Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge Graph

doi:10.3778/j.issn.1673-9418.2012054

Abstract

Abstract:

In view of the problem that some recommendation algorithms based on knowledge graphs only aggregate one end of the neighbors and cannot effectively determine the relationship between entities and users, this paper proposes a dual-end neighbor aggregation recommendation algorithm based on knowledge graphs. This algorithm explores the internal connections of knowledge graphs to discover the potential relationship between users and items. On the user side, this paper proposes a method of aggregating user neighbor information. In the relational space of the knowledge graph, the knowledge graph is used to spread and extract the user’s potential interest, and iteratively inject the potential interest into the user characteristics with attention bias to generate user embedding representation vector. At the item side, the user vector that aggregates the user’s neighbor information is sent to the KGCN (knowledge graph convolutional networks) model, and when the polymer product and its neighbor infor-mation are used, a new aggregation method is used to generate the item embedding representation. Finally, the obtained vector is sent to train. Through the inner product operation of the vector and normalization, the association score between the user and the item is obtained. Then the training is carried out in the training set to optimize the parameters. Comparative experiments are conducted on two public datasets. Compared with the baseline, on the Book-Crossing dataset, AUC and ACC are increased by 1.72% and 4.24%, and on the Last.FM dataset, AUC and ACC are increased by 1.07% and 1.14%. It is proven that the effectiveness of the algorithm is improved after the information of neighbors at both ends is aggregated.

Key words: recommendation algorithm, knowledge graph, neighbor aggregation

摘要：

针对一些基于知识图谱的推荐算法仅聚合一端邻居而无法有效确定实体与用户之间关系的问题,提出了一种基于知识图谱的双端邻居聚合推荐算法,算法通过探究知识图谱的内在联系以发掘用户和物品之间的潜在关系。在用户端,提出了一种聚合用户邻居信息的方法,在知识图谱的关系空间下,使用知识图谱来传播和提取用户的潜在兴趣,通过迭代将潜在兴趣注入具有注意偏差的用户特征中生成用户嵌入表示向量;在物品端,将融合了用户邻居信息的用户向量送入KGCN模型,并在聚合物品和其邻居信息时,采用新的聚合方式,生成物品嵌入表示向量。最后,将得到的用户和物品向量送入预测环节,通过向量的内积运算并归一化得到用户和物品的关联分数,然后在训练集中进行训练,优化参数。在两个公开数据集上进行对比实验,在 Book-Crossing数据集上,相较于最优基线,AUC和ACC分别提升了1.72%和4.24%;在Last.FM数据集上,AUC和ACC分别提升了1.07%和1.14%,从而验证在聚合两端邻居信息后,算法的有效性得到了提升。

关键词: 推荐算法, 知识图谱, 邻居聚合

CLC Number:

TP391

WANG Baoliang, PAN Wencai. Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge Graph[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(6): 1354-1361.

王宝亮, 潘文采. 基于知识图谱的双端邻居信息融合推荐算法[J]. 计算机科学与探索, 2022, 16(6): 1354-1361.

Figures/Tables 12

Fig.1 Overall framework

Fig.2 User node information aggregation

Fig.3 KGCN model

Table 1 Statistics for two datasets

统计项目	Book-Crossing	Last.FM
users	19 676	1 872
items	20 003	3 846
interactions	172 576	42 346
entities	25 787	9 366
relations	18	60
KG triples	60 787	15 518

Table 2 Parameter setting

Parameter	Book-Crossing	Last.FM
$K - u$	16	64
$K - i$	3	3
$H - u$	3	3
$H - i$	1	1
$d$	32	16
$λ$	1×10^-6	1×10^-6
$η$	1×10^-4	5×10^-3
$γ$	0.1	0.1
Batch size	256	128

Table 2 Parameter setting

Parameter	Book-Crossing	Last.FM
$K - u$	16	64
$K - i$	3	3
$H - u$	3	3
$H - i$	1	1
$d$	32	16
$λ$	1×10^-6	1×10^-6
$η$	1×10^-4	5×10^-3
$γ$	0.1	0.1
Batch size	256	128

Table 3 Results of AUC and ACC in CTR prediction

模型	Book-Crossing		Last.FM
模型	AUC	ACC	AUC	ACC
LibFM	0.685 0	0.640 0	0.777 0	0.709 0
Wide&Deep	0.712 0	0.624 0	0.756 0	0.688 0
RippleNet	0.724 9	0.662 0	0.813 2	0.746 0
KGCN	0.691 7	0.631 8	0.803 5	0.732 2
KGNN-LS	0.687 3	0.632 0	0.802 3	0.728 2
本文模型	0.737 4	0.690 1	0.821 9	0.754 5
本文模型-u	0.728 0	0.664 1	0.817 6	0.743 9
本文模型-i	0.673 8	0.619 9	0.773 0	0.700 8
不同预测函数	0.733 0	0.693 9	0.792 0	0.753 6

Table 4 Influence of parameter γ on AUC value

$γ$	Book-Crossing	Last.FM
100	0.734 9	0.814 1
10	0.737 1	0.820 6
1	0.736 1	0.820 4
0.1	0.737 4	0.821 9
0.01	0.735 8	0.821 1
直接累加	0.737 2	0.818 5

Table 4 Influence of parameter γ on AUC value

$γ$	Book-Crossing	Last.FM
100	0.734 9	0.814 1
10	0.737 1	0.820 6
1	0.736 1	0.820 4
0.1	0.737 4	0.821 9
0.01	0.735 8	0.821 1
直接累加	0.737 2	0.818 5

Table 5 Impact of K - u sampled value per hop on AUC value at user end

$K - u$	Book-Crossing	Last.FM
2	0.725 5	0.806 3
4	0.729 7	0.807 2
8	0.735 4	0.813 5
16	0.737 4	0.819 0
32	0.736 4	0.819 6
64	0.735 5	0.821 9

