A Conscious Cross-Breed Recommendation Approach Confining Cold-Start in Electronic Commerce Systems

Abstract

When a new customer enters the spectrum of the E-Commerce system, the informative records and dataset, such as about the new user, purchasing history and other browsing data become insufficient, resulting in the emergence of one serious issue such as a Cold start problem (CSP). Furthermore, when the interaction among the product items becomes limited, a new problem such as Sparsity arises to handle such problems in E-Commerce system, we have designed an extensive and hybridized methodological approach known as Cold start and sparsity aware hybridized recommendation system (CSSHRS), to reduce the Sparsity of dataset as well as to overcome the cold start problem in the recommendation framework. The proposed CSSHRS technique has been predicted by using the dataset of Last. FM, and Book-Crossing resulted in Mean absolute percentage error (MAPE) of 37%, recalls 0.07, precision 0.18, Normalized Discounted Cumulative Gain (NDCD) 0.61, and F-measure 0.1. This article proves the proposed CSSHRS technique as an effective and efficient hybrid of RS against the issue of data sparsity as well as CSP.

Keywords

• Data clustering
• cold start problem
• recommender system
• sparsity