中文摘要：

为了提高推荐系统挖掘用户感兴趣的冷门项目的能力,提出一种改进的协同过滤推荐算法.在传统算法基础上考虑项目流行度的影响,将其作为权重因子引入到相似性计算和推荐过程中,以提高用户相似性计算的可靠性和冷门项目在最终的项目推荐过程中的影响力.典型数据集上的对比实验表明,该算法能够在保持甚至提高推荐准确度的前提下,有效挖掘到用户感兴趣的冷门项目.

英文摘要：

To improve the recommendation systems＇ ability of mining unpopular items,an improved collaborative filtering algorithm is proposed.Based on traditional algorithm,items＇ popularity is considered as a weighting factor in similarity calculating and recommendation process to boost the reliability of user-similarity calculating and the influence of unpopular items in final recommending.Comparative experiments on typical dataset show that the algorithm is able to mine unpopular items effectively under the premise of maintaining or even improving recommendation accuracy.