Recommendation Algorithm Integrating Collaborative Knowledge Graph and Optimizing Graph Attention Network

doi:10.3778/j.issn.1002-8331.2201-0183

Abstract

Abstract: Considering the problems of data sparseness and high model complexity in recommendation algorithms, this paper proposes a recommendation model that integrates collaborative knowledge graphs and optimized graph attention networks. Firstly, the knowledge graph and the user-item interaction graph are combined into a collaborative knowledge graph and embedded into the optimized graph attention network model, which cannot only alleviate the data sparsity problem well, but also mine potential interests and high order relationship of users. Secondly, using an optimized graph convolutional network, by removing feature transformation and nonlinear activation modules, the model complexity can be greatly reduced without affecting the overall recommendation performance. Combined with the deviation-based attention mechanism, the deviation between the candidate item and the user’s real interest item can be sensed in time to improve the training efficiency of the model. Finally, simulation experiments are carried out on the Movielens dataset and the Double dataset, and it is concluded that the recommended performance and time complexity of the algorithm are effectively improved compared with the comparison algorithm.

Key words: knowledge graph, graph convolutional network, attention mechanism, recommendation algorithm

摘要： 考虑到推荐算法存在数据稀疏及模型复杂度较高等问题，提出了一种融合协同知识图谱与优化图注意网络的推荐模型。将用户/项目知识图谱与用户-项目交互图结合为协同知识图谱，嵌入到优化的图注意网络模型中，这不仅可以很好地缓解数据稀疏问题，还能更大程度地挖掘用户的潜在兴趣和高阶关系；使用优化的图卷积网络，通过去除特征转换和非线性激活模块，可以在不影响整体推荐性能的基础上极大地降低模型复杂度；结合基于偏差的注意力机制，及时感知候选项目与用户真实感兴趣项目之间的偏差，提升模型的训练效率。在Movielens数据集和Douban数据集上进行仿真实验，结果表明该算法在推荐性能和时间复杂度方面，相比对比算法均得到了有效的提升。

关键词: 知识图谱, 图卷积网络, 注意力机制, 推荐算法

TANG Hong, FAN Sen, TANG Fan. Recommendation Algorithm Integrating Collaborative Knowledge Graph and Optimizing Graph Attention Network[J]. Computer Engineering and Applications, 2022, 58(19): 98-106.

唐宏, 范森, 唐帆. 融合协同知识图谱与优化图注意网络的推荐算法[J]. 计算机工程与应用, 2022, 58(19): 98-106.

References

[1] LI Y，WANG H J，LIU H L，et al.A study on content-based video recommendation[C]//Proceedings of the 2017 IEEE International Conference on Image Processing（ICIP），2017：4581-4585.
[2] LI K，WAN P Z，ZHANG D Z.Collaborative filtering recommendation algorithm based on improved user similarity measure and scoring forecast[J].Journal of Chinese Computer Systems，2018，39（3）：567-571.
[3] 刘纵横，汪海涛，姜瑛，等.基于混合神经网络的序列推荐算法[J].重庆邮电大学学报（自然科学版），2021，33（3）：466-474.
LIU Z H，WANG H T，JIANG Y，et al.Sequence recommendation algorithm based on hybrid neural network[J].Journal of Chongqing University of Posts and Telecommunications（Natural Science Edition），2021，33（3）：466-474.
[4] WANG H W，ZHANG F Z，ZHAO M，et al.Multi-task feature learning for knowledge graph enhanced recommendation[C]//Proceedings of the 2019 Wide Web Conference（WWW’19），2019：2000-2010.
[5] FAN W，MA Y，LI Q，et al.Graph neural networks for social recommendation[C]//Proceedings of the 2019 World Wide Web Conference（WWW’19），2019：417-426.
[6] 李想，杨兴耀，于炯，等.基于知识图谱卷积网络的双端推荐算法[J].计算机科学与探索，2022，16（1）：176-184.
LI X，YANG X Y，YU J，et al.Double end knowledge graph convolutional networks for recommender systems[J].Journal of Frontiers of Computer Science and Technology，2022，16（1）：176-184.
[7] 潘承瑞，何灵敏，胥智杰，等.融合知识图谱的双线性图注意力网络推荐算法[J].计算机工程与应用，2021，57（1）：29-37.
PAN C R，HE L M，XU Z J，et al.Fusion knowledge graph and bilinear graph attention network recommendation algorithm[J].Computer Engineering and Applications，2021，57（1）：29-37.
[8] WANG X，HE X，WANG M，et al.Neural graph collaborative filtering[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval，2019：165-174.
[9] HE X N，DENG K，WANG X，et al.LightGCN：Simplifying and powering graph convolution network for recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval，2020：639-648.
[10] WANG H W，ZHAO M，XIE X，et al.Knowledge graph convolutional networks for recommender systems[C]//Proceedings of the 2019 World Wide Web Conference（WWW'19），2019：3307-3313.
[11] 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 & Data Mining，2019：950-958.
[12] 郑诚，黄夏炎.联合轻量图卷积网络和注意力机制的推荐方法[J].小型微型计算机系统，2021，42（12）：2525-2529.
ZHENG C，HUANG X Y.Recommendation method combining light graph convolution network and attention[J].Journal of Chinese Computer Systems，2021，42（12）：2525-2529.
[13] CHEN L，WU L，HONG R C，et al.Revisiting graph based collaborative filtering：A linear residual graph convolutional[J].arXiv：2001.10167，2020.
[14] KIPF T N，MAX W.Semi-supervised classification with graph convolutional networks[J].arXiv：1609.02907，2016.
[15] FELIX W，AMAURI H.SOUZA J，et al.Simplifying graph convolutional networks[C]//Proceedings of International Conference on Machine Learning，2019：6861-6871.
[16] THOMAS N，KIPF，MAX W.Semi-supervised classification with graph convolutional networks[C]//Proceedings of the 5th International Conference on Learning Representations，2017：14-28．
[17] 唐宏，范森，唐帆，等.融合知识图谱与注意力机制的推荐算法[J].计算机工程与应用，2022，58（5）：94-103.
TANG H，FAN S，TANG F，et al.Recommendation algorithm combining knowledge graph and attention mechanism[J].Computer Engineering and Applications，2022，58（5）：94-103..
[18] ZHU H M，FENG F L，HE X N，et al.2020.Bilinear graph neural network with neighbor interactions[J].arXiv：2002.03575，2020.