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Zuva, Keneilwe, Zuva, Tranos.  2017.  Diversity and Serendipity in Recommender Systems. Proceedings of the International Conference on Big Data and Internet of Thing. :120–124.

The present age of digital information has presented a heterogeneous online environment which makes it a formidable mission for a noble user to search and locate the required online resources timely. Recommender systems were implemented to rescue this information overload issue. However, majority of recommendation algorithms focused on the accuracy of the recommendations, leaving out other important aspects in the definition of good recommendation such as diversity and serendipity. This results in low coverage, long-tail items often are left out in the recommendations as well. In this paper, we present and explore a recommendation technique that ensures that diversity, accuracy and serendipity are all factored in the recommendations. The proposed algorithm performed comparatively well as compared to other algorithms in literature.