..............................
..............................
..............................

A Dual-End Recommendation Algorithm Integrating User Intent and Knowledge-Aware
The existing knowledge graph-based recommendation models often lack a fine-grained consideration of collaborative
information between users and items and overlook the high-order semantics and structural relationships within the graph paths.
To address these issues, a dual-end recommendation algorithm integrating User Intent and Knowledge-Aware Graph Attention
Networks (UIKGAN) is proposed. On the user end, the intent behind user-item interactions to refine the representation of
collaborative information is modeled. By propagating relationship paths, UIKGAN aggregates deeper semantic and structural
information from the knowledge graph to more accurately capture the extended representation of user intent and behavior
patterns. On the item end, UIKGAN embeds and aggregates high-order neighboring triplet information using a knowledge-
aware attention mechanism, enriching the feature representation of items. Additionally, this paper introduces an independence
modeling module to optimize the loss function, providing better interpretability of user intent. Experiments were conducted on
three public datasets, including comparative experiments with seven baseline models, ablation studies, hyperparameter
sensitivity experiments, and sparse data issue analysis. The experimental results demonstrate that the UIKGAN model
outperforms other baselines in overall performance, improving recommendation accuracy while effectively alleviating the issue
of dataset sparsity.
[1] Abdi M., Okeyo G., and Mwangi R., “Improved
Collaborative Filtering Recommender System
Based on Hybrid Similarity Measures,” The
International Arab Journal of Information
Technology, vol. 22, no. 1, pp. 99-115, 2025.
https://doi.org/10.34028/iajit/22/1/8
[2] Cao Y., Shang S., Wang J., and Zhang W.,
“Explainable Session-based Recommendation via
Path Reasoning,” arXiv Preprint, vol.
arXiv:2403.00832v1, pp. 1-13, 2024.
https://arxiv.org/abs/2403.00832
[3] Cao Y., Wang X., He X., Hu Z., and Chua T.,
“Unifying Knowledge Graph Learning and
Recommendation: Towards a Better
Understanding of User Preference,” in
Proceedings of the 28th International World Wide
Web Conference, San Francisco, pp. 151-161,
2019. https://doi.org/10.1145/3308558.3313705
[4] Chen J. and Zhang W., “Review of Point of
Interest Recommendation Systems in Location-
Based Social Networks,” Journal of Frontiers of
Computer Science and Technology, vol. 16, no. 7,
pp. 1462-1478, 2022.
https://doi.org/10.3778/j.issn.1673-9418.2112037
[5] Hamilton W., Ying R., and Leskovec J., “Inductive
Representation Learning on Large Graphs,” in
Proceedings of the 31st International Conference
on Neural Information Processing Systems,
California, pp. 1025-1035, 2017.
https://dl.acm.org/doi/10.5555/3294771.3294869
[6] Ji W., Wang H., Su G., and Liu L., “Review of
Recommendation Methods Based on Association
Rules Algorithm,” Computer Engineering and
Applications, vol. 56, no. 22, pp. 33-41, 2020.
http://cea.ceaj.org/CN/10.3778/j.issn.1002-
8331.2006-0158
[7] Ji Z. and Lv T., “A Dual End Neighbour
Recommendation Algorithm Integrating Graph
Attention and Knowledge Graph Convolutional
Networks,” Journal of Huaibei Normal University
(Natural Science Edition), vol. 45, no. 3, pp. 50-
58, 2024.
https://fmsb.cbpt.cnki.net/WKE2/WebPublication
/paperDigest.aspx?paperID=f5d788e1-b122-
45c0-b52e-b545a5d9c9a2#
[8] Kipf T. and Welling M., “Semi-Supervised
Classification with Graph Convolutional
Networks,” arXiv Preprint, vol.
arXiv:1609.02907v4, pp. 1-14, 2017.
https://10.48550/arXiv.1609.02907
[9] Lin J., Chen S., and Wang J., “Graph Neural
Networks with Dynamic and Static
Representations for Social Recommendation,” in
Proceedings of the 27th International Conference
on Database Systems for Advanced Applications,
Hyderabad, pp. 264-271, 2022.
https://doi.org/10.1007/978-3-031-00126-0_1
[10] Lyu Z., Wu Y., Lai J., Yang M., and Li C., and
Zhou W., “Knowledge Enhanced Graph Neural
Networks for Explainable Recommendation,”
IEEE Transactions on Knowledge and Data
Engineering, vol. 35, no. 5, pp. 4954-4968, 2023.
DOI: 10.1109/TKDE.2022.3142260
[11] Qu Y., Bai T., Zhang W., Nie J., and Tang J., “An
End-to-End Neighbourhood-based Interaction
Model for Knowledge-Enhanced
Recommendation,” in Proceedings of the 1st
International Workshop on Deep Learning
Practice for High-Dimensional Sparse Data,
Anchorage, pp. 1-9, 2019.
https://doi.org/10.1145/3326937.3341257 520 The International Arab Journal of Information Technology, Vol. 22, No. 3, May 2025
[12] Sha X., Sun Z., and Zhang J., “Hierarchical
Attentive Knowledge Graph Embedding for
Personalized Recommendation,” Electronic
Commerce Research and Applications, vol. 48, no.
