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Vol3 Issue2 _5

posted Mar 24, 2019, 2:15 AM by Yaseen Raouf Mohammed   [ updated Apr 7, 2019, 5:51 AM ]
 GENERATING A USER BEHAVIOR-BASED SYNTHETIC DATASET FOR IPTV SERVICES WITH APPLICATION TO RECOMMENDER SYSTEMS

 Alireza Abdollahpouri

 Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran

 Shahnaz Mohammadi Majd

 Department of Mathematics, Islamic Azad University of Sanandaj


 Abstract.
In IPTV systems, thousands of live TV channels and video contents are available to subscribers. To benefit from the rich set of contents, users need to be able to rapidly and easily find
what they are actually interested in. The integration of a recommender system into the IPTV infrastructure improves the user experience by providing an effective way of browsing for interesting
programs and movies. But to improve the quality of recommendation, a clear understanding of access pattern to the items is necessary. However, for security reasons, in IPTV systems this kind of information is not publicly available. In this paper, we try to generate a so-called artificial dataset based on a model which mimics the behavior of a typical IPTV user, and with the aid of MovieLens dataset. We then show a typical application of the dataset in recommender systems.


 Keywords:
IPTV; user behavior; synthetic dataset; recommender systems


 References
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[3] H.J. Kwon, K.S. Hong, “Personalized electronic program guide for IPTV based on collaborative filtering with novel similarity method,” In Consumer Electronics (ICCE), IEEE International Conference on: IEEE. pp 467-468, 2011.
[4] M. Krstic, M. Bjelica, “Context-aware personalized program guide based on neural network,” IEEE Transactions on Consumer Electronics. 58(4): pp 1301-1306, 2012.
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[8] MovieLens dataset. http://www.grouplens.org/data.
[9] A. Abdollahpouri, “QoS-Aware Live IPTV Streaming Over Wireless Multi-hop Networks,” Shaker- Verlag, Aachen, 2012.
[10] J. Cong, B.E. Wolfinger, “A unified load generator based on formal load specification and load transformation,” In Proceedings of the 1st international conference on Performance evaluation
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[11] A. Abdollahpouri, B.E. Wolfinger, J. Lai, and of C. Vinti, “Elaboration and formal description of 43 IPTV user models and their application to IPTV system analysis,” MMBnet, 2011.
[12] Y.J. Park, A. Tuzhilin, “The long tail of recommender systems and how to leverage it,” In Proceedings of the ACM conference on Recommender systems. pp 11-18, 2008

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