计算机科学 ›› 2016, Vol. 43 ›› Issue (12): 206-208.doi: 10.11896/j.issn.1002-137X.2016.12.037
李容,李明奇,郭文强
LI Rong, LI Ming-qi and GUO Wen-qiang
摘要: 协同过滤利用邻居用户的偏好对目标用户的偏好进行推荐预测,相似度计算是其关键。传统的相似度计算忽略了用户共同评分项目数与用户平均评分的影响,以至于在数据稀疏时不能很好地度量用户间的相似度。提出了两个修正因子来改进传统相似度,同时改进了协同过滤算法,将其应用于电影推荐。仿真结果表明,在电影推荐中,基于改进后相似度计算的协同过滤算法能取得比传统算法更低的MAE值,提高了电影推荐质量。
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