Trust-based collaborative filtering for Cross-Domain Recommendations using ANFIS and FAFCM Algorithm

G, Spoorthy; S G, Sanjeevi

doi:10.22111/ijfs.2025.49547.8745

Document Type : Research Paper

Authors

Spoorthy G ¹
Sanjeevi S G ²

¹ Research Scholar, NITW

² Department of CSE, NITW

https://doi.org/10.22111/ijfs.2025.49547.8745

Abstract

Recommendation systems are very useful in domains such as e-commerce, news portals and software requirement analysis. Collaborative filtering models have been used widely, these models often suffer from data-sparsity, interpretability and cold-start problems. To solve these issues, various machine-learning, deep-learning and kernel based models have been employed. Among these, trust based collaborative filtering and cross-domain recommendations have successfully solved the issues to some extent. However, in the recent literature, cross-domain recommendations (CDRs) are made by taking common user ratings. In our paper, we introduce a novel approach that combines CDRs with trust-aware collaborative filtering which employs ‘a partial item overlap’ scenario. Our model operates in two phases: an offline phase calculates trust between source and target users, coarse rating prediction using Adaptive Neuro-Fuzzy Inference System (ANFIS), and clustering via Firefly Fuzzy C-Means (FAFCM); and an online phase, where cluster information and item similarities are used to generate personalized recommendations. Evaluated on the Douban and Movielens datasets using MAE and RMSE metrics, our approach demonstrates improved performance compared to existing methods, effectively mitigating common limitations in recommendation systems.

Keywords

Main Subjects

Fuzzy modelling

References

[1] A. Ali, M. Vadivel, Efficient energy congestion control scheme for wireless sensor networks using adaptive neuro
fuzzy inference system with black widow optimization, Iranian Journal of Fuzzy Systems, 20(6) (2023), 187-202.
https://doi.org/10.22111/ijfs.2023.43990.7748

[2] V. Behbood, J. Lu, G. Zhang, Fuzzy refinement domain adaptation for long term prediction in banking ecosystem,
IEEE Transactions on Industrial Informatics, 10(2) (2014), 1637-1646. https://doi.org/10.1109/TII.2013.
2291942

[3] J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York, (1981), 95-107.
https://doi.org/10.1007/978-1-4757-0450-1

[4] I. Cantador, I. Fern´andez-Tob´ıas, S. Berkovsky, P. Cremonesi, Crossdomain recommender systems, in Recommender
Systems Handbook. Boston, MA, USA: Springer, (2015), 919-959. https://doi.org/10.1007/
978-1-4939-1614-2_29

[5] R. Chen, Q. Hua, Y. S. Chang, B. Wang, L. Zhang, X. Kong, A survey of collaborative filtering-based recommender
systems: From traditional methods to hybrid methods based on social networks, IEEE Access, 6 (2018), 64301-64320.
https://doi.org/10.1109/ACCESS.2018.2877543

[6] L. Chen, Y. Zhang, W. Li, Large language models for domain-agnostic recommendation systems, Proceedings of the
2023 IEEE International Conference on Data Mining (ICDM), (2023), 345-355. https://doi.org/10.1145/3705727

[7] L. Chen, J. Zheng, M. Gao, A. Zhou, W. Zeng, H. Chen, TLRec: Transfer learning for cross-domain recommendation,
Proceedings - 2017 IEEE International Conference on Big Knowledge (ICBK), (2017), 167-172. https://doi.org/
10.1109/ICBK.2017.30

[8] Y. Choi, Review-based cross-domain recommendation via hyperbolic embedding and hierarchy aware domain disentanglement, Arxiv, (2024). https://doi.org/10.1145/3618349.3636594

[9] L. S. Dandugala, K. S. Vani, Advancing big data clustering with fuzzy logic-based IMV-FCA and ensemble approach,
Iranian Journal of Fuzzy Systems, 21(2) (2024), 141-160. https://doi.org/10.22111/ijfs.2024.44621.7855

[10] X. Deng, F. Huangfu, Collaborative variational deep learning for healthcare recommendation, IEEE Access, 7
(2019), 55679-55688. https://doi.org/10.1109/ACCESS.2019.2913468

[11] A. M. Elkahky, Y. Song, X. He, A multi-view deep learning approach for cross domain user modeling in
recommendation systems, in Proc. 24th International Conference World Wide Web (WWW), (2015), 278-288.
https://doi.org/10.1145/2736277.2741667

[12] I. Fern´andez-Tob´ıas, I. Cantador, Exploiting social tags in matrix factorization models for cross-domain collaborative
filtering, In CBRecSys RecSys, (2014), 34-41. https://doi.org/10.1145/2645710.2645719

[13] S. Gao, H. Luo, D. Chen, S. Li, P. Gallinari, J. Guo. Cross-domain recommendation via cluster-level latent factor
model, In ECML and PKDD, Springer, (2013), 161-176. https://doi.org/10.1007/978-3-642-40994-3_11

[14] F. M. Harper, J. A. Konstan, The movieLens datasets: History and context, ACM Transactions on Interactive
Intelligent Systems (TiiS), 5(4) (2015), 1-9. https://doi.org/10.1145/2827872

