Knowledge graph recommendation model with multiple time scales and feature enhancement

doi:10.11772/j.issn.1001-9081.2021071241

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (4): 1093-1098.DOI: 10.11772/j.issn.1001-9081.2021071241

Special Issue: CCF第36届中国计算机应用大会 (CCF NCCA 2021)

• The 36 CCF National Conference of Computer Applications (CCF NCCA 2020) • Previous Articles Next Articles

Knowledge graph recommendation model with multiple time scales and feature enhancement

Suqi ZHANG¹(), Xinxin WANG², Shiyao SHE³, Junhua GU³^,⁴

^1.School of Information Engineering，Tianjin University of Commerce，Tianjin 300134，China
^2.School of Science，Tianjin University of Commerce，Tianjin 300134，China
^3.School of Artificial Intelligence，Hebei University of Technology，Tianjin 300401，China
^4.Hebei Province Key Laboratory of Big Data Calculation （Hebei University of Technology），Tianjin 300401，China

Received:2021-07-16 Revised:2021-09-14 Accepted:2021-09-18 Online:2021-09-27 Published:2022-04-10
Contact: Suqi ZHANG
About author:WANG Xinxin， born in 1999， M. S. candidate. Her research interests include recommender system， intelligent information processing.
SHE Shiyao， born in 1995， M. S. His research interests include recommender system， intelligent information processing.
GU Junhua， born in 1966， Ph. D.， professor. His research interests include intelligent information processing， data mining.
Supported by:
National Natural Science Foundation of China(61802282)

融合多时间尺度和特征加强的知识图谱推荐模型

张素琪¹(), 王鑫鑫², 佘世耀³, 顾军华³^,⁴

^1.天津商业大学信息工程学院，天津 300134
^2.天津商业大学理学院，天津 300134
^3.河北工业大学人工智能与数据科学学院，天津 300401
^4.河北省大数据计算重点实验室（河北工业大学），天津 300401

通讯作者: 张素琪
作者简介:王鑫鑫（1999—），女，陕西咸阳人，硕士研究生，主要研究方向：推荐系统、智能信息处理
佘世耀（1995—），男，河北邯郸人，硕士，主要研究方向：推荐系统、智能信息处理
顾军华（1966—），男，天津人，教授，博士，CCF会员，主要研究方向：智能信息处理、数据挖掘。
基金资助:
国家自然科学基金资助项目(61802282)

Abstract

Abstract:

Aiming at the problems that the existing knowledge graph recommendation models do not consider the periodic features of the user and the items to be recommended will affect the recent interests of the user， a knowledge graph recommendation model with Multiple Time scales and Feature Enhancement （MTFE） was proposed. Firstly， Long Short-Term Memory （LSTM） network was used to mine the user’s periodic features on different time scales and integrate them into user representation. Then， attention mechanism was used to mine the features strongly correlated with the user’s recent features in the items to be recommended and integrate them into the item representation after enhancement. Finally， the scoring function was used to calculate user’s ratings of items to be recommended. The proposed model was compared with PER（Personalized Entity Recommendation）， CKE（Collaborative Knowledge base Embedding）， LibFM， RippleNet， KGCN（Knowledge Graph Convolutional Network）， CKAN（Collaborative Knowledge-aware Attentive Network） knowledge graph recommendation models on real datasets Last.FM， MovieLens-1M and MovieLens-20M. Experimental results show that compared with the model with the best prediction performance， MTFE model has the F1 value improved by 0.78 percentage points， 1.63 percentage points and 1.92 percentage points and the Area Under Curve of ROC （AUC）metric improved by 3.94 percentage points， 2.73 percentage points and 1.15 percentage points on three datasets respectively. In summary， compared with comparative knowledge graph recommendation models， the proposed knowledge graph recommendation model has better recommendation effect.

