Novel Serendipitous Recommender System using Relevance Scores for Long Tail Items
Keywords:
Recommender System, Serendipity, Long Tail items, Non Popular items, Novelty Score, Bhattacharyya coefficient, k Nearest NeighborAbstract
Assisting users to aid in decision making while doing e-commerce purchases is a primary task of a traditional Recommender System. But often there is a necessity to give equitable importance to the products somehow ignored by the traditional Recommender Systems. Drawing less number of ratings from the users of the system should not be the reason to make the item fall into the long tail list of non-popular Items. To overcome such Recommender System issues of ‘Long Tail’ and ‘Popularity Bias’, we are proposing a new Serendipitous Recommender System using k nearest neighbor approach coupled with the Bhattacharyya coefficient to recommend not only novel but also at the same time, the relevant set of Items out of the long tail list of non-popular Items.
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