Abstract
Recommender system aims to solve the information overload problem by recommending a set of items that are suitable for users. Recently, the incorporation of multiple criteria into traditional single-criterion recommender system has increased the interest. In this paper, we propose a novel trust-enhanced multi-criteria recommender system using fuzzy rating in collaborative filtering framework. We have also designed a hybrid approach of traditional multi-criteria recommender system and trust-enhanced multi-criteria recommender system to reduce data sparsity problem. The empirical results show that our proposed approach demonstrates efficient recommendation as compared to traditional approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
IJntema, W., Goossen, F., Frasincar, F., Hogenboom, F.: Ontology-based news recommendation. In: Proceedings of the 2010 EDBT/ICDT Workshops, EDBT’10, pp. 16:1–16:6, New York, USA (2010)
Kant, V., Bharadwaj, K.K.: Integrating collaborative and reclusive methods for effective recommendations: a fuzzy bayesian approach. Int. J. Intell. Syst. 28(11), 1099–1123 (2013)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries, DL ’00, pp. 195–204, New York, USA (2000)
Dwivedi, P., Bharadwaj, K.K.: Effective trust-aware e-learning recommender system based on learning styles and knowledge levels. Edu. Tech. Soc. 16(4), 201–216 (2013)
Dwivedi, P.: Bharadwaj, K.K.: e-learning recommender system for a group of learners based on the unified learner profile approach. Expert Syst. 32(2), 264–276 (2015)
Adomavicius, G., Manouselis, N., Kwon, Y.: Multi-criteria recommender systems. In: Recommender Systems Handbook, pp. 769–803. Springer US, Boston, MA (2011)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender sys- tems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Sinha, R.R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: Proceedings of DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries (2001)
O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, IUI ’05, pp. 167–174, New York, USA (2005)
Candillier, L., Meyer, F., Boullé, M.: Machine learning and data mining in pattern recognition. In: Proceedings of 5th International Conference, MLDM 2007, Leipzig, Germany. Comparing State-of-the-Art Collaborative Filtering Systems, pp. 548–562. Springer, Berlin, 18–20 July 2007
Kant, V., Bharadwaj, K.K.: Fuzzy computational models of trust and distrust for enhanced recommendations. Int. J. Intell. Syst. 28(4), 332–365 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Goswami, A., Dwivedi, P., Kant, V. (2018). Trust-Enhanced Multi-criteria Recommender System. In: Pant, M., Ray, K., Sharma, T., Rawat, S., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 583. Springer, Singapore. https://doi.org/10.1007/978-981-10-5687-1_39
Download citation
DOI: https://doi.org/10.1007/978-981-10-5687-1_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5686-4
Online ISBN: 978-981-10-5687-1
eBook Packages: EngineeringEngineering (R0)