Key words: recommendation algorithm, knowledge graph, periodic feature, time scale, recent feature, feature enhancement

摘要：

针对现有的知识图谱推荐模型没有考虑到用户的周期特征以及待推荐项目会对用户近期兴趣产生影响的问题，提出一种融合多时间尺度和特征加强的知识图谱推荐模型（MTFE）。首先，采用长短期记忆（LSTM）网络在不同时间尺度上挖掘用户的周期特征并融入到用户表示中；然后，通过注意力机制挖掘待推荐项目中与用户近期特征相关性较强的特征，将其加强后融入项目表示中；最后，通过评分函数计算用户对待推荐项目的评分。在真实数据集Last.FM、MovieLens-1M和MovieLens-20M上把所提模型和个性化实体推荐（PER）、协同知识嵌入（CKE）、LibFM、RippleNet、知识图卷积网络（KGCN）、协同知识感知注意网络（CKAN）等知识图谱推荐模型进行对比。实验结果表明，在三个数据集上MTFE相较于表现最优的对比模型的F1性能分别提升了0.78、1.63和1.92个百分点，AUC指标在三个数据集上分别提升了3.94、2.73和1.15个百分点。可见，所提模型相较于对比图谱推荐模型有更好的推荐效果。

CLC Number:

TP181

Suqi ZHANG, Xinxin WANG, Shiyao SHE, Junhua GU. Knowledge graph recommendation model with multiple time scales and feature enhancement[J]. Journal of Computer Applications, 2022, 42(4): 1093-1098.

张素琪, 王鑫鑫, 佘世耀, 顾军华. 融合多时间尺度和特征加强的知识图谱推荐模型[J]. 《计算机应用》唯一官方网站, 2022, 42(4): 1093-1098.

Figures/Tables 9

References 19

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数据集	用户数	项目数	评分数	实体数	关系数
Last.FM	1 872	3 846	92 346	9 366	60
MovieLens-20M	138 159	16 954	13 501 622	102 569	32
MovieLens-1M	66 040	3 210	916 714	5 740	25

数据集	用户数	项目数	评分数	实体数	关系数
Last.FM	1 872	3 846	92 346	9 366	60
MovieLens-20M	138 159	16 954	13 501 622	102 569	32
MovieLens-1M	66 040	3 210	916 714	5 740	25

数据集	d	L	M	λ	Lr	Batch
Last.FM	8	3	20	10^-4	5×10^-4	128
MovieLens-20M	32	2	30	10^-7	2×10^-2	65 536
MovieLens-1M	32	2	30	10^-7	2×10^-2	6 218

数据集	d	L	M	λ	Lr	Batch
Last.FM	8	3	20	10^-4	5×10^-4	128
MovieLens-20M	32	2	30	10^-7	2×10^-2	65 536
MovieLens-1M	32	2	30	10^-7	2×10^-2	6 218

模型	Last.FM		MovieLens-1M		MovieLens-20M
模型	AUC	F1	AUC	F1	AUC	F1
PER	0.633 1	0.596 7	0.721 4	0.698 1	0.832 9	0.788 4
CKE	0.744 2	0.673 2	0.783 8	0.762 9	0.924 5	0.880 6
LibFM	0.776 8	0.709 2	0.814 4	0.791 3	0.966 1	0.914 7
RippleNet	0.784 6	0.715 1	0.849 7	0.816 9	0.968 0	0.922 1
KGCN	0.796 9	0.718 6	0.867 4	0.829 4	0.976 0	0.931 2
CKAN	0.797 7	0.730 6	0.862 3	0.823 5	0.970 2	0.930 6
MTFE	0.837 1	0.738 4	0.894 7	0.845 7	0.987 5	0.950 4

Knowledge graph recommendation model with multiple time scales and feature enhancement

融合多时间尺度和特征加强的知识图谱推荐模型

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Metrics

数据集	AUC
数据集	L=1	L=2	L=3	L=4
Last.FM	0.826 1	0.835 1	0.838 1	0.837 2
MovieLens-20M	0.889 8	0.891 4	0.895 7	0.892 6

数据集	AUC
数据集	M=10	M=20	M=30	M=40
Last.FM	0.813 5	0.838 1	0.836 4	0.834 7
MovieLens-20M	0.862 1	0.885 7	0.891 4	0.881 2

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