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http://dx.doi.org/10.9717/kmms.2022.25.6.819

Multiple Fusion-based Deep Cross-domain Recommendation  

Hong, Minsung (Dept. of Smart Tourism Research Center, KyungHee University)
Lee, WonJin (Research Institute of Information and Culture Technology, Dankook University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.6, 2022 , pp. 819-832 More about this Journal
Abstract
Cross-domain recommender system transfers knowledge across different domains to improve the recommendation performance in a target domain that has a relatively sparse model. However, they suffer from the "negative transfer" in which transferred knowledge operates as noise. This paper proposes a novel Multiple Fusion-based Deep Cross-Domain Recommendation named MFDCR. We exploit Doc2Vec, one of the famous word embedding techniques, to fuse data user-wise and transfer knowledge across multi-domains. It alleviates the "negative transfer" problem. Additionally, we introduce a simple multi-layer perception to learn the user-item interactions and predict the possibility of preferring items by users. Extensive experiments with three domain datasets from one of the most famous services Amazon demonstrate that MFDCR outperforms recent single and cross-domain recommendation algorithms. Furthermore, experimental results show that MFDCR can address the problem of "negative transfer" and improve recommendation performance for multiple domains simultaneously. In addition, we show that our approach is efficient in extending toward more domains.
Keywords
Deep Learning; Cross-Domain Recommendation; Doc2Vec; Multi-Target Recommendation;
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1 M. Hong and J.J. Jung, "Multi-Criteria Tensor Model for Tourism Recommender Systems," Expert Systems with Applications, Vol. 170, pp. 114537, 2021.   DOI
2 C. Zhao, C. Li, R. Xiao, H. Deng, and A. Sun, "CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network," Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 229-238, 2020.
3 S. Sedhain, A.K. Menon, S. Sanner, and L. Xie, "Autorec: Autoencoders Meet Collaborative Filtering," Proceedings of the 24th international conference on World Wide Web, pp. 111-112, 2015.
4 F. Zhu, Y. Wang, C. Chen, J. Zhou, L. Li, and G. Liu, "Cross-domain Recommendation: Challenges, Progress, and Prospects," arXiv Preprint, arXiv:2103.01696, 2021.
5 F. Zhu, Y. Wang, C. Chen, G. Liu, and X. Zheng, "A Graphical and Attentional Framework for Dual-target Cross-domain Recommendation," Proceedings of the International Joint Conference on Artificial Intelligence, pp. 3001-3008, 2020.
6 D. Khattar, V. Kumar, V. Varma, and M. Gupta, "Weave&rec: A Word Embedding Based 3-D Convolutional Network for News Recommendation," Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1855-1858, 2018.
7 H. Caselles-Dupre, F. Lesaint, and J. Royo-Letelier, "Word2vec Applied to Recommendation: Hyperparameters Matter," Proceedings of the 12th ACM Conference on Recommender Systems, pp. 352-356, 2018.
8 B.P. Chamberlain, E. Rossi, D. Shiebler, S. Sedhain, and M.M. Bronstein, "Tuning Word 2vec for Large Scale Recommendation Systems," Proceedings of the 14th ACM Conference on Recommender Systems, pp. 732-737, 2020.
9 P. Karvelis, D. Gavrilis, G. Georgoulas, and C. Stylios, "Topic Recommendation Using Doc2 Vec," Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-6, 2018.
10 R. Esmeli, M. Bader-El-Den, and H. Abdullahi, "Using Word2Vec Recommendation for Improved Purchase Prediction," Proceedings of the 2020 International Joint Conference on Neural Networks (IJ CNN), pp. 1-8, 2020.
11 L.V. Nguyen, T.H. Nguyen, J.J. Jung, and D. Camacho, "Extending Collaborative Filtering Recommendation Using Word Embedding: A Hybrid Approach," Concurrency and Computation: Practice and Experience, e6232, 2021.
12 A. Dau and N. Salim, "Recommendation System Based on Deep Learning Methods: A Systematic Review and New Directions," Artificial Intelligence Review, Vol. 53, No. 4, pp. 2709-2748, 2020.   DOI
13 R. Salakhutdinov, A. Mnih, and G. Hinton, "Restricted Boltzmann Machines for Collaborative Filtering," Proceedings of the 24th international conference on Machine learning, pp. 791-798, 2007.
