STN-CDRS: Sentiment Transfer Network for Cross-Domain Recommendation Systems

Nikita Taneja; Hardeo Kumar Thakur

doi:10.24423/cames.2024.900

Nikita Taneja Computer Science and Technology, Manav Rachna University, Faridabad, India
Hardeo Kumar Thakur School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

DOI: http://dx.doi.org/10.24423/cames.2024.900

Abstract

In enterprise environments, the products may come from a variety of categories or domains. Users may engage with entities in one domain, but not in the others when they are presented with multiple domains. Such users are referred to as “cold-starters” in other domains. The primary difficulty in cross-domain recommendation systems is to efficiently transfer user’s latent information based on their engagements in one domain into the other domains. The advancements in recommendation systems have inspired us to develop review-driven recommendation models that utilize cross-domain knowledge transfer and deep learning models. This work proposes a sentiment transfer network specifically designed for providing recommendation in cross-domain (STN-CDRS). The novelty of the work lies in the user rating enrichment mechanism, which is done by extracting latent information from user review data to fill sparse rating matrix. This enrichment uses previously developed RNN-Core method for efficiently learning user reviews. The reviews provided by the users are used to enrich sparse data across domains. This enrichment allows two things: alleviates the cold start problem and allows more intersecting users across domains to bridge the gap while learning. This work empirically demonstrates its efficiency by iteratively updating over the baseline recommendation models in terms of MAE (mean absolute error), RMSD (root mean squared deviation), precision and recall measures with other state-of-the-art-review-aided cross-domain recommendation systems.

Keywords

cross-domain recommendations, sentiment transfer network, user reviews, deep learning, knowledge transfer,

