A Study of Recent Recommender System Techniques

Abstract

The influx of data in most domains is huge and dynamic, leading to big data and hence the need to build a recommender system grows stronger. This work is a comprehensive survey of the current status of different recommendation approaches, their limitations and extension which when applied may eradicate the incessant information overload problem of web entirely. Further, an investigation is conducted on the Google Scholar database, delineating the temporal distribution of different recommendation techniques. Several popular and most-used evaluation metrics, domain-specific applications, and data sets used in the recommendation are reviewed. By summarizing the current state-of-the-art, this work may help researchers in the field of recommendation system techniques and provides future directions highlighting issues that need to be focused on.