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Multi-source serialization cross-domain recommendation algorithm based on deep learning
- 1 Dalian Polytechnic University
- 2 Dalian Polytechnic University
- 3 ,Dalian Polytechnic University
* Author to whom correspondence should be addressed.
Abstract
Cross-domain recommendation is an effective approach to solve the cold start and data sparsity problems in recommendation systems. Sequential recommendation can model user behavior sequences and improve the accuracy of recommendation. Currently, few recommendation algorithms consider both aspects together, and most of them do not utilize multi-source information sufficiently. In view of this, this paper proposes a multi-source serialization cross-domain recommendation model, which fully considers the temporal and contextual relationships in two domains, and fuses multi-source information on the basis of achieving cross-domain recommendation tasks, and reinforce the embedding representation by fitting the interest forgetting function. Finally, use a Multilayer Perceptron as the mapping function to learn the nonlinear mapping relationship between the source domain and the target domain, subsequently enabling recommendations for new users in the target domain. On Amazon dataset, this model can significantly enhance the accuracy of recommendation.
Keywords
Cross-Domain Recommendation, Multi-Source Information, Serialization, Interest Forgetting Curve
[1]. Liu Huiting,Guo Lingling,Li Peipei,Zhao Peng,Wu Xindong. Collaborative filtering with a deep adversarial and attention network for cross-domain recommendation[J]. Information Sciences,2021,565.
[2]. Zhang L, Ge Y, Ma J, et al. Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation[J]. arXiv preprint arXiv:2206.13255, 2022.
[3]. Li P, Brost B, Tuzhilin A. Adversarial Learning for Cross Domain Recommendations[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2022.
[4]. Sahu A K. User and item spaces transfer from additional domains for cross-domain recommender systems[J]. Applied Intelligence, 2023, 53(5): 5766-5783.
[5]. Ai Z, Wang S, Jia S, et al. Core Interests Focused Self-attention for Sequential Recommendation[C]//International Conference on Database Systems for Advanced Applications. Springer, Cham, 2022: 306-314.
[6]. Guo J, Ji W, Yuan J, et al. Multi-channel Orthogonal Decomposition Attention Network for Sequential Recommendation[C]//Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham, 2022: 288-300.
[7]. Kheldouni A, Boumhidi J. A Study of Bidirectional Encoder Representations from Transformers for Sequential Recommendations[C]//2022 International Conference on Intelligent Systems and Computer Vision (ISCV).IEEE, 2022: 1-5.
[8]. Chen L, Zhu H. Behavior prediction based on a Commodity Utility-Behavior Sequence model[J]. Machine Learning with Applications, 2022, 9: 100314.
[9]. Hou Y, Mu S, Zhao W X, et al. Towards Universal Sequence Representation Learning for Recommender Systems[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022: 585-593.
[10]. Chen J, Wang C, Wang J. Modeling the interest-forgetting curve for music recommendation[C]//Proceedings of the 22nd ACM international conference on Multimedia. 2014: 921-924.
[11]. Man T, Shen H, Jin X, et al. Cross-domain recommendation: An embedding and mapping approach[C]//IJCAI. 2017, 17: 2464-2470.
[12]. Kang S K, Hwang J, Lee D, et al. Semi-supervised learning for cross-domain recommendation to cold-start users[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019: 1563-1572.
[13]. Zhu Y, Ge K, Zhuang F, et al. Transfer-meta framework for cross-domain recommendation to cold-start users[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021: 1813-1817.
Cite this article
Chen,Z.;Wang,Z.;Wang,X. (2024). Multi-source serialization cross-domain recommendation algorithm based on deep learning. Applied and Computational Engineering,44,150-159.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
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