Hybrid Recommender System via Personalized Users’ Context

Anthony Nosshi; Aziza Asem; Mohamed Badr Senousy

doi:10.2478/cait-2019-0006

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Hybrid Recommender System via Personalized Users’ Context

Cybernetics and Information Technologies

Volume 19 (2019): Issue 1 (March 2019)

By: Anthony Nosshi, Aziza Asem and Mohamed Badr Senousy

Open Access

|Mar 2019

Abstract

In movie domain, finding the appropriate movie to watch is a challenging task. This paper proposes a recommender system that suggests movies in cinema that fit the user’s available time, location, mood and emotions. Conducted experiments for evaluation showed that the proposed method outperforms the other baselines.

References

1. Champiri, Z. D., S. R. Shahamiri, S. S. B. Salim. A Systematic Review of Scholar Context-Aware Recommender Systems. – Expert Syst. Appl., Vol. 42, 2015, No 3, pp. 1743-1758.10.1016/j.eswa.2014.09.017
Search in Google Scholar Back to article
2. Shu, J., X. Shen, H. Liu, B. Yi, Z. Zhang. A Content-Based Recommendation Algorithm for Learning Resources. – Multimed. Syst., Vol. 24, 2018, No 2, pp. 163-173.10.1007/s00530-017-0539-8
Search in Google Scholar Back to article
3. Fu, M., H. Qu, D. Moges, L. Lu. Attention Based Collaborative Filtering. – Neurocomputing, Vol. 311, 2018, pp. 88-98.10.1016/j.neucom.2018.05.049
Search in Google Scholar Back to article
4. Wang, F., S. Lin, X. Luo, H. Wu, R. Wang, F. Zhou. A Data-Driven Approach for Sketch-Based 3D Shape Retrieval via Similar Drawing-Style Recommendation. – Comput. Graph. Forum, Vol. 36, October 2017, No 7, pp. 157-166.10.1111/cgf.13281
Search in Google Scholar Back to article
5. Chu, P. M., S. J. Lee. A Novel Recommender System for e-Commerce. – In: Proc. of 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI’17), 2017, pp. 1-5.10.1109/CISP-BMEI.2017.8302310
Search in Google Scholar Back to article
6. Zhao, Q., C. Wang, P. Wang, M. Zhou, C. Jiang. A Novel Method on Information Recommendation via Hybrid Similarity. – IEEE Trans. Syst. Man, Cybern. Syst., Vol. 48, 2018, No 3, pp. 448-459.10.1109/TSMC.2016.2633573
Search in Google Scholar Back to article
7. Hariadi, I., D. Nurjanah. Hybrid Attribute and Personality Based Recommender System for Book Recommendation. – In: Proc. of International Conference on Data and Software Engineering (ICoDSE’17), 2017, pp. 1-5.10.1109/ICODSE.2017.8285874
Search in Google Scholar Back to article
8. Yang, S.-B., S.-H. Shin, Y. Joun, C. Koo. Exploring the Comparative Importance of Online Hotel Reviews’ Heuristic Attributes in Review Helpfulness: A Conjoint Analysis Approach. – J. Travel Tour. Mark., Vol. 34, September 2017, No 7, pp. 963-985.10.1080/10548408.2016.1251872
Search in Google Scholar Back to article
9. Wang, H., K. Guo. The Impact of Online Reviews on Exhibitor Behaviour: Evidence from Movie Industry. – Enterp. Inf. Syst., Vol. 11, November 2017, No 10, pp. 1518-1534.10.1080/17517575.2016.1233458
Search in Google Scholar Back to article
10. Clarizia, F., F. Colace, M. Lombardi, F. Pascale. A Context Aware Recommender System for Digital Storytelling. – In: Proc. of IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA’18), 2018, pp. 542-549.10.1109/AINA.2018.00085
Search in Google Scholar Back to article
11. Boffa, S., C. D. Maio, B. Gerla, M. Parente. Context-Aware Advertisment Recommendation on Twitter through Rough Sets. – In: Proc. of IEEE International Conference on Fuzzy Systems (FUZZ-IEEE’18), 2018, pp. 1-8.10.1109/FUZZ-IEEE.2018.8491510
Search in Google Scholar Back to article
12. Yang, Q. A Novel Recommendation System Based on Semantics and Context Awareness. – Computing, Vol. 100, 2018, No 8, pp. 809-823.10.1007/s00607-018-0627-4
Search in Google Scholar Back to article
13. Dixit, V. S., P. Jain. A Proposed Framework for Recommendations Aggregation in Context Aware Recommender Systems. – In: Proc. of 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2018, pp. 209-214.10.1109/CONFLUENCE.2018.8442603
Search in Google Scholar Back to article
