A Recommendation Approach Based on Heterogeneous Network and Dynamic Knowledge Graph
Authors: 
Shanshan Wan, Yuquan Wu, Ying Liu, Linhu Xiao, Maozu Guo Academic Editor: Yu-an TanAuthors Info & Claims
Abstract
Besides data sparsity and cold start, recommender systems often face the problems of selection bias and exposure bias. These problems influence the accuracy of recommendations and easily lead to overrecommendations. This paper proposes a recommendation approach based on heterogeneous network and dynamic knowledge graph (HN-DKG). The main steps include (1) determining the implicit preferences of users according to user’s cross-domain and cross-platform behaviors to form multimodal nodes and then building a heterogeneous knowledge graph; (2) Applying an improved multihead attention mechanism of the graph attention network (GAT) to realize the relationship enhancement of multimodal nodes and constructing a dynamic knowledge graph; and (3) Leveraging RippleNet to discover user’s layered potential interests and rating candidate items. In which, some mechanisms, such as user seed clusters, propagation blocking, and random seed mechanisms, are designed to obtain more accurate and diverse recommendations. In this paper, the public datasets are used to evaluate the performance of algorithms, and the experimental results show that the proposed method has good performance in the effectiveness and diversity of recommendations. On the MovieLens-1M dataset, the proposed model is 18%, 9%, and 2% higher than KGAT on F1, NDCG@10, and AUC and 20%, 2%, and 0.9% higher than RippleNet, respectively. On the Amazon Book dataset, the proposed model is 12%, 3%, and 2.5% higher than NFM on F1, NDCG@10, and AUC and 0.8%, 2.3%, and 0.35% higher than RippleNet, respectively.
References
[1]
M. Zhang, G. Wang, L. Ren, J. Li, K. Deng, and B. Zhang, “METoNR: a meta explanation triplet oriented news recommendation model,” Knowledge-Based Systems, vol. 238, 2022.
[2]
B. Koo, H. Jeon, and U. Kang, “PGT: news recommendation coalescing personal and global temporal preferences,” Knowledge and Information Systems, vol. 63, no. 12, pp. 3139–3158, 2021.
[3]
H. Werneck, N. Silva, A. Pereira, M. Carvalho, A. Bellogin, J. Martinez-Gil, F. Mourão, and L. Rocha, “A reproducible POI recommendation framework: works mapping and benchmark evaluation,” Information Systems, vol. 108, 2022.
[4]
L. Zhang, Z. Sun, J. Zhang, Y. Wu, and Y. Xia, “Conversation-based adaptive relational translation method for next poi recommendation with uncertain check-ins,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 10, pp. 7810–7823, 2023.
[5]
B. Mulyawan, Vionelsy, and T. Sutrisno, “Product recommendation system on building materials shopping using FP-Growth algorithm,” IOP Conference Series: Materials Science and Engineering, vol. 1007, no. 1, 2020.
[6]
Y. Zhu, Q. Lin, H. Lu, K. Shi, D. Liu, J. Chambua, S. Wan, and Z. Niu, “Recommending learning objects through attentive heterogeneous graph convolution and operation-aware neural network,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 4178–4189, 2023.
[7]
F. J. Tey, T. Y. Wu, C. L. Lin, and J. L. Chen, “Accuracy improvements for cold-start recommendation problem using indirect relations in social networks,” Journal of Big Data, vol. 8, no. 1, pp. 98–118, 2021.
[8]
N. Silva, D. Carvalho, A. C. M. Pereira, F. Mourão, and L. Rocha, “The pure cold-start problem: a deep study about how to conquer first-time users in recommendations domains,” Information Systems, vol. 80, pp. 1–12, 2019.
[9]
Y. Wang, “Research on recommendation algorithm of education platform for data sparsity,” Journal of Jiamusi University (Natural Science Edition), vol. 39, no. 6, pp. 137–139+166, 2021.
[10]
B. Xu, “Research on interpretable recommendation method for sparse data,” Sichuan University, Sichuan, China, 2021, MA thesis.
