Recommender Systems Questions
Recommender systems handle the data sparsity problem in music platforms by utilizing collaborative filtering techniques. These techniques analyze user behavior and preferences to identify similar users or items and make recommendations based on their preferences. By leveraging the collective wisdom of a large user base, recommender systems can overcome the lack of explicit ratings or feedback for many items.
To address the cold start problem in music platforms, recommender systems employ various strategies. One approach is content-based filtering, where the system analyzes the characteristics of songs, such as genre, artist, or lyrics, to make recommendations for new users or items. Another approach is hybrid filtering, which combines collaborative filtering and content-based filtering to provide recommendations. Additionally, recommender systems may prompt new users to provide initial preferences or use demographic information to make initial recommendations until sufficient user data is available.