计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 181-187.doi: 10.11896/jsjkx.201100031
所属专题: 大数据&数据科学 虚拟专题
李康林1,2, 古天龙2, 宾辰忠2
LI Kang-lin1,2, GU Tian-long2, BIN Chen-zhong2
摘要: 大数据时代,由于信息过载,用户很难从海量数据中寻找出感兴趣的内容,个性化推荐系统的诞生极好地解决了这个问题。协同过滤算法被广泛应用于个性化推荐领域,但由于模型的限制,推荐效果未能得到进一步提升。现有的基于协同过滤模型的改进方法大多都是通过引入表示学习方法来得到更好的用户表示向量和项目表示向量,或通过改进用户项目匹配函数来提升推荐能力,但此类工作都致力于从单个交互提取用户-项目交互信息。文中提出了一种多空间交互协同过滤推荐算法,将用户向量和项目向量映射到多空间,从多角度做用户-项目交互,使用两层注意力机制聚合最终的用户表示向量和项目表示向量,以进行评分预测。在公开的真实数据集上,多空间交互协同过滤模型(MSICF)与多个基线模型进行了对比实验,MSICF模型的评估优于对比的基线方法。
中图分类号:
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