1, pp. 1-14, 2021.
https://doi.org/10.1016/j.elerap.2021.101071
[13] Tang X., Wang T., Yang H., and Song H.,
“AKUPM: Attention-Enhanced Knowledge-
Aware User Preference Model for
Recommendation,” in Proceedings of the 25th
ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining,
Anchorage, pp. 1891-1899, 2019.
https://doi.org/10.1145/3292500.3330705
[14] The Book-Crossing Dataset,
http://www2.informatik.uni-
freiburg.de/~cziegler/BX/, Last Visited, 2025
[15] The Last.FM Dataset, https://www.last.fm/api,
Last Visited, 2025
[16] The MovieLens 10M Dataset,
https://grouplens.org/datasets/movielens/10m/,
Last Visited, 2025
[17] Tian X. and Chen H., “Survey on Applications of
Knowledge Graph Embedding in
Recommendation Tasks,” Journal of Frontiers of
Computer Science and Technology, vol. 16, no. 8,
pp. 1681-1705, 2022.
http://fcst.ceaj.org/CN/10.3778/j.issn.1673-
9418.2112070
[18] Wang H., Zhang F., Wang J., Miao Z., Li W., Xie
X., and Guo M., “RippleNet: Propagating User
Preferences on the Knowledge Graph for
Recommender Systems,” in Proceedings of the
27th ACM International Conference on
Information and Knowledge Management, Torino,
pp. 417-426, 2018.
https://doi.org/10.1145/3269206.3271739
[19] Wang H., Zhang F., Zhang M., Leskovec J., Zhao
M., Li W., and Wang Z., “Knowledge-Aware
Graph Neural Networks with Label Smoothness
Regularization for Recommender Systems,” in
Proceedings of the 25th ACM SIGKDD
International Conference on Knowledge
Discovery and Data Mining, Anchorage, pp. 968-
977, 2019.
https://doi.org/10.1145/3292500.3330836
[20] Wang H., Zhao M., Xie X., Li W., and Guo M.,
“Knowledge Graph Convolutional Networks for
Recommender Systems,” in Proceedings of the
World Wide Web Conference, San Francisco, pp.
3307-3313, 2019.
https://doi.org/10.1145/3308558.331341
[21] Wang X., He X., Cao Y., Liu M., and Chua T.,
“KGAT: Knowledge Graph Attention Network for
Recommendation,” in Proceedings of the 25th
ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining,
Anchorage, pp. 950-958, 2019.
https://doi.org/10.1145/3292500.3330989
[22] Wang X., Huang T., Wang D., Yuan Y., Liu Z., He
X., and Chua T., “Learning Intents behind
Interactions with Knowledge Graph for
Recommendation,” in Proceedings of the Web
Conference, Ljubljana, pp. 878-887, 2021.
https://doi.org/10.1145/3442381.3450133
[23] Wang Z., Lin G., Tan H., Chen Q., and Liu X.,
“CKAN: Collaborative Knowledge-Aware
Attentive Network for Recommender Systems,”
in Proceedings of the 43rd International ACM
SIGIR Conference on Research and Development
in Information Retrieval, Xi’an, pp. 219-228,
2020. https://doi.org/10.1145/3397271.3401141
[24] Wang Z., Wang Z., Li X., Yu Z., Guo B., Chen L.,
“Exploring Multi-Dimension User-Item
Interactions with Attentional Knowledge Graph
Neural Networks for Recommendation,” IEEE
Transactions on Big Data, vol. 9, no. 1, pp. 212-
226, 2022. DOI:10.1109/TBDATA.2022.3154778
[25] Xu Z., Liu H., Li J., Zhang Q., and Tang Y.,
“CKGAT: Collaborative Knowledge-Aware
Graph Attention Network for Top-N
Recommendation,” Applied Sciences, vol. 12, no.
3, pp. 1-23, 2022.
https://doi.org/10.3390/app12031669
[26] Yang C., Chen X., Wang C., and Liu T.,
“Recommendation Strategy Based on Users’
Preferences for Fine-Grained Attributes,” Data
Analysis and Knowledge Discovery, vol. 5, no. 10,
pp. 94-102, 2021. DOI: 10.11925/infotech.2096-
3467.2021.0291
[27] Yin Y., Zhu X., Wang W., Zhang Y., Wang P., Fan
Y., and Guo J., “HEC-GCN: Hypergraph
Enhanced Cascading Graph Convolution Network
for Multi-Behavior Recommendation,” arXiv
Preprint, vol. arXiv:2412.14476v1, pp. 1-12,
2024. https://arxiv.org/abs/2412.14476
[28] Zhang M., Zhang X., Liu S., Tian H., and Yang Q.,
“Review of Recommendation Systems Using
Knowledge Graph,” Computer Engineering and
Applications, vol. 59, no. 4, pp. 30-42, 2023.
http://cea.ceaj.org/CN/10.3778/j.issn.1002-
8331.2209-0033
[29] Zhao Y., Liu L., Wang H., Han H., and Pei D.,
“Survey of Knowledge Graph Recommendation
System Research,” Journal of Frontiers of
Computer Science and Technology, vol. 17, no. 4,
pp. 771-791, 2023.
https://doi.org/10.3778/j.issn.1673-9418.2205052
[30] Zou L., Xia L, Gu Y., Zhao X., Liu W., Huang J.,
and Yin D., “Neural Interactive Collaborative
Filtering,” in Proceedings of the 43rd International
ACM SIGIR Conference on Research and
Development in Information Retrieval, Xi’an, pp.
749-758, 2020.
https://doi.org/10.1145/3397271.3401181
A Dual-End Recommendation Algorithm Integrating User Intent and Knowledge-Aware ... 521
Zijie Ji is a Master’s candidate, his
research focuses on Recommendation
Systems.
Teng Lv is a Professor, Ph.D., his
research focuses on Recommendation
Systems and Data Management. He is
the Corresponding author of this
paper.
Yi Yu is a Master’s candidate, his
Research Focuses on
Recommendation Systems.