[15] J. He, R. Liu, F. Zhuang, F. Lin, C. Niu, Q. He, A general cross-domain recommendation framework via Bayesian
neural network, Proceedings - IEEE International Conference on Data Mining (ICDM), (2018), 1001-1006. https:
//doi.org/10.1109/ICDM.2018.00130

[16] M. He, J. Zhang, J. Zhang, MINDTL: Multiple incomplete domains transfer learning for information recommendation,
China Communications, 14(11) (2017), 218-236. https://doi.org/10.1109/CC.2017.8233662

[17] H. Izakian, A. Abraham, Fuzzy c-means and fuzzy swarm for fuzzy clustering problem, Expert Systems with
Applications, 38 (2011), 1835-1838. https://doi.org/10.1016/j.eswa.2010.07.104

[18] H. Li, J. Cao, Y. Gao, Y. Liu, S. Pang, Cross-domain sequential recommendation via neural process, Arxiv, (2024).
https://doi.org/10.48550/arXiv.2410.13588

[19] D. Liang, R. G. Krishnan, M. D. Hoffman, T. Jebara, Variational autoencoders for collaborative filtering, WWW
’18: Proceedings of the 2018 World Wide Web Conference, (2018), 689-698. https://doi.org/10.1145/3178876.
3186150

[20] T. Man, H. Shen, X. Jin, X. Cheng, Cross-domain recommendation: An embedding and mapping approach, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Main track, (2017), 2464-2470.
https://doi.org/10.24963/ijcai.2017/343

[21] J. Nayak, M. Nanda, K. Nayak, B. Naik, H. S. Behera, An improved firefly fuzzy c-means (FAFCM) algorithm
for clustering real world data sets, Advanced Computing, Networking and Informatics, 1 (2014), 339-348. https:
//doi.org/10.1007/978-3-319-07353-8_35

[22] W. Pan, Z. Ming, Interaction-rich transfer learning for collaborative filtering with heterogeneous user feedback,
IEEE Intelligent Systems, 29(6) (2014), 48-54. https://doi.org/10.1109/MIS.2014.59

[23] S. Tan, J. Bu, X. Qin, C. Chen, D. Cai. Cross domain recommendation based on multi-type media fusion, Neurocomputing, 127 (2014), 124-134. https://doi.org/10.1016/j.neucom.2013.06.035

[24] Y. T. Tsai, C. S. Wuy, H. L. Hsuy, T. Liuy, P. L. Cheny, W. H. Chen, K. T. Liao, A cross-domain recommender
system based on common-sense knowledge bases, IEEE Conference on Technologies and Applications of Artificial
Intelligence (TAAI), (2017), 80-83. https://doi.org/10.1109/TAAI.2017.8277258

[25] M. Vijaikumar, et al., Neural cross-domain collaborative filtering with shared entities, Arxiv, (2020). https://doi.
org/10.1007/978-3-030-46130-9_17

[26] D. Wang, Y. Liang, D. Xu, X. Feng, R. Guan, A content based recommender system for computer science publications,
Knowledge-Based Systems, 157 (2018), 1-9. https://doi.org/10.1016/j.knosys.2018.05.001

[27] H. Wang, X. Liu, T. Zhang, Review-based cross domain recommendation via hyperbolic embedding, Proceedings of
the 2024 IEEE Conference on Artificial Intelligence (AI), (2024), 2345-2356. https://doi.org/10.1145/3701551.
3703486

[28] Z. Xu, F. Zhang, W. Wang, H. Liu, X. Kong, Exploiting trust and usage context for cross-domain recommendation,
IEEE Access, 4 (2016), 2398-2407. https://doi.org/10.1109/ACCESS.2016.2559530

[29] X. S. Yang, Firefly algorithms for multimodal optimization, In: Watanabe O., Zeugmann T., (eds.) Stochastic
algorithms: Foundations and applications, SAGA 2009, LNCS, 5792 (2009), 169-78. https://doi.org/10.1007/
978-3-642-04944-6_16

[30] L. Zhao, S. J. Pan, Q. Yang, A unifified framework of active transfer learning for cross-system recommendation,
Artificial Intelligence, 245 (2017), 38-55. https://doi.org/10.1016/j.artint.2016.12.001

[31] T. Zhou, Y. Wang, X. Chen, Cross-domain sequential recommendation, Proceedings of the 2023 ACM Conference
on Recommender Systems (RecSys), (2023), 123-130. https://doi.org/10.1145/3604915.3608752

[32] F. Zhu, C. Chen, Y. Wang, G. Liu, X. Zheng, DTCDR: A framework for dual-target cross-domain recommendation,
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, (2019), 1533-
1542. https://doi.org/10.1145/3357384.3357904

Trust-based collaborative filtering for Cross-Domain Recommendations using ANFIS and FAFCM Algorithm

References

References

Volume 23, Issue 1January and February 2026Pages 23-36

Volume 23, Issue 1
January and February 2026
Pages 23-36