Table 5 Impact of K - u sampled value per hop on AUC value at user end

$K - u$	Book-Crossing	Last.FM
2	0.725 5	0.806 3
4	0.729 7	0.807 2
8	0.735 4	0.813 5
16	0.737 4	0.819 0
32	0.736 4	0.819 6
64	0.735 5	0.821 9

Table 6 Impact of K - i sampled value per hop on ACC value at item end

$K - i$	Book-Crossing	Last.FM
1	0.670 2	0.749 2
2	0.680 0	0.741 6
3	0.690 1	0.754 5
4	0.689 4	0.739 7
5	0.691 3	0.752 6
6	0.691 4	0.746 8
8	0.689 6	0.745 6

Table 6 Impact of K - i sampled value per hop on ACC value at item end

$K - i$	Book-Crossing	Last.FM
1	0.670 2	0.749 2
2	0.680 0	0.741 6
3	0.690 1	0.754 5
4	0.689 4	0.739 7
5	0.691 3	0.752 6
6	0.691 4	0.746 8
8	0.689 6	0.745 6

Table 7 Impact of user end aggregation hops H - u on AUC value

$H - u$	Book-Crossing	Last.FM
1	0.733 3	0.806 2
2	0.734 1	0.808 0
3	0.737 4	0.821 9
4	0.735 7	0.811 4

Table 7 Impact of user end aggregation hops H - u on AUC value

$H - u$	Book-Crossing	Last.FM
1	0.733 3	0.806 2
2	0.734 1	0.808 0
3	0.737 4	0.821 9
4	0.735 7	0.811 4

Table 8 Impact of item end aggregation hops H - i on ACC value

$H - i$	Book-Crossing	Last.FM
1	0.690 1	0.754 5
2	0.444 1	0.733 3
3	0.445 0	0.734 3
4	0.447 2	0.732 8

Table 8 Impact of item end aggregation hops H - i on ACC value

$H - i$	Book-Crossing	Last.FM
1	0.690 1	0.754 5
2	0.444 1	0.733 3
3	0.445 0	0.734 3
4	0.447 2	0.732 8

Table 9 Influence of vector dimension value d on AUC value

$d$	Book-Crossing	Last.FM
4	0.739 1	0.806 2
8	0.736 7	0.819 5
16	0.735 5	0.821 9
32	0.737 4	0.820 6
64	0.736 3	0.814 5
128	0.734 4	0.815 9

Table 9 Influence of vector dimension value d on AUC value

$d$	Book-Crossing	Last.FM
4	0.739 1	0.806 2
8	0.736 7	0.819 5
16	0.735 5	0.821 9
32	0.737 4	0.820 6
64	0.736 3	0.814 5
128	0.734 4	0.815 9

References 15

[1]	KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37.
[2]	SHI Y, LARSON M, HANJALIC A. Collaborative filtering beyond the user-item matrix[J]. ACM Computing Surveys, 2014, 47(1): 1-45.
[3]	MOHSEN J, MARTIN E. A matrix factorization technique with trust propagation for recommendation in social networks[C]// Proceedings of the 4th ACM Conference on Recom-mender Systems, Barcelona, Spain, Sep 26-30, 2010. New York: ACM, 2010: 135-142.
[4]	WANG H, WANG J, ZHAO M, et al. Joint topic-semantic-aware social recommendation for online voting[C]// Procee-dings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore, Nov 6-10, 2017. New York: ACM, 2017: 347-356.
[5]	WANG H, ZHANG F, HOU M, et al. Shine: signed hetero-geneous information network embedding for sentiment link prediction[C]// Proceedings of the 11th ACM International Conference on Web Search and Data Mining, Los Angeles, Feb 5-9, 2018. New York: ACM, 2018: 592-600.
[6]	WANG Q, MAO Z, WANG B, et al. Knowledge graph em-bedding: a survey of approaches and applications[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(12): 2724-2743. DOI URL
[7]	WANG H, ZHANG F, XIE X, et al. DKN: deep knowledge-aware network for news recommendation[C]// Proceedings of the 2018 World Wide Web Conference, Lyon, Apr 23-27, 2018. New York: ACM, 2018: 1835-1844.
[8]	WANG H, ZHANG F, WANG J, et al. RippleNet: propagating user preferences on the knowledge graph for recommender systems[C]// Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 417-426.
[9]	WANG H, ZHANG F, WANG J, et al. Exploring high-order user preference on the knowledge graph for recommender systems[J]. ACM Transactions on Information Systems, 2019, 37(3): 1-26.
[10]	WANG H, ZHAO M, XIE X, et al. Knowledge graph convo-lutional networks for recommender systems[C]// Proceedings of the 2019 World Wide Web Conference, San Francisco, May 13-17, 2019. New York: ACM, 2019: 3307-3313.
[11]	WANG H, ZHANG F, ZHANG M, et al. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, Aug 4-8, 2019. New York: ACM, 2019: 968-977.
[12]	WANG X, WANG D, XU C, et al. Explainable reasoning over knowledge graphs for recommendation[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Con-ference, the 9th AAAI Symposium on Educational Adv-ances in Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 5329-5336.
[13]	WANG X, HE X N, CAO Y X, et al. KGAT: knowledge graph attention network for recommendation[C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, Aug 4-8, 2019. New York: ACM, 2019: 950-958.
[14]	RENDLE S. Factorization machines with LibFM[J]. ACM Transactions on Intelligent Systems and Technology, 2012, 3(3): 57.
[15]	CHENG H T, KOC L, HARMSEN J, et al. Wide & deep learning for recommender systems[C]// Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, Sep 15, 2016. New York: ACM, 2016: 7-10.