References

1. S.S. Khanal, P.W.C. Prasad, A. Alsadoon, A. Maag, A systematic review: machine learning based recommendation systems for e-learning, Education and Information Technologies, 25(4): 2635–2664, 2020, doi: 10.1007/s10639-019-10063-9.
2. T. Zang, Y. Zhu, H. Liu, R. Zhang, J. Yu, A survey on cross-domain recommendation: Taxonomies, methods, and future directions, ACM Transactions on Information Systems, 41(2): 1–39, 2022, doi: 10.1145/3548455.
3. G. Guo, H. Qiu, Z. Tan, Y. Liu, J. Ma, X. Wang, Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems, Knowledge-Based Systems, 138: 202–207, 2017, doi: 10.1016/j.knosys.2017.10.005.
4. B. Lika, K. Kolomvatsos, S. Hadjiefthymiades, Facing the cold start problem in recommender systems, Expert Systems with Applications, 41(4, Part 2): 2065–2073, 2014, doi: 10.1016/j.eswa.2013.09.005.
5. I. Cantador, I. Fernández-Tobías, S. Berkovsky, P. Cremonesi, Cross-domain recommender systems, [in:] Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira [Eds.], pp. 919–959, Springer, Boston, MA, 2015, doi: 10.1007/978-1-4899-7637-6_27.
6. F. Zhu, Y. Wang, C. Chen, J. Zhou, L. Li, G. Liu, Cross-domain recommendation: challenges, progress, and prospects, arXiv, 2021, arXiv: 2103.01696.
7. W. Pan, E. Xiang, N. Liu, Q. Yang, Transfer learning in collaborative filtering for sparsity reduction, [in:] Proceedings of the AAAI Conference on Artificial Intelligence, 24(1): 230–235, 2010, doi: 10.1609/aaai.v24i1.7578.
8. X. Fang, Making recommendations using transfer learning, Neural Computing and Applications, 33(15): 9663–9676, 2021, doi: 10.1007/s00521-021-05730-3.
9. X. Qiu, T. Sun, Y. Xu, Y. Shao, N. Dai, X. Huang, Pre-trained models for natural language processing: A survey, Science China Technological Sciences, 63: 1872–1897, 2020, doi: 10.1007/s11431-020-1647-3.
10. T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, J. Dean, Distributed representations of words and phrases and their compositionality, [in:] Proceedings of the NIPS, Red Hook, NY, USA, 5–8 December, pp. 3111–3119, 2013.
11. J. Devlin, M. Chang, K. Lee, K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, [in:] Proceedings of the NAACL, Minneapolis, MN, USA, June 2–7, pp. 4171–4186, 2019.
12. T. Brown et al., Language models are few-shot learners, Advances in Neural Information Processing Systems, 33: 1877–1901, 2020.
13. A. Rietzler, S. Stabinger, P. Opitz, S. Engl, Adapt or get left behind: Domain adaptation through BERT language model finetuning for aspect-target sentiment classification, [in:] Proceedings of the LREC, Marseille, 11–16 May, pp. 4933–4941, 2020.
14. Q. Guo, F. Zhuang, C. Qin, H. Zhu, X. Xie, H. Xiong, Q. He, A survey on knowledge graph-based recommender systems, arXiv, 2020, arXiv: 2003.00911.
15. Y. Li, J.J. Xu, P.P. Zhao, J.H. Fang, W. Chen, L. Zhao, ATLRec: An attentional adversarial transfer learning network for cross-domain recommendation, Journal of Computer Science and Technology, 35(4): 794–808, 2020, doi: 10.1007/s11390-020-0314-8.
16. Z. Huang, H. Wang, E.P. Xing, D. Huang, Self-challenging improves cross-domain generalization, [in:] European Conference on Computer Vision-ECCV 2020, A. Vedaldi, H. Bischof, T. Brox, J.M. Frahm [Eds.], Lecture Notes in Computer Science, Vol. 12347, pp. 124–140, Springer, Cham, 2020, doi: 10.1007/978-3-030-58536-5_8.
17. H. Liu, L. Guo, P. Li, P. Zhao, X. Wu, Collaborative filtering with a deep adversarial and attention network for cross-domain recommendation, Information Sciences, 565: 370–389, 2021.
18. H. Wang, D. Amagata, T. Makeawa, T. Hara, N. Hao, K. Yonekawa, M. Kurokawa, A DNN-based cross-domain recommender system for alleviating cold-start problem in e-commerce, IEEE Open Journal of the Industrial Electronics Society, 1: 194–206, 2020, doi: 10.1109/OJIES.2020.3012627.
19. H.J. Xue, X.Y. Dai, J. Zhang, S. Huang, J. Chen, Deep matrix factorization models for recommender systems, [in:] Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 3203–3209, 2017, doi: 10.24963/ijcai.2017/447.
20. J. Tang, K. Wang, Personalized top-n sequential recommendation via convolutional sequence embedding, [in:] WSDM’18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565–573, 2018, doi: 10.1145/3159652.3159656.
21. S.T. Zhong, L. Huang, C.D. Wang, J.H. Lai, P.S. Yu, An autoencoder framework with attention mechanism for cross-domain recommendation, IEEE Transactions on Cybernetics, 52(6): 5229–5241, 2022, doi: 10.1109/TCYB.2020.3029002.
22. T. Man, H. Shen, X. Jin, X. Cheng, Cross-domain recommendation: An embedding and mapping approach, [in:] Proceeding of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 2464–2470, 2017, doi: 10.24963/ijcai.2017/343.
23. F. Zhu, Y. Wang, C. Chen, G. Liu, M. Orgun, J. Wu, A deep framework for cross-domain and cross-system recommendations, arXiv, 2020, arXiv: 2009.06215.
