Abstract
In this paper, we propose a model for exploiting community based usage information for video retrieval. Implicit usage information from a pool of past users could be a valuable source to address the difficulties caused due to the semantic gap problem. We propose a graph-based implicit feedback model in which all the usage information can be represented. A number of recommendation algorithms were suggested and experimented. A simulated user evaluation is conducted on the TREC VID collection and the results are presented. Analyzing the results we found some common characteristics on the best performing algorithms, which could indicate the best way of exploiting this type of usage information.
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References
Craswell, N., Szummer, M.: Random walks on the click graph. In: IGIR 2007: Proceedings of the 30th annual international ACM SIGIR 2007, pp. 239–246. ACM, New York (2007)
Finin, T.W.: GUMS: A General User Modeling Shell. In: User Models in Dialog Systems, pp. 411–430. Springer, Heidelberg (1989)
Hopfgartner, F., Urban, J., Villa, R., Jose, J.: Simulated Testing of an Adaptive Multimedia Information Retrieval System. Content-Based Multimedia Indexing, 2007. In: CBMI 2007. International Workshop, Bourdeaux, France, pp. 328–335 (2007)
Hopfgartner, F., Jose, J.: Evaluating the Implicit Feedback Models for Adaptive Video Retrieval. In: ACMMIR 2007, pp. 323–331 (2007)
Jaimes, A., Christel, M., Gilles, S., Ramesh, S., Ma, W.: Multimedia Information Retrieval: What is it, and why isn’t anyone using it? In: ACM MIR 2005, pp. 3–8. ACM Press, New York (2005)
Kelly, D., Belkin, N.J.: Display time as implicit feedback: understanding task effects. In: ACM SIGIR 2004., pp. 377–384. ACM, New York (2004)
Kelly, D., Teevan, J.: Implicit Feedback for Inferring User Preference: A Bibliography. SIGIR Forum 37(2), 18–28 (2003)
Over, P., Ianeva, T.: TRECVID 2006 Overview. In: TRECVid 2006 – Text Retrieval Conference TRECVID, Gaithersburg, MD (2006)
Urban, J., Hilaire, X., Hopfgartner, F., Villa, R., Jose, J., Chantamunee, S., Goto, Y.: TRECVID 2006. TRECVID, Glasgow University, Gaithersburg, MD (2006)
White, R., Bilenko, M., Cucerzan, S.: Studying the use of popular destinations to enhance web search interaction. In: ACM SIGIR 2007, pp. 159–166. ACM Press, New York (2007)
White, R., Jose, J.M., van Rijsbergen, C.J., Ruthven, I.: A Simulated Study of Implicit Feedback Models. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 311–326. Springer, Heidelberg (2004)
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Vallet, D., Hopfgartner, F., Jose, J. (2008). Use of Implicit Graph for Recommending Relevant Videos: A Simulated Evaluation. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_20
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DOI: https://doi.org/10.1007/978-3-540-78646-7_20
Publisher Name: Springer, Berlin, Heidelberg
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