计算机科学 ›› 2021, Vol. 48 ›› Issue (11): 176-183.doi: 10.11896/jsjkx.201000004
陈源毅1,3, 冯文龙2,3, 黄梦醒2,3, 冯思玲2,3
CHEN Yuan-yi1,3, FENG Wen-long2,3, HUANG Meng-xing2,3, FENG Si-ling2,3
摘要： 针对个性化推荐,常用的推荐算法有内容推荐、物品协同过滤(Item CF)和用户协同过滤(User CF),但是这些算法以及它们的改进算法大多偏向于关注用户的显性反馈(标签、评分等)或评分数据,缺少对多维度用户行为和行为顺序的利用,导致推荐准确率不够高及冷启动等问题。为了提高推荐精度,文中提出了一种基于知识图谱的行为路径协同过滤推荐算法(BR-CF)。首先根据用户行为数据,考虑行为顺序创建行为图谱(behavior graph)和行为路径(behavior route),然后采用向量化技术(Keras Tokenizer)将文本类型的路径向量化,最后计算多维度行为路径向量之间的相似度,对各维度分别进行路径协同过滤推荐。在此基础上,文中提出了两种BR-CF与Item CF相结合的改进算法。实验结果表明,在阿里天池数据集UserBehavior上,BR-CF算法能够有效地在多个维度中进行推荐,实现数据的充分利用和推荐的多样性,并且此改进算法很好地提升了Item CF的推荐性能。
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