[11]
H. Wang, F. Zhang, M. Zhao, W. Li, X. Xie, and M. Guo, “Multi-task feature learning for knowledge graph enhanced recommendation,” in Proceedings of the The world wide web conference, San Francisco, CA, USA, May 2019.
[12]
S. Zhu, J. Yang, J. Zhang, A. Bozzon, L. K. Huang, and C. Xu, “Recurrent knowledge graph embedding for effective recommendation,” in Proceedings of the 12th ACM conference on recommender systems, pp. 297–305, Vancouver, Canada, October 2018.
[13]
H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li, X. Jie, and M. Guo, “Ripplenet: propagating user preferences on the knowledge graph for recommender systems,” in Proceedings of the 27th ACM international conference on information and knowledge management, pp. 417–426, Torino, Italy, October 2018.
[14]
B. Yuan, Y. Liu, J. Y. Hsia, Z. Dong, and C. J. Lin, “Unbiased Ad click prediction for position-aware advertising systems,” in Proceedings of the 14th ACM Conference on Recommender Systems, pp. 368–377, Brazil, September 2020.
[15]
T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay, “Accurately interpreting clickthrough data as implicit feedback,” Acm Sigir Forum, vol. 51, no. 1, pp. 154–161, 2017.
[16]
S. Wan, S. Yang, Y. Liu, J. Ding, D. Qiu, and C. Wei, “An image recommendation algorithm based on target alternating attention and user affiliation network,” Applied Sciences, vol. 13, no. 7, p. 4389, 2023.
[17]
Y. Lu, C. Shi, L. Hu, and Z. Liu, “Relation structure-aware heterogeneous information network embedding,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 4456–4463, 2019.
[18]
C. Shi, B. Hu, W. X. Zhao, and P. S. Yu, “Heterogeneous information network embedding for recommendation,” IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 2, pp. 357–370, 2019.
[19]
J. Zhao, Z. Zhou, Z. Guan, W. Zhao, W. Ning, G. Qiu, and X. He, “Intentgc: a scalable graph convolution framework fusing heterogeneous information for recommendation,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2347–2357, Anchorage, AK, USA, August 2019.
[20]
J. Ding, Y. Quan, X. He, Y. Li, and D. Jin, “Reinforced negative sampling for recommendation with exposure data,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), pp. 2230–2236, Macao, China, August 2019.
[21]
X. Wang, Y. Xu, X. He, Y. Cao, M. Wang, and T. S. Chua, “Reinforced negative sampling over knowledge graph for recommendation,” in Proceedings of the Web Conference 2020 (WWW '20), pp. 99–109, Taipei, Taiwan, April 2020.
[22]
X. Wang, R. Zhang, Y. Sun, and J. Qi, “Doubly robust joint learning for recommendation on data missing not at random,” International Conference on Machine Learning, vol. 97, pp. 6638–6647, 2019.
[23]
W. Zhang, W. Bao, X. Y. Liu, K. Yang, Q. Lin, H. Wen, and R. Ramezani, “Large-scale causal approaches to debiasing post-click conversion rate estimation with multi-task learning,” in Proceedings of the Web Conference 2020, Taipei, Taiwan, April 2020.
[24]
L. Yang, Z. Xiao, W. Jiang, Y. Wei, Y. Hu, and H. Wang, “Dynamic heterogeneous graph embedding using hierarchical attentions,” in Proceedings of the Advances in Information Retrieval: 42nd European Conference on IR Research, pp. 425–432, ECIR, Lisbon, Portugal, April 2020.
[25]
J. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, “Attention-based models for speech recognition,” Advances in Neural Information Processing Systems, vol. 1, pp. 577–585, 2015.
[26]
Y. Gong and Q. Zhang, “Hashtag recommendation using attention-based convolutional neural network,” in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 577–585, IJCAI'16, New York, NY, USA, July 2016.
[27]
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in Proceedings of the ICLR 2018, Vancouver, Canada, May 2018.
[28]
X. Li, X. Zhang, P. Wang, and Z. Cao, “Web services recommendation based on Metapath-guided graph attention network,” The Journal of Supercomputing, vol. 78, no. 10, pp. 12621–12647, 2022.
