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Information Systems Frontiers

, Volume 20, Issue 6, pp 1157–1171 | Cite as

TV-Program Retrieval and Classification: A Comparison of Approaches based on Machine Learning

  • Fedelucio Narducci
  • Cataldo Musto
  • Marco de Gemmis
  • Pasquale Lops
  • Giovanni Semeraro
Article

Abstract

Electronic Program Guides (EPGs) are systems that allow users of media applications, such as web TVs, to navigate scheduling information about current and upcoming programming. Personalized EPGs help users to overcome information overload in this domain, by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. In this paper we introduce the concept of personal channel, on which Personalized EPGs are grounded, that provides users with potentially interesting programs and videos, by exploiting program genres (documentary, sports, …) and short textual descriptions of programs to find and categorize them. We investigate the problem of adopting appropriate algorithms for TV-program classification and retrieval, in the context of building personal channels, which is harder than a classical retrieval or classification task because of the short text available. The approach proposed to overcome this problem is the adoption of a new feature generation technique that enriches the textual program descriptions with additional features extracted from Wikipedia. Results of the experiments show that our approach actually improves the retrieval performance, while a limited positive effect is observed on classification accuracy.

Keywords

Recommender systems Electronic program guides Content-based filtering 

Notes

Acknowledgments

This work fulfils the research objectives of the PAC02L1_00061 project MAIVISTO “Massive Adaptive Internet VIdeo STreaming using the clOud” funded by the Italian Ministry of University and Research (MIUR). The authors are grateful to Mauro Barbieri, Jan H. M. Korst, Verus Pronk, Ramon Clout from Philips Research Eindhoven, who provided expertise that greatly supported our research. The authors wish to thank Philips Research Eindhoven and Axel Springer for providing access to the dataset used in the experiments.

