To perform product or services’ recommendations, the Recommender System (RS) is used by most of the social media, such as Twitter, LinkedIn, Netflix, etc. and potential e-marketers, to name, Amazon, Flipkart, Alibaba, eBay, Myntra, etc. including the famous search engine Google. All of these systems uses Machine Learning (ML) algorithms claimed from the field of Artificial Intelligence (AI). However, choosing an appropriate ML algorithm to fulfil this task of Recommender System (RS) is a critical issue, if not impossible, since a considerably large number of algorithms find place in the literature. Practitioners and researchers developing Recommender System leaves a very little information about their current approaches in algorithm usage, thus it is sufficient to create further confusion to perform the task of selecting appropriate algorithm. The current paper presents a systematic insight in to the subject analysing the usage of machine learning algorithms for Recommender System (RS), and thereby identifies the research opportunities to bring further improvement into the system used. The study carried exposes that the Bayesian network and Decision Tree algorithms are widely adopted and used in the Recommender System (RS) due to their relative simplicity along with required performance. The software system requirements and the design phases adopted for the same also appears to have ample of further research opportunities. This paper presents a systematic analysis of the topic under consideration with recommendations of performance measures and evaluation procedures as per its suitability for designing an effective RS.

Machine Learning, Recommender System, Deep Learning, Artificial Intelligence, systematic study

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