Knowledge Graph Multi-Target Cross-Domain Recommendation on Digital Cultural Resources

doi:10.3778/j.issn.1002-8331.2201-0129

Abstract

Abstract: Digital cultural resources are rich and diverse. Considering the diversity and heterogeneity of resource types, the recommendation of digital cultural resources can be divided into several different subdomains. However, most of the current recommendation methods only aim at single domain, which are unable to capture the propagation of user preferences among multiple domains and make effective use of the information provided by other domains. Therefore, a know-
ledge graph multi-target cross-domain recommendation model（KGMT） is proposed. Firstly, the relationship between different domains is constructed through knowledge graph, and the global domain embedding of users and items is generated. Then, a fusion attention module based on self-attention mechanism is adopted to combine the embedding representation of target domain and global domain. The whole information is effectively used to improve each target domain. Finally, several experiments are carried out on the real-world datasets of Douban and national culture cloud platform. The experimental results show that the performance of KGMT is better than the baselines, and the evaluating indicators of target domains are improved.

Key words: recommendation system, cross-domain recommendation, knowledge graph, data heterogeneity, digital cultural resources

摘要： 数字文化资源具有资源丰富、种类繁多等特点。针对数字文化资源的推荐，考虑到其资源类型的异构多样性，又可以划分为多个不同的子类别域。然而目前大多数的推荐方法仅针对单个物品类别域，无法捕获用户偏好在多个域之间的传播，并有效地利用其他域所提供的信息。基于此，一种基于知识图谱的多目标跨域推荐模型（knowledge graph multi-target cross-domain recommendation model，KGMT）被提出。首先通过知识图谱构建不同域之间的联系，并生成其中有关用户和物品的全局域嵌入。然后采用一种基于自注意力机制的融合注意力模块来联合目标域和全局域的嵌入表征，有效地利用全局域信息来提高每个目标域的推荐效果。最后分别在豆瓣和国家文化云平台的真实数据集上进行了多组实验，实验结果证明KGMT的表现优于基线模型，同时提高了多个目标域的推荐指标。

关键词: 推荐系统, 跨域推荐, 知识图谱, 数据异构, 数字文化资源

TONG Xiaokai, ZHU Xinjuan, WANG Xihan, HU Zhulin. Knowledge Graph Multi-Target Cross-Domain Recommendation on Digital Cultural Resources[J]. Computer Engineering and Applications, 2023, 59(10): 142-150.

童小凯, 朱欣娟, 王西汉, 胡竹林. 数字文化资源知识图谱多目标跨域推荐方法[J]. 计算机工程与应用, 2023, 59(10): 142-150.

