A Systematic Review on Recommendation Systems (Research Paper Sample)

Recommendation systems: A Systematic Review
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Recommendation systems: A Systematic Review
Introduction
A recommendation system, or a recommender system, is a subclass of information filtering systems that seeks to predict the “rating” or “preference” a user would give to an item. There are many areas where recommender systems are utilized, with commonly recognized examples as playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders. These systems can operate using a single input, like music or videos, or a combination of multiple inputs (for example, items watched along with ratings and comments from friends). The classification of recommendation systems falls under two broad categories: collaborative filtering and individualized learning (Sundaresan, 2011). Collaborative filtering is a more traditional form of recommendation where the system utilizes feedback data from users to create personalized recommendations. On the other hand, individualized learning recommends items based on past user behavior rather than relying solely on feedback data.