结合用户背景信息的协同过滤推荐算法

Collaborative filtering algorithm using user background information

针对个性化推荐系统中协同过滤算法面对的数据稀疏问题,提出了一种结合用户背景信息的推荐算法。该算法充分利用已有的用户数据和领域知识,对用户背景信息的相似度建模,在进行协同过滤前预先填充用户—项评分矩阵。实验表明该方法能够有效地提高推荐精度,并且不会带来性能上的瓶颈。

Aiming at the difficulty of data sparsity in personalized recommendation systems, a new collaborative filtering algorithm using user background information was presented. The algorithm took full advantage of user data and domain knowledge in hand, modeled user similarity based on user background information and filled in the user-item rating matrix in advance before the traditional collaborative filtering. The experimental results show that the new algorithm can improve the recommendation accuracy efficiently and will not cause bottleneck on performance.