A Comprehensive Survey on Recommender Systems Techniques and Challenges in Big Data Analytics with IOT Applications

Abstract

Purpose: Purpose of this research is to carry out survey on Recommendation systems techniques in Big Data Analytics. This article presents designing of recommender systems and evaluates it with help of various performance metrics in IoT applications.
 
 Theoretical framework: With fast development and applications of Internet of Things, large amount of user data is generated and accumulated every day. Growth of media consumption in online social networks is exponential which requires an efficient and effective recommendation system to enhance excellence in experience for users. Recommender systems help users to overcome Information Overload problem by providing them relevant contents. 
 
 Method/design/approach: The main aspect of recommender system is how to take complete advantage of this ubiquitous data. Recommender system is mainly used to guess or predict users’ interests and make relevant recommendations. Collaborative filtering is the technique that uses the relationships between users and between items in order to build a prediction. Collaborative filtering algorithms are mainly categorized as model-based methods and memory-based methods. In this article, various methods to build recommender system are described. Similarly, Collaborative filtering uses Pearson cosine, cosine vector, Jaccard similarity to identify same users or items. Recommender system has various applications in domain such as healthcare, transportation, agriculture, e-media etc. 
 
 Findings: Evaluation of recommender system with help of metrics such as Precision and Recall is presented. Comparison of experimental results is presented with help of MAE and RMSE. Recommendation system helps to discover relevant insights and can be one of the vital technologies in future IoT solutions.
 
 Research, Practical & social implications: The research makes significant contribution by providing survey of existing recommender systems along with challenges faced while designing effective and accurate recommender. Various similarity measures to find similar users or items are investigated with future pointer direction. Recommender system help in decision making process.
 
 Originality/value: The results and conclusion obtained in this research are helpful in development of novel Recommender systems which definitely assist users to overcome Information Overload issue. It helps user to save network load as well.