Recommendation Diversity

Recommendation diversity aims to broaden the range of items suggested to users, counteracting the "filter bubble" effect of overly personalized systems that limit exposure to new or unexpected options. Current research focuses on developing algorithms and model architectures, such as matrix factorization and attention mechanisms, that incorporate diversity metrics (e.g., entropy, coverage) into existing recommendation models, often within knowledge graph contexts or by disentangling user preferences. This work is significant because increased recommendation diversity can enhance user experience, mitigate biases, and improve the overall performance of recommender systems across various applications, including e-commerce and news aggregation.