[29]
Z. Zhang, J. Huang, and Q. Tan, “Association rules enhanced knowledge graph attention network,” Knowledge-Based Systems, vol. 239, 2022.
[30]
W. Zhang, W. Bao, X. Y. Liu, K. Yang, Q. Lin, H. Wen, and R. Ramezani, “Large-scale causal approaches to debiasing post-click conversion rate estimation with multi-task learning,” in Proceedings of the Web Conference 2020, pp. 2775–2781, Taipei, Taiwan, April 2020.
[31]
S. Guo, L. Zou, Y. Liu, W. Ye, S. Cheng, S. Wang, H. Chen, D. Yin, and Y. Chang, “Enhanced doubly robust learning for debiasing post-click conversion rate estimation,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 275–284, Canada, July 2021.
[32]
Z. Huang, Y. Cheng, R. Cheng, Y. Sun, N. Mamoulis, and X. Li, “Meta structure: computing relevance in large heterogeneous information networks,” in Proceedings of the 22nd ACM SIGKDD International conference on knowledge discovery and data mining, pp. 1595–1604, San Francisco, CA, USA, August 2016.
[33]
X. Yu, X. Ren, Q. Gu, Y. Sun, and J. Han, “Collaborative filtering with entity similarity regularization in heterogeneous information networks,” in Proceedings of the International Joint Conference on Artificial Intelligence Heterogeneous Information Network Analysis 27, Beijing, China, August 2013.
[34]
C. Shi, X. Kong, Y. Huang, P. S Yu, and B. Wu, “Hetesim: a general framework for relevance measure in heterogeneous networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 10, pp. 2479–2492, 2014.
[35]
H. Wang, F. Zhang, X. Xie, and M. Guo, “DKN: deep knowledge-aware network for news recommendation,” in Proceedings of the 2018 world wide web conference, Lyon, France, April 2018.
[36]
C. Shi and P. S. Yu, Heterogeneous Information Network Analysis and Applications, Springer International Publishing, Cham, Switzerland, 2017.
[37]
Y. Wang, Y. Wu, and J. Wu, “Research on recommendation model for multi label system based on heterogeneous network,” Journal of Software, vol. 28, no. 10, pp. 2611–2624, 2017.
[38]
B. Hu, C. Shi, W. X. Zhao, and P. S. Yu, “Leveraging meta-path based context for top-n recommendation with a neural co-attention model,” in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 1531–1540, London, UK, August 2018.
[39]
W. Feng and J. Wang, “Incorporating heterogeneous information for personalized tag recommendation in social tagging systems,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12), pp. 1276–1284, Beijing, China, August 2012.
[40]
C. Shi, Z. Zhang, P. Luo, P. S. Yu, Y. Yue, and B. Wu, “Semantic path based personalized recommendation on weighted heterogeneous information networks,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 453–462, Melbourne, Australia, October 2015.
[41]
X. Yu, X. Ren, Y. Sun, B. Sturt, U. Khandelwal, Q. Gu, B. Norick, and J. Han, “Recommendation in heterogeneous information networks with implicit user feedback,” in Proceedings of the 7th ACM conference on Recommender systems, pp. 347–350, Hong Kong, China, October 2013.
[42]
X. Wang, X. He, Y. Cao, M. Liu, and T. S. Chua, “Kgat: knowledge graph attention network for recommendation,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, Anchorage, AK, USA, August 2019.
[43]
X. Wang, “Research on relational metric social recommendation algorithm based on graph attention network,” Beijing Jiaotong University, Beijing, China, 2021, MA thesis.
[44]
Y. Zeng and S. Liu, “Research on recommendation algorithm of Graph attention Network based on Knowledge graph,” Journal of Physics: Conference Series, vol. 2113, no. 1, 2021.
[45]
S. Wan and Y. Liu, “A security detection approach based on autonomy-oriented user sensor in social recommendation network,” International Journal of Distributed Sensor Networks, vol. 18, no. 3, 2022.
