A Comment Aspect-Level User Preference Transfer Model for Cross-Domain Recommendations
Pages 1 - 25
Abstract
Traditional cross-domain recommendation models make it difficult to deeply mine users' aspect-level preferences from comment information due to existing problems such as polysemy of comment text, sparse comment data, and user cold start. A Cross-Domain Recommender (CDR) model that integrates comment knowledge enhancement and aspect-level user preference transfer (C-KE-AUT) was proposed to address the above issues. Firstly, an aspect-level user preference extraction model was constructed by combining the RoBERTa word embedding model, high-level feature representation based on Transformer, and aspect-level attention-learning methods. Then, a user aspect-level preference cross-domain transfer model was constructed based on a two-stage generative adversarial network that can transfer the aspect-level interest preferences of users in the source domain to the target domain with sparse data. The experimental results on the Amazon 2018 comment dataset indicated that the recommendation performance of the proposed C-KE-AUT model was significantly superior to other advanced comparative models.
References
[1]
Abdi, M. H., Okeyo, G., & Mwangi, R. W. (2018). Matrix factorization techniques for context-aware collaborative filtering recommender systems: A survey. Computer and Information Science, 11(2), 1. Advance online publication.
[2]
Bernardis, C., & Cremonesi, P. (2021). NFC: A deep and hybrid item-based model for item cold-start recommendation. User Modeling and User-Adapted Interaction, 32(4), 747–780.
[3]
ChinJ.ZhaoK.JotyS. R.CongG. (2018). ANR: Aspect-based neural recommender. In CIKM ’18: Proceedings of the 27th ACM international conference on information and knowledge management (pp. 147–156). ACM. 10.1145/3269206.3271810
[4]
Da’u, A., & Salim, N. (2019). Recommendation system based on deep learning methods: A systematic review and new directions. Artificial Intelligence Review, 53(4), 2709–2748.
[5]
Dong, S., Wang, P., & Abbas, K. (2021). A survey on deep learning and its applications. Computer Science Review, 40, 100379. Advance online publication.
[6]
Du, Y., Wang, L., Peng, Z., & Guo, W. (2023). Review-based hierarchical attention cooperative neural networks for recommendation. Neurocomputing, 447, 38–47.
[7]
Fernández-Tobías, I., Cantador, I., Tomeo, P., Anelli Vito, W., & Di Noia, T. (2019). Addressing the user cold start with cross-domain collaborative filtering: Exploiting item metadata in matrix factorization. User Modeling and User-Adapted Interaction, 29(2), 443–486.
[8]
Hong, W., Zheng, N., Xiong, Z., & Hu, Z. (2020). A parallel deep neural network using reviews and item metadata for cross-domain recommendation. IEEE Access : Practical Innovations, Open Solutions, 8(2), 41774–41783.
[9]
HuangM.XiaoN. (2022). A recommendation model of deeply fusion rating matrix and review text. In 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) (pp. 793–796). IEEE. 10.1109/EEBDA53927.2022.9745009
[10]
Huang, W., Hao, F., Shang, J., Yu, W., Zeng, S., Bisogni, C., & Loia, V. (2023). Dual-LightGCN: Dual light graph convolutional network for discriminative recommendation. Computer Communications, 204, 89–100.
[11]
Javed, U., Shaukat, K., Hameed, I., Iqbal, F., Alam, T., & Luo, S. (2021). A review of content-based and context-based recommendation systems. International Journal of Emerging Technologies in Learning, 16(3), 274–306.
[12]
Jeong, C., Jang, S., Park, E., & Choi, S. (2020). A context-aware citation recommendation model with BERT and graph convolutional networks. Scientometrics, 124(3), 1907–1922.
[13]
Lai, X., & Jie, Q. (2023). Deep semantic-level cross-domain recommendation model based on DSV-CDRM. International Journal of Information Technology and Web Engineering, 18(1), 1–20.
[14]
Li, A., & Li, T. (2023). Fusion of XLNet and BiLSTM-TextCNN for Weibo sentiment analysis in Spark big data environment. International Journal of Ambient Computing and Intelligence, 14(1), 1–18.
[15]
LiC.ZhaoM.ZhangH.YuC.ChengL.ShuG.KongB.NiuD. (2022). RecGURU: Adversarial learning of generalized user representations for cross-domain recommendation. In Proceedings of the fifteenth ACM international conference on web search and data mining, (pp. 571–581). ACM. 10.1145/3488560.3498388
[16]
LiP.TuzhilinA. (2020). DDTCDR: Deep dual transfer cross domain recommendation. In Proceedings of the 13th international conference on web search and data mining (pp. 331–339). ACM. 10.1145/3336191.3371793
[17]
Li, S., Zhang, Y., Li, Y., & Yu, Z. (2019). The user preference identification for product improvement based on online comment patch. Electronic Commerce Research, 21(2), 423–444.
[18]
Liu, F., Cheng, Z., Zhu, L., Liu, C., & Nie, L. (2020). An attribute-aware attentive GCN model for attribute missing in recommendation. IEEE Transactions on Knowledge and Data Engineering, 34(9), 4077–4088.
[19]
Lv, S., & Pan, L. (2023). BTSAMA: A personalized music recommendation method combining TextCNN and attention. International Journal of Ambient Computing and Intelligence, 14(1), 1–23.
[20]
Ni, X., Nie, J., Zuo, Z., Xie, H., Liang, X., Jiang, M., Xu, J., Yu, S., & Liu, M. (2023). DITN: User’s indirect side-information involved domain-invariant feature transfer network for cross-domain recommendation. Information Processing & Management, 60(6), 103494. Advance online publication.
[21]
Nie, J., Zhao, Z., Huang, L., Nie, W., & Wei, Z. (2021). Cross-domain recommendation via user-clustering and multidimensional information fusion. IEEE Transactions on Multimedia, 25, 868–880.
