Creating A Weighted Hybridization Approach for A Music Recommendation System to Tackle Significant Challenges Inherent in Recommendation Systems
Keywords:
Music Recommendation system, Information Overloading, Long tail, Sparsity, Cold- start, Weighted hybridizationAbstract
The Music Recommendation System (MRS) functions as a remedy to the information overload prevalent in the digital music landscape. This research paper addresses prominent challenges encountered by recommendation systems, specifically the Long Tail phenomenon, data sparsity, and the cold-start problem, through the implementation of a weighted hybrid approach. This approach integrates collaborative filtering techniques based on both user preferences and item characteristics. Notably, our proposed system incorporates contextual information in the generation of music recommendations. Experiments were conducted using a benchmark dataset and synthetic data derived from a Music Portal application. The results demonstrate the system's efficacy in accurately capturing user interests, considering diverse factors such as a user's historical preferences, profile, item similarities, timestamps, and social connections.
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