融合内容与矩阵分解的混合推荐算法

传统的基于内容的推荐算法往往具有较低的准确性，而协同过滤推荐算法中普遍存在数据稀缺性和项目冷启动问题。为解决上述问题，提出了一种融合内容与协同矩阵分解技术的混合推荐算法。该算法实现了在共同的低维空间中分解内容和协同矩阵，同时保留数据的局部结构。在参数优化方面利用一种基于乘法更新规则的迭代方法，以此提高学习能力。实验结果表明，该算法优于其他具有代表性的项目冷启动推荐算法，有效缓解了数据稀疏性，提高了推荐准确性。

Hybrid recommendation algorithm based on content and matrix factorization

Traditional content-based recommendation algorithm has lower accuracy, while data sparseness and cold start problems are common in collaborative filtering recommendation algorithms. To solve this problem, this paper proposed a hybrid recommendation algorithm based on content and collaborative matrix factorization technique. The algorithm realized the decomposition of content and collaborative matrix in a common low-dimensional space while preserving the local data structure. This paper used an iterative method based on multiplication update rules in parameter optimization, improved learning ability. The experimental results show that the proposed algorithm is superior to other representative projects cold start recommendation algorithm, which effectively alleviates the data sparseness and improves the efficiency of the algorithm.