融合用户和项目相关信息的协同过滤算法研究

针对User-based协同过滤和Item-based协同过滤算法的不足，提出了一种新的推荐算法。该算法融合用户-项目评分数据集所包含的用户相关和项目相关的信息来推荐商品，并且利用模糊聚类技术分别将相似的项目和相似的用户聚类，改善传统推荐算法的数据稀疏性和可扩展性问题。实验结果表明，将用户相关和项目相关的信息融合能够提供更好的推荐。

Research on Collaborative Filtering Algorithm Based on Fusing User and Item's Correlative Information

Aiming at the disadvantages of user-based collaborative filtering and item-based collaborative filtering algorithms,the paper proposed a novel recommendation algorithm that generated item's recommendation by fusing user and item's correlative information inhering in the user-item rating dataset.The algorithm also involved the fuzzy clustering of similar items and similar users to improve the data sparsity and scalability of traditional collaborative filtering algorithms.Experiments showed a better recommendation could be provided by fusing user and item's correlative information.