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Research Article

Contextual Bandit Approach-based Recommendation System for Personalized Web-based Services

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References

  • Abdi, H., and L. J. Williams. 2010. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2 (4):433–59. doi:10.1002/wics.101.
  • Ben Schafer, J., D. Frankowski, J. Herlocker, and S. Sen. 2007. Collaborative filtering recommender systems. In The adaptive web, 291–324. Springer.
  • Binucci, C., F. De Luca, E. D. Giacomo, G. Liotta, and F. Montecchiani. 2017. Designing the content analyzer of a travel recommender system. Expert Systems with Applications 87:199–208. doi:10.1016/j.eswa.2017.06.028.
  • Burke, R. 2002. Hybrid recommender systems: Survey and experiments. User Modeling and User-adapted Interaction 12 (4):331–70. doi:10.1023/A:1021240730564.
  • Cantador, I., P. Brusilovsky, and T. Kuflik. Movielens dataset, 2011.
  • Chu, W., L. Lihong, L. Reyzin, and R. Schapire. Contextual bandits with linear payoff functions. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 208–214, JMLR Workshop and Conference Proceedings, 2011
  • Errami, M., J. D. Wren, J. M. Hicks, and H. R. Garner. 2007. etblast: A web server to identify expert reviewers, appropriate journals and similar publications. Nucleic Acids Research 35 (suppl_2):W12–W15. doi:10.1093/nar/gkm221.
  • Guo, G., J. Zhang, and N. Yorke-Smith. 2015. Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowledge-Based Systems 74:14–27. doi:10.1016/j.knosys.2014.10.016.
  • Inan, E., F. Tekbacak, and C. Ozturk. 2018. Moreopt: A goal programming based movie recommender system. Journal of Computational Science 28:43–50. doi:10.1016/j.jocs.2018.08.004.
  • Kiran, R., P. Kumar, and B. Bhasker. 2020. Dnnrec: A novel deep learning based hybrid recommender system. Expert Systems with Applications 144:113054.
  • Lihong, L., W. Chu, J. Langford, and R. E. Schapire. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web, 661–70, 2010.
  • Lops, P., M. De Gemmis, and G. Semeraro. 2011. Content-based recommender systems: State of the art and trends. In Recommender systems handbook, 73–105. Springer.
  • Meng, S., Q. Lianyong, L. Qianmu, W. Lin, X. Xiaolong, and S. Wan. 2019. Privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems. Future Generation Computer Systems 96:324–35. doi:10.1016/j.future.2019.02.016.
  • Mustaqeem, A., S. M. Anwar, and M. Majid. 2020. A modular cluster based collaborative recommender system for cardiac patients. Artificial Intelligence in Medicine 102:101761.
  • Pradhan, T., and S. Pal. 2019. A hybrid personalized scholarly venue recommender system integrating social network analysis and contextual similarity. In Future Generation Computer Systems 110 (2020): 1139–1166
  • Qaiser, S., and R. Ali. 2018. Text mining: Use of tf-idf to examine the relevance of words to documents. International Journal of Computer Applications 181 (1):07. doi:10.5120/ijca2018917395.
  • Qian, X., H. Feng, G. Zhao, and T. Mei. 2013. Personalized recommendation combining user interest and social circle. IEEE Transactions on Knowledge and Data Engineering 26 (7):1763–77. doi:10.1109/TKDE.2013.168.
  • Qian, Y., Y. Zhang, M. Xiao, Y. Han, and L. Peng. 2019. Ears: Emotion-aware recommender system based on hybrid information fusion. Information Fusion 46:141–46. doi:10.1016/j.inffus.2018.06.004.
  • Qingyun, W., H. Wang, G. Quanquan, and H. Wang. Contextual bandits in a collaborative environment. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 529–38, 2016.
  • Shun, L., F. Luo, J. Yang, G. Ranzi, and J. Wen. 2019. A personalized electricity tariff recommender system based on advanced metering infrastructure and collaborative filtering. International Journal of Electrical Power & Energy Systems 113:403–10. doi:10.1016/j.ijepes.2019.05.042.
  • Turrin, R., M. Quadrana, A. Condorelli, R. Pagano, and P. Cremonesi. 2015. 30music listening and playlists dataset. In 9th ACM Conference on Recommender Systems, RecSys 2015. Vol. 1441. CEUR-WS, 2015
  • Valcarce, D., A. Landin, J. Parapar, and Á. Barreiro. 2019. Collaborative filtering embeddings for memory-based recommender systems. Engineering Applications of Artificial Intelligence 85:347–56. doi:10.1016/j.engappai.2019.06.020.
  • Wang, D., Y. Liang, X. Dong, X. Feng, and R. Guan. 2018. A content-based recommender system for computer science publications. Knowledge-Based Systems 157:1–9. doi:10.1016/j.knosys.2018.05.001.
  • Wang, H.-C., H.-T. Jhou, and Y.-S. Tsai. 2018. Adapting topic map and social influence to the personalized hybrid recommender system. In Information Sciences.

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