Abstract
The recommendation system (RS) suffers badly from the cold start problem (CSP) that occurs due to the lack of sufficient information about the new customers, purchase history, and browsing data. Moreover, data sparsity problems also arise when the interaction is made among a limited number of items. These issues not only pose a negative impact on the recommendation but also significantly condense the diversity of choices available on the particular platform. To tackle these issues, a novel methodological approach called sparsity and cold start aware hybrid recommended system (SCSHRS) has been designed to suppress data sparsity and CSP in RS. The performance of the proposed SCSHRS method is tested on MovieLens-20 M, Last.FM and Book-Crossing data sets and compared with the prevailing techniques. Based on the evaluation reports with the standards, the proposed SCSHRS system gives Mean Absolute Percentage Error of 40%, and, precision (0.16), recall (0.08), F-measure (0.1), and Normalized Discounted Cumulative Gain of 0.65. This study completely describes the SCSHRS mechanism and its comparison with other pre-proposed historic and traditional processes based on collaborative filtering.
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