[22]
Qian, F., Zhang, C., Feng, L., Lu, C., Zhang, G., & Hu, G. (2020). Tubal-sampling: Bridging tensor and matrix completion in 3-D seismic data reconstruction. IEEE Transactions on Geoscience and Remote Sensing, 59(1), 854–870.
[23]
Sang, S., Yuan, W., Li, W., Yang, Z., Zhang, Z., & Liu, N. (2023). Position-aware graph neural network for session-based recommendation. Knowledge-Based Systems, 262, 110201. Advance online publication.
[24]
Soydaner, D. (2022). Attention mechanism in neural networks: Where it comes and where it goes. Neural Computing & Applications, 34(16), 13371–13385.
[25]
WangC.LiangY.LiuZ.ZhangT.YuP. S. (2021). Pre-training graph neural network for cross domain recommendation. In Proceedings of the 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), 140–145. 10.1109/CogMI52975.2021.00026
[26]
Wei, W. (2021). Multi-channel relation modeling for session-based recommendation with self-supervised learning. In W. Cao, A. Ozcan, H. Xie, & B. Guan, B. (Eds.) CONF-CDS 2021 Communications in computer and information science: Vol. 1513. Computing and data science (pp. 158–170). Springer.
[27]
Wu, S., Zhang, Y., Gao, C., Bian, K., & Cui, B. (2020). GARG: Anonymous recommendation of point-of-interest in mobile networks by graph convolution network. Data Science and Engineering, 5(4), 433–447.
[28]
XieR.LiuQ.WangL.LiuS.ZhangB.LinL. (2022). Contrastive cross-domain recommendation in matching. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining (pp. 4226–4236). ACM. 10.1145/3534678.3539125
[29]
Xu, Z., Lin, H., & Wu, M. (2023). A course recommendation algorithm for a personalized online learning platform for students from the perspective of deep learning. International Journal of Information Technology and Web Engineering, 18(1), 1–17.
[30]
Yang, O., Guo, B., & Tang, X. (2021). Mobile app cross-domain recommendation with multi-graph neural network. ACM Transactions on Knowledge Discovery from Data, 15(4), 55. Advance online publication.
[31]
Zang, T., Zhu, Y., Liu, H., Zhang, R., & Yu, J. (2021). A survey on cross-domain recommendation: Taxonomies, methods, and future direction. ACM Transactions on Information Systems, 41(2), 42. Advance online publication.
[32]
Zhang, J. J., Hua, X., Zhao, P., & Kang, K. (2023). Dual cross-domain session-based recommendation with multi-channel integration. AI Communications, 36(4), 341–359.
[33]
Zhang, Q., Hao, P., & Lu, J. (2019). Cross-domain recommendation with semantic correlation in tagging systems. In 2019 International joint conference on neural networks (IJCNN) (pp. 1–8). IEEE. 10.1109/IJCNN.2019.8852049
[34]
Zhang, T., Chen, C., Wang, D., Guo, J., & Song, B. (2023). A VAE-based user preference learning and transfer framework for cross-domain recommendation. IEEE Transactions on Knowledge and Data Engineering, 35(10), 10383–10396.
[35]
ZhaoC.LiC.XiaoR.DengH.SunA. (2020). CATN: Cross-domain recommendation for cold-start users via aspect transfer network. In SIGIR ’20: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 229–238). ACM. 10.1145/3397271.3401169
[36]
Zhong, S. T., Huang, L., Wang, C. D., Lai, J.-H., & Yu, P. S. (2020). An autoencoder framework with attention mechanism for cross-domain recommendation. IEEE Transactions on Cybernetics, 52(6), 5229–5241. 33156800.
[37]
Zhu, F., Wang, Y., Zhou, J., Chen, C., Li, L., & Liu, G. (2021). A unified framework for cross-domain and cross-system recommendations. IEEE Transactions on Knowledge and Data Engineering, 5(8), 69–78.
[38]
ZhuY.TangZ.LiuY.ZhuangF.XieR.ZhangX.LinL.HeQ. (2021). Personalized transfer of user preferences for cross-domain recommendation. In WSDM ’22: Proceedings of the fifteenth ACM international conference on web search and data mining (pp. 1507–1515). ACM. 10.1145/3488560.3498392
[39]
Zuo, Z., Nie, J., Zhao, Z., Xie, H., Ding, X., Yu, S., Yue, Y., Huang, L., & Wang, X. (2022, July). RPITN: Review based preference invariance transfer network for cross-domain recommendation. In 2022 IEEE international conference on multimedia and expo (ICME) (pp. 1–6). IEEE.
Index Terms
- A Comment Aspect-Level User Preference Transfer Model for Cross-Domain Recommendations
Index terms have been assigned to the content through auto-classification.
Recommendations
CRAS: cross-domain recommendation via aspect-level sentiment extraction
AbstractTo address the problem of sparse data and cold-start when facing new users and items in the single-domain recommendation, cross-domain recommendation has gradually become a hot topic in the recommendation system. This method enhances target domain ...
Personalized Transfer of User Preferences for Cross-domain Recommendation
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningCold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-start recommendations in the target domain. How to transfer user's ...
Domain ranking for cross domain collaborative filtering
UMAP'12: Proceedings of the 20th international conference on User Modeling, Adaptation, and PersonalizationIn recommendation systems a variation of the cold start problem is a situation where the target user has few-to-none item ratings belonging to the target domain (e.g., movies) to base recommendations on. One way to overcome this is by basing ...
Comments
Information & Contributors
Information
Published In
Copyright © 2024.
Publisher
IGI Global
United States
Publication History
Published: 17 September 2024
Author Tags
Qualifiers
- 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 09 Jan 2025