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An Ontology-Based Hybrid Recommender System for Internet Protocol Television

  • Mohammad Wahiduzzaman Khan
  • Gaik-Yee ChanEmail author
  • Fang-Fang Chua
  • Su-Cheng Haw
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10645)

Abstract

Internet Protocol Television (IPTV) has gained popularity in providing TV channels and program choices to broad range of user. The service providers are attempting ways to attract more users’ subscription and as from user point of view, they would like to have channel or program recommendations based on their preferences as well as public suggestions. This motivates us to propose an ontology-based hybrid recommender system. This system applies content-based and collaborative filtering in IPTV domain to increase users’ satisfaction. The preliminary experimental results show that our proposed system works more effectively by eliminating the cold-start problem, over specialization, data sparsity and new item problems and efficiently by using the ontological user profile for computation of recommendations.

Keywords

Collaborative filtering Content-based Hybrid recommender system Internet protocol television Ontology 

Notes

Acknowledgement

This work is supported by the funding of TM R&D from the Telekom Malaysia, Malaysia.

References

  1. 1.
    Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth”. In: Human Factors in Computing Systems Conference, pp. 210–217 (1995)Google Scholar
  2. 2.
    Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: 5th International Conference on Machine Learning, pp. 46–54 (1998)Google Scholar
  3. 3.
    Pazzani, M.J.: A framework for collaborative, content-based, and demographic filtering. Artif. Intell. Rev. 13, 393–408 (1999)CrossRefGoogle Scholar
  4. 4.
    Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72079-9_10 CrossRefGoogle Scholar
  6. 6.
    Cantador, I., Bellogín, A., Castells, P.: A multilayer ontology-based hybrid recommendation model. AI Commun. 21(2–3), 203–210 (2008)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Middleton, S.E., Roure, D.D., Shadbolt, N.R.: Ontology-based recommender systems. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 779–796. Springer, Heidelberg (2009). doi: 10.1007/978-3-540-92673-3_35 CrossRefGoogle Scholar
  8. 8.
    Bambini, R., Cremonesi, P., Turrin, R.: A recommender system for an IPTV service provider: a real large-scale production environment. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 299–300. Springer, Boston (2011). doi: 10.1007/978-0-387-85820-3_9 CrossRefGoogle Scholar
  9. 9.
    Park, D.H., Kim, H.K., Choi, I.Y., Kim, J.K.: A literature review and classification of recommender systems research. Expert Syst. Appl. 39, 10059–100072 (2012)CrossRefGoogle Scholar
  10. 10.
    Carrer-Neto, W., Hernandez-Alcaraz, M.L., Valencia-Garcia, R., Garcia-Sanchez, F.: Social knowledge-based recommender system. Application to the movies domain. Expert Syst. Appl. 39, 10990–11000 (2012)CrossRefGoogle Scholar
  11. 11.
    Pripuzic, K., Zarko, I.P., Podobnik, V. Lovrek, I.,Cavka, M., Petkovic, I., Stulic, P., Gojceta, M.: Building an IPTV VoD recommender system: an experience report. In: 12th International Conference on Telecommunications, pp. 155–162. IEEE (2013)Google Scholar
  12. 12.
    Zhang, Z., Gong, L., Xie, J.: Ontology-based collaborative filtering recommendation algorithm. In: Liu, D., Alippi, C., Zhao, D., Hussain, A. (eds.) BICS 2013. LNCS, vol. 7888, pp. 172–181. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38786-9_20 CrossRefGoogle Scholar
  13. 13.
    Bahramian, Z., Abbaspour, R.A.: An Ontology-based tourism recommender system based on spreading activation model. In: International Conference on Sensors and Models in Remote Sensing and Photogrammetry, pp. 83–90 (2015)Google Scholar
  14. 14.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  15. 15.
    Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: learning new user preferences in recommender systems. In: International Conference of Intelligent User Interfaces, pp. 127–134 (2002)Google Scholar
  16. 16.
    Grouplens, Movielens 100K Dataset. http://grouplens.org/datasets/movielens/100k/

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohammad Wahiduzzaman Khan
    • 1
  • Gaik-Yee Chan
    • 1
    Email author
  • Fang-Fang Chua
    • 1
  • Su-Cheng Haw
    • 1
  1. 1.Faculty of Computing and InformaticsMultimedia UniversityCyberjayaMalaysia

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