14. Colombo-Mendoza, L. O., R. Valencia-García, A. Rodríguez-González, G. Alor-Hernández, J. J. Samper-Zapater. RecomMetz: A Context-Aware Knowledge-Based Mobile Recommender System for Movie Showtimes. – Expert Syst. Appl., Vol. 42, 2015, No 3, pp. 1202-1222.10.1016/j.eswa.2014.09.016
Search in Google Scholar Back to article
15. Abbas, M., M. U. Riaz, A. Rauf, M. T. Khan, S. Khalid. Context-Aware Youtube Recommender System. – In: Proc. of International Conference on Information and Communication Technologies (ICICT’17), 2017, pp. 161-164.10.1109/ICICT.2017.8320183
Search in Google Scholar Back to article
16. Cai, G., W. Gu. Heterogeneous Context-Aware Recommendation Algorithm with Semi-Supervised Tensor Factorization BT. – Intelligent Data Engineering and Automated Learning – IDEAL’17, 2017, pp. 232-241.10.1007/978-3-319-68935-7_26
Search in Google Scholar Back to article
17. Kim, D., C. Park, J. Oh, S. Lee, H. Yu. Convolutional Matrix Factorization for Document Context-Aware Recommendation. – In: Proc. of 10th ACM Conf. Recomm. Syst. (RecSys’16), 2016, pp. 233-240.10.1145/2959100.2959165
Search in Google Scholar Back to article
18. Yu, P., L. Lin, J. Wang. A Novel Framework to Alleviate the Sparsity Problem in Context-Aware Recommender Systems. – New Rev. Hypermedia Multimed., Vol. 23, April 2017, No 2, pp. 141-158.10.1080/13614568.2016.1152319
Search in Google Scholar Back to article
19. Deng, S., D. Wang, X. Li, G. Xu. Exploring User Emotion in Microblogs for Music Recommendation. – Expert Syst. Appl., Vol. 42, 2015, No 23, pp. 9284-9293.10.1016/j.eswa.2015.08.029
Search in Google Scholar Back to article
20. Zhao, Z., et al. Social-Aware Movie Recommendation via Multimodal Network Learning. – IEEE Trans. Multimed., Vol. 20, 2018, No 2, pp. 430-440.10.1109/TMM.2017.2740022
Search in Google Scholar Back to article
21. Zhang, Y., R. Jin, Z.-H. Zhou. Understanding Bag-of-Words Model: A Statistical Framework. – Int. J. Mach. Learn. Cybern., Vol. 1, 2010, No 1, pp. 43-52.10.1007/s13042-010-0001-0
Search in Google Scholar Back to article
22. P. Ekman, R. J. Davidson, Eds. The Nature of Emotion: Fundamental Questions. New York, NY, US, Oxford University Press, 1994.
Search in Google Scholar Back to article
23. Nosshi, A., A. Asem, M. B. Senousy. Hybrid Recommender System Using Emotional Fingerprints Model. – Int. J. Inf. Retr. Res., Vol. 9, 2019, No 3, p. Article 4 (in Press).10.4018/IJIRR.2019070104
Search in Google Scholar Back to article
24. Kohavi, R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. – In: Proc. of 14th International Joint Conference on Artificial Intelligence, Vol. 2, 1995, pp. 1137-1143.
Search in Google Scholar Back to article
25. Yuan, T., H. Wu, J. Zhu, L. Shen, G. Qian. MS-UCF: A Reliable Recommendation Method Based on Mood-Sensitivity Identification and User Credit. – In: Proc. of International Conference on Information Management and Processing (ICIMP’18), 2018, pp. 16-20.10.1109/ICIMP1.2018.8325834
Search in Google Scholar Back to article
26. Wang, H.-C., H.-T. Jhou, Y.-S. Tsai. Adapting Topic Map and Social Influence to the Personalized Hybrid Recommender System. – Inf. Sci., NY, 2018.
Search in Google Scholar Back to article
27. Lim, H., H.-J. Kim. Item Recommendation Using Tag Emotion in Social Cataloging Services. – Expert Syst. Appl., Vol. 89, 2017, pp. 179-187.10.1016/j.eswa.2017.07.046
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.2478/cait-2019-0006 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702

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Language: English

Page range: 101 - 115

Submitted on: Jan 29, 2019

Accepted on: Feb 14, 2019

Published on: Mar 29, 2019

Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Movie recommender,

Emotion recommendation,

Hybrid recommender system,

sentiment analysis,

spatio-temporal recommendation

Related subjects:

Computer sciences,

Information technology

© 2019 Anthony Nosshi, Aziza Asem, Mohamed Badr Senousy, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 19 (2019): Issue 1 (March 2019)