摘要
基于知识图谱的推荐方法是推荐系统的研究热点之一,利用用户历史行为及物品特征在知识图谱结构化表示数据的辅助下解决推荐系统数据稀疏性及冷启动问题。但是用户的兴趣易受特定物品所影响,而知识图谱以结构化形式存储数据,实体与实体之间仅存在链路关系,这就导致了单纯利用知识图谱的推荐方法在点击率预测方面性能较差。基于此,提出一种基于局部影响力与深层偏好传播的推荐方法(local influence and deep preference propagation,LIDP),充分利用知识图谱结构化数据在偏好传播中存在实体影响力的优点。LIDP模型首先对知识图谱逐层偏好传播获取数据影响力权重并根据所获数据权重进行局部影响力计算;其次对局部影响力进行用户历史行为的兴趣增强表示进而获取用户表示;最后对用户表示与物品的向量表示进行内积操作以获取最终交互概率。LIDP模型在MovieLens-1M数据集上相比最优基准模型GNRF,AUC、ACC、MAE和F 1值分别提高了0.16%、0.52%、0.87%、0.21%;在Book-Crossing数据集上,这些提升分别为0.45%、2.14%、1.29%、0.93%。实验结果表明,LIDP模型能有效获取深层次用户兴趣偏好,在推荐系统中具有良好的性能和效果,可以为用户提供更好的个性化推荐服务。
Based on knowledge graphs,recommendation methods have become one of the hot research topics in recommender systems.They utilize user historical behaviors and item features with the assistance of structured representation of data in knowledge graphs to address the sparsity and cold-start problems in recommendation systems.However,user interests are easily influenced by specific items,while knowledge graphs store data in structured forms with entities linked only through relational edges.This leads to poor performance in click-through rate prediction when solely relying on knowledge graph-based recommendation methods.A recommendation method called local influence and deep preference propagation(LIDP)is proposed,which fully exploits the advantages of entity influence in preference propagation within structured data of knowledge graphs.The LIDP model first propagates preferences layer by layer in the knowledge graph to obtain data influence weights,and then calculates local influence based on these weights.Next,it enhances user representations based on the enhanced representation of user interests through their historical behaviors.Finally,it calculates the final interaction probability by taking the inner product of user representations and item vector representations.On the MovieLens-1M dataset,compared to the optimal baseline model GNRF,LIDP improves AUC,ACC,MAE,and F 1 score by 0.16%,0.52%,0.87%,and 0.21%respectively.On the Book-Crossing dataset,these improvements are 0.45%,2.14%,1.29%,and 0.93%respectively.Experimental results demonstrate that the LIDP model effectively captures deep-level user interest preferences,exhibiting good performance and effectiveness in recommendation systems,thereby providing users with better personalized recommendation services.
作者
徐伟
李翔
朱全银
任珂
孙纪舟
XU Wei;LI Xiang;ZHU Quanyin;REN Ke;SUN Jizhou(School of Computer and Software Engineering,Huaiyin Institute of Technology,Huaian 223003,Jiangsu,China)
出处
《陕西师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2024年第3期105-119,共15页
Journal of Shaanxi Normal University：Natural Science Edition
基金
国家自然科学基金青年项目(62002131)。
关键词
局部影响力
知识图谱
推荐系统
深层偏好传播
兴趣增强
local influence
knowledge graph
recommender system
deep preference propagation
interest enhancement