References

[1] 项琳.国家文化惠民工程在全面建成小康社会中的成就、贡献与经验[J].图书馆论坛，2021，41（12）：29-34.
XIANG L.Achievements，contribution and experience of China’s cultural benefits project[J].Library Tribune，2021，41（12）：29-34.
[2] 罗云川，李彤.公共文化资源共享治理策略探析[J].图书馆工作与研究，2016（4）：28-32.
LUO Y C，LI T.The governance strategy of public cultural resources sharing[J].Library Work and Study，2016（4）：28-32.
[3] BATMAZ Z，YUREKLI A，BILGE A，et al.A review on deep learning for recommender systems：challenges and remedies[J].Artificial Intelligence Review，2019，52（1）：1-37.
[4] 冀振燕，吴梦丹，杨春，等.可扩展的融合多源异构数据的推荐模型[J].北京邮电大学学报，2021，44（3）：106-111.
JI Z Y，WU M D，YANG C，et al.Scalable recommendation models fusing multi-source heterogeneous data[J].Journal of Beijing University of Posts and Telecommunications，2021，44（3）：106-111.
[5] 朱欣娟，童小凯，王西汉，等.一种融合协同因子的知识图谱传播推荐模型[J].西安工程大学学报，2022，36（2）：1-9.
ZHU X J，TONG X K，WANG X H，et al.A propagation-based knowledge graph recommender model with collaborative factor[J].Journal of Xi’an Polytechnic University，2022，36（2）：1-9.
[6] 秦川，祝恒书，庄福振，等.基于知识图谱的推荐系统研究综述[J].中国科学：信息科学，2020，50（7）：937-956.
QIN C，ZHU H S，ZHUANG F Z，et al.A survey on knowledge graph-based recommender systems[J].Scientia Sinica Informationis，2020，50（7）：937-956.
[7] SANCHEZ-LENGELING B，REIF E，PEARCE A，et al.A gentle introduction to graph neural networks[J].Distill，2021，6（9）：e33.
[8] ZHU F，WANG Y，CHEN C，et al.Cross-domain recommendation：challenges，progress，and prospects[C]//Proceedings of the 30th International Joint Conference on Artificial Intelligence，2021：4721-4728.
[9] LEUNG C W，CHAN S C，CHUNG F.Applying cross-level association rule mining to cold-start recommendations[C]//Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology-Workshops，2007：133-136.
[10] WANG J，LV J.Tag-informed collaborative topic modeling for cross domain recommendations[J].Knowledge-Based Systems，2020，203：106119.
[11] ZHU F，WANG Y，CHEN C，et al.A deep framework for cross-domain and cross-system recommendations[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence，2018：3711-3717.
[12] FU W，PENG Z，WANG S，et al.Deeply fusing reviews and contents for cold start users in cross-domain recommendation systems[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence，2019：94-101.
[13] LU Y，DONG R，SMYTH B.Why I like it：multi-task learning for recommendation and explanation[C]//Proceedings of the 12th ACM Conference on Recommender Systems，2018：4-12.
[14] HU G，ZHANG Y，YANG Q.Transfer meets hybrid：a synthetic approach for cross-domain collaborative filtering with text[C]//The 2019 World Wide Web Conference，2019：2822-2829.
[15] YUAN F，YAO L，BENATALLAH B.DARec：deep domain adaptation for cross-domain recommendation via transferring rating patterns[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence，2019：4227-4233.
[16] LI P，TUZHILIN A.DDTCDR：deep dual transfer cross domain recommendation[C]//Proceedings of the 13th International Conference on Web Search and Data Mining，2020：331-339.
[17] ZHU F，CHEN C，WANG Y，et al.DTCDR：a framework for dual-target cross-domain recommendation[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management，2019：1533-1542.
[18] LIU M，LI J，LI G，et al.Cross domain recommendation via bi-directional transfer graph collaborative filtering networks[C]//Proceedings of the 29th ACM International Conference on Information and Knowledge Management，2020：885-894.
[19] ZHU F，WANG Y，CHEN C，et al.A graphical and attentional framework for dual-target cross-domain recommendation[C]//Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence，2021：3001-3008.
[20] KRISHNAN A，DAS M，BENDRE M，et al.Transfer learning via contextual invariants for one-to-many cross-domain recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval，2020：1081-1090.
[21] LI J，PENG Z，WANG S，et al.Heterogeneous graph embedding for cross-domain recommendation through adversarial learning[C]//Proceedings of the 25th International Conference on Database Systems for Advanced Applications，2020：507-522.
[22] WANG Z，DAI Z，POCZOS B，et al.Characterizing and avoiding negative transfer[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：11293-11302.
[23] GUO Q，ZHUANG F，QIN C，et al.A survey on knowledge graph-based recommender systems[J].Scientia Sinica Informationis，2020，50（7）：937-957.
[24] 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，2018：417-426.
[25] WANG H，ZHAO M，XIE X，et al.Knowledge graph convolutional networks for recommender systems[C]//The 2019 World Wide Web Conference，2019：3307-3313.
[26] WANG X，HE X，CAO Y，et al.KGAT：knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，2019：950-958.
[27] LIN Y，LIU Z，SUN M，et al.Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence，2015：2181-2187.
[28] LE Q，MIKOLOV T.Distributed representations of sentences and documents[C]//International Conference on Machine Learning，2014：1188-1196.
[29] GROVER A，LESKOVEC J.Node2vec：scalable feature lear-
ning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，2016：855-864.
[30] ZANG T，ZHU Y，LIU H，et al.A survey on cross-domain recommendation：taxonomies，methods，and future directions[J].arXiv：2108.03357，2021.
[31] 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，2018：1835-1844.
[32] VASWANI A，SHAZEER N，PARMAR N，et al.Attention is all you need[C]//Advances in Neural Information Processing Systems，2017：5998-6008.
[33] HE X，LIAO L，ZHANG H，et al.Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web，2017：173-182.
[34] XUE H J，DAI X，ZHANG J，et al.Deep matrix factorization models for recommender systems[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence，2017：3203-3209.