[46]
Y. Luo, “Research on file recommendation methods based on Cn RippleNet,” Northwest University for Nationalities, Lanzhou, China, 2022, MA thesis.
[47]
C. Shi, “Research on improved RippleNet recommendation method,” Huazhong University of Science and Technology, Wuhan, China, 2022, MA thesis.
[48]
Y. Luo, B. Sha, and T. Xu, “A recommended method based on the weighted RippleNet network mode,” Journal of Physics: Conference Series, vol. 2025, no. 1, 2021.
[49]
Y. Wang, L. Dong, Y. Li, and H. Zhang, “Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet,” PLoS One, vol. 16, no. 5, 2021.
[50]
X. S. Ji, Y. B. Liu, and L. M. Luo, “Similarity measurement based on user interest in collaborative filtering,” Journal of Computer Applications, vol. 30, no. 10, pp. 2618–2620, 2010.
[51]
J. Yang and L. Gao, “Application of prospective selection sampling algorithm in product recommendation,” Computer Applications, vol. 9, no. 9, pp. 2177–2178, 2005.
[52]
J. Xu, X. Wang, H. Zhang, and P. Lv, “Heterogeneous and clustering-enhanced personalized preference transfer for cross-domain recommendation,” Information Fusion, vol. 99, pp. 101892–102535, 2023.
[53]
T. Man, H. Shen, X. Jin, and X. Cheng, “Cross-domain recommendation: an embedding and mapping approach,” in Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), pp. 2464–2470, Melbourne, Australia, August 2017.
[54]
C. Chen, M. Zhang, Y. Zhang, W. Ma, Y. Liu, and S. Ma, “Efficient heterogeneous collaborative filtering without negative sampling for recommendation,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 1, pp. 19–26, 2020.
[55]
F. Zhang, J. N. Yuan, D. Lian, X. Xie, and W. Y. Ma, “Collaborative knowledge base embedding for recommender systems,” in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 353–362, San Francisco, CA, USA, August 2016.
[56]
X. He and T. S. Chua, “Neural factorization machines for sparse predictive analytics,” in Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 355–364, Tokyo, Japan, August 2017.
[57]
B. Antoine, N. Usunier, A. Garcia-Durán, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS'13), pp. 2787–2795, Lake Tahoe, NV, USA, December 2013.
[58]
D. Bolya, C. Y. Fu, X. Dai, P. Zhang, and J. Hoffman, “Hydra attention: efficient attention with many heads,” in Proceedings of the Computer Vision– ECCV 2022 Workshops, ECCV, Tel Aviv, Israel, October 2022.
[59]
M. Pan, “Research on social recommender system based on graph attention network,” Northeast Normal University, Changchun, China, 2021, MA thesis.
[60]
N. Hurley and M. Zhang, “Novelty and diversity in top-n recommendation--analysis and evaluation,” ACM Transactions on Internet Technology, vol. 10, no. 4, pp. 1–30, 2011.
[61]
S. Vargas and P. Castells, “Rank and relevance in novelty and diversity metrics for recommender systems,” in Proceedings of the fifth ACM conference on Recommender systems (RecSys '11), pp. 109–116, Chicago, IL, USA, October 2011.
Index Terms
- A Recommendation Approach Based on Heterogeneous Network and Dynamic Knowledge Graph
Index terms have been assigned to the content through auto-classification.
Recommendations
User's Interests-Based Movie Recommendation in Heterogeneous Network
IIKI '15: Proceedings of the 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI)For large data set of movies and various users' interests, recommender systems should pay attention to time consumption and personal interest. However, existing information filtering techniques rarely research users' interests in movies. In order to ...
Content-aware Recommendation via Dynamic Heterogeneous Graph Convolutional Network
AbstractWith the explosive growth of products and multimedia contents on the Internet, the desire of users to find these online resources matching their interests makes it imperative to develop high quality recommendation systems. In this paper, we ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2024 Shanshan Wan et al.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher
John Wiley and Sons Ltd.
United Kingdom
Publication History
Published: 03 January 2024
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 10 Nov 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in