摘要
将知识图谱中的辅助知识应用于推荐系统中,在一定程度上可以缓解数据稀疏问题。但现有基于知识图谱的推荐方法大多只利用实体间的显式关系建模用户行为,而用户和推荐物品之间可能存在无法显式表达的关系。因此,该文提出了一种融合知识图谱传播特征和提示学习范式的推荐模型。首先,以用户与物品的历史交互为起点,利用知识图谱传播用户偏好,获得用户的动态行为信息;然后,将用户静态属性特征信息作为输入,利用提示学习技术,引入预训练语言模型中的隐式知识,挖掘出用户的潜在兴趣,作为对知识图谱显式知识的补充;最后,根据模板词在预训练语言模型词汇表中的概率完成对用户的推荐。实验表明,该方法在MovieLens-1M、Book-Crossing和Last.FM三个数据集上与其他模型相比具有良好的推荐性能,在AUC评价指标上平均分别提升6.4%、4.0%和3.6%,在F1评价指标上平均分别提升了6.0%、1.8%和3.2%。
The data sparsity issue in recommendation can be resolved by including explicit information in the knowledge graph.Most existing knowledge graph-based methods capture user behaviors solely through entity relationships,ignoring the implicit cues between users and items to recommend.To this end,this paper proposes a unique recommendation approach incorporating the knowledge graph and the prompt learning.In particular,the knowledge graph is employed to propagate user preferences and produce corresponding dynamic behaviors.And,the implicit insights absent from the knowledge graph could be absorbed by feeding pre-trained language model(PLM)with static user features under the prompt learning setting.Finally,the template probability within the PLM vocabulary is intuitively selected as the possibility of the recommendation.Experiments on the MovieLens-1M,Book-Crossing,and Last.FM datasets show that our technique outperforms state-of-the-art baselines by 6.4%,4.0%and 3.6%in AUC,and 6.0%,1.8%,and 3.2%in F,value,respectively.
作者
黄偲偲
柯文俊
张杭
方志
余增文
汪鹏
王清理
HUANG Sisi;KE Wenjun;ZHANG Hang;FANG Zhi;YU Zengwen;WANG Peng;WANG Qingli(Beijing Institute of Computer Technology and Applications,Beijing 100854,China;School of Computer Science and Engineering,Southeast University,Nanjing,Jiangsu 211189,China;School of Computer Science and Technology,Xidian University,Xi'an,Shaanxi 710000,China)
出处
《中文信息学报》
CSCD
北大核心
2023年第5期122-134,共13页
Journal of Chinese Information Processing
基金
国家重点研究与发展计划(2018YFD1100302)。
关键词
知识图谱
推荐系统
提示学习
预训练语言模型
knowledge graph
recommendation system
prompt learning
pre-trained language model
