计算机科学 ›› 2017, Vol. 44 ›› Issue (3): 247-253.doi: 10.11896/j.issn.1002-137X.2017.03.051
何明,刘毅,常盟盟,吴小飞
HE Ming, LIU Yi, CHANG Meng-meng and WU Xiao-fei
摘要： 上下文感知推荐系统的主要任务是利用上下文信息进一步提高推荐系统的推荐精度和用户满意度。提出了一种基于上下文项目评分分裂的推荐方法。该方法首先依据项目分裂判别标准对多维度上下文信息下的项目进行分裂,然后根据分裂结果并通过上下文维度进行聚类。在此基础上,利用协同过滤推荐算法进行未知评分预测。最后,面向不同的项目分裂标准,在LDOS-CoMoDa数据集上进行仿真对比实验。实验结果表明,相对于其他推荐算法,该方法有效提升了推荐精度,达到了提高推荐质量效果的目的。
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