Adaptive User Interface Design: A Case Study of Web Recommendation System

Authors

  • Wan Nur Liyana Wan Husain Human Computer Interaction (HCI) Research Lab, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
  • Azrul Hazri Jantan Human Computer Interaction (HCI) Research Lab, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.

Keywords:

Adaptive User Interface, Recommendation System, Personalization,

Abstract

Personalization is another special form of adaptation where it widely implements in the development of recommendation system that gives adaptive web environment for users. The main concept of recommendation system is to obtain user behavior factor from user interest or search history and predict the next request as they visit web pages. There are some issues arise in recommender systems which they need a lot of data to efficiently build recommendations. Large amount of items and user data are best for getting good recommendations. In practice, however many challenges exist and evaluation results of recommendation systems has been mixed. This paper presents the case study of Netflix and other case studies in the development of recommendation system and analyzes some of the problems and challenges in implementing recommender systems.

References

H. Nakamura, Y. Gao, H. Gao, H. Zhang, A. Kiyohiro, and T. Mine, “Adaptive user interface agent for personalized public transportation recommendation system: PATRASH,” in Proceedings of International Conference on Principles and Practice of Multi-Agent Systems, 2014, pp. 238–245.

H. Soh, S. Sanner, M. White and G. Jamieson, “Deep sequential recommendation for personalized adaptive user interfaces,” in Proceedings of the 22nd International Conference on Intelligent User Interfaces, 2017, pp. 589-593.

A. Felfernig, R. Burke, and P. Pu, “Preface to the special issue on user interfaces for recommender systems,” User Modeling and UserAdapted Interaction, vol. 22, no. 4-5, pp. 313–316, 2012.

L. Mengjuan, W. Wei, Z. Fan, X. Hao, Q. Zhiguang, and L. Xucheng, “ActiveRec: A novel context-sensitive ranking method for active movie recommendation,” in Proceedings of International Conference on Advanced Cloud and Big Data, 2016, pp. 92-97.

A. R. Krishnan and R. Remya, “A case study on various recommendation systems,” International Journal of Computer Applications, vol. 133, no. 15, pp. 5-8, 2016.

C. Lynch, “Personalization and recommender systems in the larger context: new directions and research questions,” in Proceedings of the Second DELOS Network of Excellence Workshop on Personalization and Recommender Systems in Digital Libraries, 2001, pp. 1-5.

M. Jafari, F. S. Sabzchi, and A. J. Irani, “Applying web usage mining techniques to design effective web recommendation systems: A case study,” in Advances in Computer Science: an International Journal, vol. 3, no. 2, pp. 78-90, 2014.

B. Pradel, S. Sean, J. Delporte, S. Guérif, C. Rouveirol, N. Usunier, and F. Dufau-Joel, “A case study in a recommender system based on purchase data,” in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 377- 385.

B.-H. Huang, and B.-R. Dai, “A weighted distance similarity model to improve the accuracy of collaborative recommender system,” in Proceedings of 16th IEEE International Conference on Mobile Data Management, 2015, pp. 104-109.

S. Saravanan, “Design of large-scale content-based recommender system using hadoop mapreduce framework,” in Proceedings of 8th International Conference on Contemporary Computing (IC3), 2015, pp. 302-307.

V. A. Rohani, Z. M. Kasirun, and K. Ratnavelu, “An enhanced contentbased recommender system for academic social networks,” in Proceedings of IEEE 4th International Conference on Big Data and Cloud Computing, 2014, pp. 424-431.

A. L. Garrido, M. G. Buey, S. Ilarri, I. Fũrstner, and L. Szedmina, “KGNR: A knowledge-based geographical news recommender,” in Proceedings of IEEE 13th International Symposium on Intelligent Systems and Informatics (SISY), 2015, pp. 195-198.

J. Xu, Y. Yao, H. Tong, X. Tao, and J. Lu, “RaPare: A generic strategy for cold-start rating prediction problem,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 6, pp. 1296-1309, 2017.

L. Safoury, and A. Salah, “Exploiting user demographic attributes for solving cold-start problem in recommender system,” in Lecture Notes on Software Engineering, 2013, pp. 303-307.

X. Amatriain, and J. Basilico, “Recommender systems in industry: A Netflix case study,” in Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, Eds. Springer, 2015, pp. 385-419.

E. Colson, “Using human and machine processing in recommendation systems,” in Human Computation and Crowdsourcing: Works in Progress and Demonstration Abstracts AAAI Technical Report, 2013.

G. Linden, B. Smith, and J. York, “Amazon.com recommendations: item-to-item collaborative filtering,” in IEEE Internet Computing, vol. 7, no. 1, pp. 76-80, 2003.

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Published

2017-10-20

How to Cite

Wan Husain, W. N. L., & Jantan, A. H. (2017). Adaptive User Interface Design: A Case Study of Web Recommendation System. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-3), 137–140. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2890

Issue

Vol. 9 No. 3-3: Special Issue on Software Engineering I

Section

Articles

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