References

  1. Amatriain, X., & Basilico, J. (2015). Recommender systems in industry: A netflix case study. In Ricci, F., Rokach, L., Shapira, B., Kantor, P. B. (Ed.), Recommender systems handbook (pp. 385–419). Springer.Google Scholar
  2. Artikis, A., Sergot, M., & Paliouras, G. (2010). A logic programming approach to activity recognition. In Proceedings of the 2nd ACM international workshop on events in multimedia, EiMM ’10 (pp. 3–8). New York: ACM.  https://doi.org/10.1145/1877937.1877941.
  3. Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval. Boston: Addison-Wesley Longman Publishing Co., Inc.Google Scholar
  4. Ballan, L., Bertini, M., Del Bimbo, A., Seidenari, L., & Serra, G. (2011). Event detection and recognition for semantic annotation of video. Multimedia Tools and Applications, 51(1), 279–302.  https://doi.org/10.1007/s11042-010-0643-7.CrossRefGoogle Scholar
  5. Cremonesi, P., Turrin, R., & Airoldi, F. (2011). Hybrid algorithms for recommending new items. In Proceedings of the 2nd international workshop on information heterogeneity and fusion in recommender systems, Hetrec ’11 (pp. 33–40). New York: ACM.  https://doi.org/10.1145/2039320.2039325.
  6. Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B., & Sampath, D. (2010). The YouTube video recommendation system. In Proceedings of the fourth ACM conference on recommender systems, recsys ’10 (pp. 293–296). New York: ACM.  https://doi.org/10.1145/1864708.1864770.
  7. Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., & Harshman, R.A. (1990). Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41, 391–407.CrossRefGoogle Scholar
  8. de Gemmis, M., Lops, P., Musto, C., Narducci, F., & Semeraro, G. (2015). Semantics-aware content-based recommender systems. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.) Recommender Systems Handbook (pp. 119–159). Springer.Google Scholar
  9. Deldjoo, Y., Elahi, M., Cremonesi, P., Garzotto, F., Piazzolla, P., & Quadrana, M. (2016). Content-based video recommendation system based on stylistic visual features. Journal of Data Semantics, 2016 (online version), 1–15.Google Scholar
  10. Dousson, C., & Le Maigat, P. (2007). Chronicle recognition improvement using temporal focusing and hierarchization. In Proceedings of the 20th international joint conference on artifical intelligence, IJCAI’07 (pp. 324–329). San Francisco: Morgan Kaufmann Publishers Inc. http://dl.acm.org/citation.cfm?id=1625275.1625326.
  11. Ehrmantraut, M., Härder, T., Wittig, H., & Steinmetz, R. (1996). The personal electronic program guide - towards the pre-selection of individual TV programs. In Proceedings of the fifth international conference on information and knowledge management, CIKM ’96 (pp. 243–250). New York: ACM.  https://doi.org/10.1145/238355.238505.
  12. Ferragina, P., & Scaiella, U. (2010). TAGME: On-the-fly annotation of short text fragments (by wikipedia entities). In Proceedings of the 19th ACM conference on information and knowledge management, CIKM 2010 (pp. 1625–1628). Toronto: ACM.  https://doi.org/10.1145/1871437.1871689.
  13. Fischer, S., Lienhart, R., & Effelsberg, W. (1995). Automatic recognition of film genres. In Proceedings of the third ACM international conference on multimedia, MULTIMEDIA ’95 (pp. 295–304). New York: ACM.  https://doi.org/10.1145/217279.215283.
  14. Gabrilovich, E., & Markovitch, S. (2006). Overcoming the brittleness bottleneck using Wikipedia: enhancing text categorization with encyclopedic knowledge, AAAI’06. In Proceedings of the 21st national conference on artificial intelligence (pp. 1301–1306): AAAI press.Google Scholar
  15. Harris, Z.S. (1968). Mathematical structures of language. New York: Interscience.Google Scholar
  16. Hu, W., Xie, N., Li, L., Zeng, X., & Maybank, S. (2011). A survey on visual content-based video indexing and retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 41 (6), 797–819.  https://doi.org/10.1109/TSMCC.2011.2109710.CrossRefGoogle Scholar
  17. Joachims, T. (1998). Text categorization with support vector machines: learning with many relevant features. In C. Nédellec, & C. Rouveirol (Eds.) Proceedings of ECML-98, 10th European conference on machine learning, lecture notes in artificial intelligence, (Vol. 1398 pp. 137–142). Heidelberg: Springer. http://www.joachims98.ps.
  18. Kennedy, L., & Hauptmann, A. (2006). LSCOM Lexicon definitions and annotations version 1.0, DTO challenge workshop on large scale concept ontology for multimedia, Tech. rep., Columbia University.Google Scholar
  19. Ko, H.G., Kim, E., Ko, I.Y., & Chang, D. (2014). Semantically-based recommendation by using semantic clusters of users’ viewing history. In International conference on big data and smart computing (BIGCOMP) (pp. 83–87). IEEE.Google Scholar
  20. Lowe, W. (2001). Towards a theory of semantic space. In Proceedings of the 23rd annual meeting of the cognitive science society (pp. 576–581).Google Scholar
  21. Martinez, A., Pazos Arias, J., Vilas, A., Duque, J., & Nores, M. (2009). What’s on TV tonight? An efficient and effective personalized recommender system of TV programs. IEEE Transactions on Consumer Electronics, 55 (1), 286–294.  https://doi.org/10.1109/TCE.2009.4814447.CrossRefGoogle Scholar
  22. Memar, S., Affendey, L.S., Mustapha, N., Doraisamy, S.C., & Ektefa, M. (2013). An integrated semantic-based approach in concept based video retrieval. Multimedia Tools and Applications, 64(1), 77–95.  https://doi.org/10.1007/s11042-011-0848-4.CrossRefGoogle Scholar
  23. Mittal, A., & Cheong, L.F. (2004). Addressing the problems of bayesian network classification of video using high-dimensional features. IEEE Transactions on Knowledge and Data Engineering, 16(2), 230–244.  https://doi.org/10.1109/TKDE.2004.1269600.CrossRefGoogle Scholar
