current state of research and exposes existing gaps and obstacles to assist future researchers in developing an effective recommendation system.