计算机科学 ›› 2021, Vol. 48 ›› Issue (2): 114-120.doi: 10.11896/jsjkx.200900152
韩立锋, 陈莉
HAN Li-feng, CHEN Li
摘要： 冷启动一直是推荐系统领域中被密切关注的问题,针对新注册用户冷启动的问题,文中提出了一种融合用户人口统计学信息与项目流行的推荐模型。首先对训练集用户进行聚类,将训练集用户划分为若干类。然后计算新用户与所属类别中其他用户之间的距离,选择其近邻用户集,在评分计算时综合考虑项目流行度对推荐效果的影响,进而为目标用户推送感兴趣的节目。最后在经典推荐系统数据集中对所提模型进行验证。实验结果表明,该模型明显优于传统协同过滤算法,并在一定程度上解决了冷启动问题。
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