A Comparative Analysis of Various Algorithms of Recommender Systems for Serendipity using Novelty Scores
Keywords:
Serendipity, Long Tail Items, Recommender System, TANGENT, KFN, Novelty Score, Relevance Score, Popularity BiasAbstract
The thrust on serendipity is assisting the traditional recommender systems to narrow down on the abundance of recommendations with special weightage and emphasis on waiting-to-be-recommended ‘long tail’ items. Further, it also paves the way for moving from the overlooked ‘accuracy’ aspect of recommender systems to the highly fruitful and rightful aspect of ‘user satisfaction’. As the serendipitous recommender systems inculcate the refreshing ‘novelty’ component, the inherent traditional recommender systems’ issues of ‘long tail problem’, ‘popularity bias’, ‘cold start problem’, ‘over specialization issue’, ‘matthew effect’, etc. are overcome. Hence, in this paper, we investigate and analyze the effectiveness of three different serendipitous recommender system algorithms, TANGENT, KFN and an already published NOVEL SERENDIPITOUS ALGORITHM on a prominent ‘novelty score’ metric. The detailed and rigorous analysis suggest that all the three algorithms are able to surpass the 50 % novelty score benchmark, with the overall novelty scores of 55.57 % for the TANGENT algorithm, 79.39 % for the KFN algorithm and 83.03 % for the NOVEL SERENDIPITOUS ALGORITHM. The results vindicate the overall supremacy and efficacy of NOVEL SERENDIPITOUS ALGORITHM over the other two serendipitous algorithms.
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