24. F. Yuan, L. Yao, B. Benatallah, DARec: Deep domain adaptation for cross-domain recommendation via transferring rating pattern, arXiv, 2019, arXiv: 1905.10760.
25. C. Zhao, C. Li, C. Fu, Cross-domain recommendation via preference propagation Graph-Net, [in:] Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2165–2168, 2019, doi: 10.1145/3357384.3358166.
26. C. Zhao, C. Li, R. Xiao, H. Deng, A. Sun, 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, 2020, doi: 10.1145/3397271.3401169.
27. C. Li, C. Quan, L. Peng, Y. Qi, Y. Deng, L. Wu, A capsule network for recommendation and explaining what you like and dislike, [in:] SIGIR’19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 275–284, 2019, doi: 10.1145/3331184.3331216.
28. Y. Xu, Z. Peng, Y. Hu, X. Hong, W. Fu, Cross-domain recommendation for mapping sentiment review pattern, [in:] Knowledge Science, Engineering and Management Springer – KSEM 2018, W. Liu, F. Giunchiglia, B. Yang [Eds.], Lecture Notes in Computer Science, Vol. 11061, pp. 324–336, Springer, Cham, 2018, doi: 10.1007/978-3-319-99365-2_29.
29. Y. Xu, Z. Peng, Y. Hu, X. Hong, SARFM: A sentiment-aware review feature mapping approach for cross-domain recommendation, [in:] Web Information Systems Engineering – WISE 2018, H. Hacid et al. [Eds.], Lecture Notes in Computer Science, Vol. 11234, pp. 3–18, Springer, Cham, 2018, doi: 10.1007/978-3-030-02925-8_1.
30. Y. Wang, H. Yu, G. Wang, Y. Xie, Cross-domain recommendation based on sentiment analysis and latent feature mapping, Entropy, 22(4): 473, 2020, doi: 10.3390/e22040473.
31. W. Hong, N. Zheng, Z. Xiong, Z. Hu, A parallel deep neural network using reviews and item metadata for cross-domain recommendation, IEEE Access, 8: 41774–41783, 2020, doi: 10.1109/ACCESS.2020.2977123.
32. Y. Cai, W. Ke, E. Cui, F. Yu, A deep recommendation model of cross-grained sentiments of user reviews and ratings, Information Processing & Management, 59(2): 102842, 2022, doi: 10.1016/j.ipm.2021.102842.
33. N. Taneja, H.K. Thakur, Evaluating the scalability of matrix factorization and neighborhood-based recommender systems, International Journal of Information Technology and Computer Science (IJITCS), 15(1): 21–29, 2023, doi: 10.5815/ijitcs.2023.01.03.
34. G. Hu, Y. Zhang, Q. Yang, CoNet: Collaborative cross networks for cross-domain recommendation, [in:] CIKM’18: Proceedings of the 27th ACM international Conference on Information and Knowledge Management, pp. 667–676, 2018, doi: 10.1145/3269206.3271684.
35. P. Li, A. Tuzhilin, DDTCDR: Deep dual transfer cross domain recommendation, [in:] Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 331–339, 2020.
36. X. He, L. Liao, H. Zhang, L. Nie, X. Hu, T.S. Chua, Neural collaborative filtering, [in:] WWW’17: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182, 2017, doi: 10.1145/3038912.3052569.
37. N. Taneja, H.K. Thakur, RNNCore: Lexicon aided recurrent neural network for sentiment analysis, International Journal of Computing and Digital System, 12(1): 1561–1568, 2021, doi: 10.12785/ijcds/1201126.
38. M. Song, T. Chambers, Text mining with the Stanford CoreNLP, [in:] Measuring Scholarly Impact, Y. Ding, R. Rousseau, D. Wolfram [Eds.], pp. 215–234, Springer, Cham, 2014, doi: 10.1007/978-3-319-10377-8_10.
39. Z. Liu, L. Zheng, J. Zhang, J. Han, S.Y. Philip, JSCN: Joint spectral convolutional network for cross domain recommendation, [in:] 2019 IEEE International Conference on Big Data (Big Data), pp. 850–859, IEEE, 2019.
40. S. AlZu’bi, A. Alsmadiv, S. AlQatawneh, M. Al-Ayyoub, B. Hawashin, Y. Jararweh, A brief analysis of Amazon online reviews, [in:] Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp. 555–560, 2019, doi: 10.1109/SNAMS.2019.8931816.
41. Y. Liu, Y. Zhou, S.Wen, C. Tang, A strategy on selecting performance metrics for classifier evaluation, International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 6(4): 20–35, 2014, doi: 10.4018/IJMCMC.2014100102.
42. A.K. Sahu, P. Dwivedi, Knowledge transfer by domain-independent user latent factor for cross-domain recommender systems, Future Generation Computer Systems, 108: 320–333, 2020, doi: 10.1016/j.future.2020.02.024.
43. C. Xing, X. Yang, Cross-domain recommendation model based on SVD++ and tag, Computer Engineering, 44(4): 225–230, 2018.
44. C. Li, M. Zhao, H. Zhang, C. Yu, L. Cheng, G. Shu, B. Kong, D. Niu, RecGURU: Adversarial learning of generalized user representations for cross-domain recommendation, [in:] WSDM’22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 571–581, 2022, doi: 10.1145/3488560.3498388.
45. Z. Li, D. Amagata, Y. Zhang, T. Maekawa, T. Hara, K. Yonekawa, M. Kurokawa, HML4Rec: Hierarchical meta-learning for cold-start recommendation in flash sale e-commerce, Knowledge-Based Systems, 255: 109674, 2022, doi: 10.1016/j.knosys.2022.109674.
46. Y. Deng, Recommender systems based on graph embedding techniques: A review, IEEE Access, 10: 51587–51633, 2022, doi: 10.1109/ACCESS.2022.3174197.