14 S. Li, J. Kawale, and Y. Fu, "Deep Collaborative Filtering via Marginalized Denoising Auto-Encoder," Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 811-820, 2015.
15 J. Lin, K. Sugiyama, M.Y. Kan, and T.S. Chua, "Addressing Cold-Start in App Recommendation: Latent User Models Constructed from Twitter Followers," Proceedings of the 36th International ACM SIGIR Conference on Research and development in Information Retrieval, pp. 283-292, 2013.
16 H.H. Lee and W.J. Lee, "Image Label Prediction Algorithm based on Convolution Neural Network with Collaborative Layer," Journal of Korea Multimedia Society, Vol. 23, No. 6, pp. 756-764, 2020.   DOI
17 M. Hong, "Decrease and Conquer-Based Parallel Tensor Factorization for Diversity and Real-Time of Multi-Criteria Recommendation," Information Sciences, Vol. 562, pp. 259-278, 2021.   DOI
18 M.M. Khan, R. Ibrahim, and I. Ghani, "Cross Domain Recommender Systems: A Systematic Literature Review," ACM Computing Surveys (CSUR), Vol. 50, No. 3, pp. 1-34, 2017.
19 T. Man, H. Shen, X. Jin, and X. Cheng, "Cross-Domain Recommendation: An Embedding and Mapping Approach. Proceedings of the International Joint Conference on Artificial Intelligence, Vol. 17, pp. 2464-2470, 2017.
20 T. Zang, Y. Zhu, H. Liu, R. Zhang, and J. Yu, "A Survey on Cross-Domain Recommendation: Taxonomies, Methods, and Future Directions," arXiv Preprint, arXiv:2108.03357, 2021.
21 M.K. Najafabadi, M.B. Nair, and A. Mohamed, "Tag Recommendation Model Using Feature Learning via Word Embedding," Proceedings of the 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 305-310, 2021.
22 F. Xue, X. He, X. Wang, J. Xu, K. Liu, and R. Hong, "Deep Item-based Collaborative Filtering for Top-n Recommendation," ACM Transactions on Information Systems (TOIS), Vol. 37, No. 3, pp. 1-25, 2019.
23 X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.S. Chua, "Neural Collaborative Filtering," Proceedings of the 26th International Conference on World Wide Web, pp. 173-182, 2017.
24 S. Min and Y. Oh, "Implementation of Recipe Recommendation System Using Ingredients Combination Analysis based on Recipe Data," Journal of Korea Multimedia Society, Vol. 24, No. 8, pp. 1114-1121, 2021   DOI
25 J.W. Baek and K.Y. Chung, "Multimedia Recommendation Using Word2Vec-Based Social Relationship Mining," Multimedia Tools and Applications, Vol. 80, No. 26, pp. 34499-34515, 2021.   DOI
26 F. Zhu, C. Chen, Y. Wang, G. Liu, and X. Zheng, "DTCDR: A Framework for DualTarget Cross-Domain Recommendation," Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1533-1542, 2019.
27 Q. Cui, T. Wei, Y. Zhang, and Q. Zhang, "HeroGRAPH: A Heterogeneous Graph Framework for Multi-Target Cross-Domain Recommendation," Proceedings of the 3rd Workshop on Online Recommender Systems and User Modeling co-located with the 14th ACM Conference on Recommender Systems (Rec Sys), 2020.
28 M.G. Ozsoy, "From Word Embeddings to Item Recommendation," arXiv Preprint, arXiv: 1601.01356, 2016.
29 X. He, H. Zhang, M.Y. Kan, and T.S. Chua, "Fast Matrix Factorization for Online Recommendation with Implicit Feedback," Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 549-558, 2016.
30 X. Glorot, A. Bordes, and Y. Bengio, "Deep Sparse Rectifier Neural Networks," Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, pp. 315-323, 2011.
31 J. Ni, J. Li, and J. McAuley, "Justifying Recommendations Using Distantly-Labeled Reviews and Fine-Grained Aspects," Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP ), pp. 188-197, 2019.
32 Y. Zhang, Y. Liu, P. Han, C. Miao, L. Cui, B. Li, and H. Tang, "Learning Personalized Itemset Mapping for Cross-Domain Recommendation," Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJ CAI ), pp. 2561-2567, 2020.
33 P. Li and A. Tuzhilin, "DDTCDR: Deep Dual Transfer Cross Domain Recommendation," Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 331-339, 2020.