  24. Mohammad, S., & Hirst, G. (2012). Distributional measures of semantic distance: a survey. CoRR arXiv:1203.1858.
  25. Musto, C. (2010). Enhanced vector space models for content-based recommender systems. In Proceedings of the fourth ACM conference on Recommender systems, RecSys ’10 (pp. 361–364). New York: ACM..  https://doi.org/10.1145/1864708.1864791
  26. Musto, C., Narducci, F., Lops, P., Semeraro, G., de Gemmis, M., Barbieri, M., Korst, J., Pronk, V., & Clout, R. (2012). Enhanced semantic TV-show representation for personalized electronic program guides. In Masthoff, J., Mobasher, B., Desmarais, M.C. & Nkambou, R. (Eds.) Proceedings of user modeling, adaptation, and personalization: 20th international conference, UMAP 2012, Montreal, Canada, July 16-20, 2012 (pp. 88–199). Berlin: Springer. ISBN 978-3-642-31454-4.  https://doi.org/10.1007/978-3-642-31454-4_16.CrossRefGoogle Scholar
  27. Musto, C., Semeraro, G., Lops, P., & de Gemmis, M. (2011). Random indexing and negative user preferences for enhancing content-based recommender systems. In E-commerce and web technologies - 12th international conference, EC-web 2011, Toulouse, France, August 30–September 1, 2011. Proceedings, lecture notes in business information processing Vol. 85 (pp. 270–281). Springer.Google Scholar
  28. Pappas, N., & Popescu-Belis, A. (2015). Combining content with user preferences for non-fiction multimedia recommendation: A study on TED lectures. Multimedia Tools and Applications, 74(4), 1175–1197.  https://doi.org/10.1007/s11042-013-1840-y.CrossRefGoogle Scholar
  29. Paschke, A., & Bichler, M. (2008). Knowledge representation concepts for automated SLA management. Decision Support Systems, 46(1), 187–205.  https://doi.org/10.1016/j.dss.2008.06.008.CrossRefGoogle Scholar
  30. Porter, M. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137.CrossRefGoogle Scholar
  31. Qi, G.J., Song, Y., Hua, X.S., Zhang, H.J., & Dai, L.R. (2006). Video annotation by active learning and cluster tuning. In Computer vision and pattern recognition workshop, 2006. CVPRW ’06 (pp. 114–114).  https://doi.org/10.1109/CVPRW.2006.211.
  32. Rocchio, J. (1971). Relevance feedback in information retrieval. In G. Salton (Ed.) The SMART retrieval system (pp. 313–323).Google Scholar
  33. Rubenstein, H., & Goodenough, J.B. (1965). Contextual correlates of synonymy. Communications of the ACM, 8(10), 627–633.  https://doi.org/10.1145/365628.365657.CrossRefGoogle Scholar
  34. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM, Computing Surveys, 34(1), 1–47.CrossRefGoogle Scholar
  35. Shapira, B., Rokach, L., & Freilikhman, S. (2013). Facebook single and cross domain data for recommendation systems. User Modeling and User-Adapted Interaction, 23(2-3), 211–247.  https://doi.org/10.1007/s11257-012-9128-x.CrossRefGoogle Scholar
  36. Shet, V., Harwood, D., & Davis, L. (2005). Vidmap: video monitoring of activity with prolog. In Advanced video and signal based surveillance, 2005. IEEE conference on AVSS 2005 (pp. 224–229).  https://doi.org/10.1109/AVSS.2005.1577271.
  37. Smeaton, A.F., Murphy, N., O’Connor, N.E., Marlow, S., Lee, H., McDonald, K., Browne, P., & Ye, J. (2001). The Físchlár digital video system: a digital library of broadcast TV programmes. In Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries (pp. 312–313). ACM.Google Scholar
  38. Smyth, B., & Cotter, P. (2001). Personalized electronic programme guides. Artificial Intelligence Magazine, 22(2), 89–98.CrossRefGoogle Scholar
  39. Snoek, C.G.M., Worring, M., van Gemert, J.C., Geusebroek, J.M., & Smeulders, A.W.M. (2006). The challenge problem for automated detection of 101 semantic concepts in multimedia. In Proceedings of the 14th annual ACM international conference on Multimedia, MULTIMEDIA ’06 (pp. 421–430). New York: ACM.  https://doi.org/10.1145/1180639.1180727.
  40. Tang, J., Hua, X.S., Wang, M., Gu, Z., Qi, G.J., & Wu, X. (2009). Correlative linear neighborhood propagation for video annotation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39 (2), 409–416.  https://doi.org/10.1109/TSMCB.2008.2006045.CrossRefGoogle Scholar
  41. Turney, P., & Pantel, P. (2010). From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research (JAIR), 37, 141–188.CrossRefGoogle Scholar
  42. Xu, J., Zhang, L.J., Lu, H., & Li, Y. (2002). The development and prospect of personalized TV program recommendation systems. In Proceedings of the fourth IEEE international symposium on multimedia software engineering, MSE ’02 (p. 82). Washington: IEEE Computer Society. http://dl.acm.org/citation.cfm?id=824463.824803.
  43. Xu, C., Wang, J., Lu, H., & Zhang, Y. (2008). A novel framework for semantic annotation and personalized retrieval of sports video. IEEE Transactions on Multimedia, 10 (3), 421–436.  https://doi.org/10.1109/TMM.2008.917346.CrossRefGoogle Scholar
  44. Yanagawa, A., Chang, S.F., Kennedy, L., & Hsu, W. (2007). Columbia University’s baseline detectors for 374 LSCOM semantic visual concepts. Columbia University ADVENT Technical Report.Google Scholar
  45. Yuan, Y. (2003). Research on video classification and retrieval. Xi’an: Ph.D. thesis, School of Electronic and Information Engineering, Xi’an Jiaotong University.Google Scholar
  46. Yuan, X., Lai, W., Mei, T., Hua, X.S., Wu, X.Q., & Li, S. (2006). Automatic video genre categorization using hierarchical svm. In IEEE international conference on image processing (pp. 2905–2908).  https://doi.org/10.1109/ICIP.2006.313037.

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Bari Aldo MoroBariItaly

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