[1]胡婷婷,黄 刚*,吴长旺.融合知识图卷积网络的双端邻居推荐算法[J].计算机技术与发展,2022,32(10):34-40.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 006]
　HU Ting-ting,HUANG Gang*,WU Chang-wang.A Tow-endian Neighbor Recommendation Algorithm for Convolutional Networks Fused with Knowledge Graph[J].,2022,32(10):34-40.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 006]
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融合知识图卷积网络的双端邻居推荐算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年10期
页码:
34-40
栏目:
大数据与云计算
出版日期:
2022-10-10

文章信息/Info

Title:
A Tow-endian Neighbor Recommendation Algorithm for Convolutional Networks Fused with Knowledge Graph
文章编号:
1673-629X(2022)10-0034-07
作者:
胡婷婷; 黄 刚* ; 吴长旺
南京邮电大学 计算机学院、软件学院、网络空间安全学院,江苏 南京 210023
Author(s):
HU Ting-ting; HUANG Gang* ; WU Chang-wang
School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
关键词:
知识图谱; KGCN; 推荐系统; 用户偏好; 准确性
Keywords:
knowledge graph; KGCN; recommendation system; user preference; accuracy
分类号:
TP309
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 10. 006
摘要:
针对现有的基于知识图谱的推荐对于用户信息的考虑少于对物品信息的考虑,提出一种融合知识图卷积网络的双端邻居推荐算法,在用户端及物品端同时进行特征提取。 对于用户特征的提取,是通过用户偏好在知识图谱中的扩散过程实现。 对于物品特征的提取,是将邻居信息聚合到物品节点生成嵌入向量,因各个邻居的权重与用户点击物品的邻居节点紧密联系,因此基于 KGCN 模型来实现。 最后让用户兴趣传播与物品特征聚合交替进行。 在两个数据集上进行对比实验,在 MovieLens-1M 数据集上,与基线方法相比,AUC 和 F1 分别提升了 1. 5% 和 2. 0% ,在 Book-Crossing 数据集上,AUC 和 F1 分别提升了 5. 3% 和 1. 9% ,算法有效性得到显著提升。
Abstract:
Aiming at the fact that the existing recommendation based on knowledge graph pays less attention to user information than item information, a two - end neighbor recommendation algorithm based on knowledge graph convolution network is proposed to extract features at both user and item sides. The extraction of user personalized features is carried out through the diffusion process of user preferences in the knowledge graph. For item feature extraction,neighbor information is aggregated to item node to generate embedding vector.Since the weight of each neighbor is closely related to the neighbor node of the item clicked by the user,the vector of item feature is extracted based on KGCN model. Finally,user interest dissemination and item feature aggregation are carried out in turn. Comparison experiments were conducted on two data sets. Compared with baseline method,AUC and F1 improved by 1. 5% and 2. 0% respectively onMovielens-1M data set,while AUC and F1 improved by 5. 3% and 1. 9% respectively on Book-Crossing data set. The effectiveness of the proposed algorithm has been significantly improved.

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更新日期/Last Update: 2022-10-10