Personalised recommendations in e-commerce

Most of the current personalised recommender systems use either collaborative filtering or data mining for offering recommendations. However, such methods are beset with problems of sparsity and scalability. In this paper, we present a System for Personalised REcommendations in E-commerce (SPREE) that combines the strengths of both collaborative filtering and data mining for providing better recommendations. We experimentally evaluate our system and show the benefits using a set of real and synthetic datasets. We also propose a novel similarity metric for efficiently computing collaborative users. Experimental results show that the proposed similarity metric is up to 12 orders of magnitude faster and has better predictive capabilities compared to other similarity metrics.

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