Research on semantic feature-enhanced collaborative filtering recommendation algorithm

With the explosive growth of information, recommendation systems play a crucial role in addressing the information overload problem. The collaborative filtering algorithm, one of the core technologies in recommendation systems, confronts challenges such as data sparsity and cold start. This paper proposes a semantic feature - enhanced collaborative filtering recommendation algorithm, which optimizes the algorithm's performance by mining and integrating semantic features. Experimental results demonstrate that this algorithm outperforms traditional algorithms in terms of recommendation accuracy and diversity, offering novel ideas for the development of recommendation systems.