Abstract
One of the challenges in video recommendation systems is the New Item problem, which happens when the system is unable to recommend video items, that no information is available about them. For example, in the popular movie-sharing websites, such as Youtube, everyday, hundred millions of hours of videos are uploaded and big portion of these videos may not contain any meta-data, to be used by the system to generate recommendations.
In this paper, we address this problem by proposing a method, that is based on automatic analysis of the video content in order to extract a number representative low-level visual features. Such features are then used to generate personalized content-based recommendations. Our evaluation shows that our proposed method can outperform the baselines, by producing more relevant recommendations. Hence, a set low-level features extracted automatically can be more descriptive and informative of the video content than a set of high-level expert annotated features.
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References
Datasets – grouplens. http://grouplens.org/datasets/, Accessed: 01 May, 2015
Youtube. http://www.youtube.com. Accessed: 01 April, 2015
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)
Ahn, J.-W., Brusilovsky, P., Grady, J., He, D., Syn, S.Y.: Open user profiles for adaptive news systems: help or harm? In: Proceedings of the 16th international conference on World Wide Web, pp. 11–20. ACM (2007)
Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. J. Comput. Vis. 12(1), 43–77 (1994)
Billsus, D., Pazzani, M.J.: User modeling for adaptive news access. User Model. User-Adap. Inter. 10(2–3), 147–180 (2000)
Bogdanov, D., Herrera, P.: How much metadata do we need in music recommendation? a subjective evaluation using preference sets. In: ISMIR, pp. 97–102 (2011)
Bogdanov, D., Serrà, J., Wack, N., Herrera, P., Serra, X.: Unifying low-level and high-level music similarity measures. IEEE Trans. Multimedia 13(4), 687–701 (2011)
Brezeale, D., Cook, D.J.: Automatic video classification: a survey of the literature. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(3), 416–430 (2008)
Burke, R.: Hybrid recommender systems: Survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)
Cantador, I., Szomszor, M., Alani, H., Fernández, M., Castells, P.: Enriching ontological user profiles with tagging history for multi-domain recommendations (2008)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143–177 (2004)
Elahi, M., Ricci, F., Rubens, N.: Active learning strategies for rating elicitation in collaborative filtering: a system-wide perspective. ACM Trans. Intell. Syst. Technol. (TIST) 5(1), 13 (2013)
Horn, B.K., Schunck, B.G.: Determining optical flow. In: 1981 Technical Symposium East, pp. 319–331. International Society for Optics and Photonics (1981)
Hu, W., Xie, N., Li, L., Zeng, X., Maybank, S.: A survey on visual content-based video indexing and retrieval. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(6), 797–819 (2011)
Knees, P., Pohle, T., Schedl, M., Widmer, G.: A music search engine built upon audio-based and web-based similarity measures. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and Development in Information Retrieval, pp. 447–454. ACM (2007)
Lehinevych, T., Kokkinis-Ntrenis, N., Siantikos, G., Dogruöz, A.S., Giannakopoulos, T., Konstantopoulos, S.: Discovering similarities for content-based recommendation and browsing in multimedia collections
Lops, P., De Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, Heidelberg (2011)
Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 54–88 (2004)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital libraries, pp. 195–204. ACM (2000)
Pazzani, M.J., Billsus, D.: Content-based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Rasheed, Z., Shah, M.: Video categorization using semantics and semiotics. In: Rosenfeld, A., Doermann, D., DeMenthon, D. (eds.) Video Mining, pp. 185–217. Springer, Heidelberg (2003)
Rasheed, Z., Sheikh, Y., Shah, M.: On the use of computable features for film classification. IEEE Trans. Circ. Syst. Video Technol. 15(1), 52–64 (2005)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer Verlag, Heidelberg (2011)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 1–35. Springer Verlag, Heidelberg (2011)
Seyerlehner, K., Schedl, M., Pohle, T., Knees, P.: Using block-level features for genre classification, tag classification and music similarity estimation. Submission to Audio Music Similarity and Retrieval Task of MIREX 2010 (2010)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4:2 (2009)
Radu, V.: Application. In: Radu, V. (ed.) Stochastic Modeling of Thermal Fatigue Crack Growth. ACM, vol. 1, pp. 63–70. Springer, Heidelberg (2015)
Yang, B., Mei, T., Hua, X.-S., Yang, L., Yang, S.-Q., Li, M.: Online video recommendation based on multimodal fusion and relevance feedback. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 73–80. ACM (2007)
Zhao, X., Li, G., Wang, M., Yuan, J., Zha, Z.-J., Li, Z., Chua, T.-S.: Integrating rich information for video recommendation with multi-task rank aggregation. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 1521–1524. ACM (2011)
Zhou, H., Hermans, T., Karandikar, A.V., Rehg, J.M.: Movie genre classification via scene categorization. In: Proceedings of the International Conference on Multimedia, pp. 747–750. ACM (2010)
Acknowledgments
This work is supported by Telecom Italia S.p.A., Open Innovation Department, Joint Open Lab S-Cube, Milan.
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Deldjoo, Y., Elahi, M., Quadrana, M., Cremonesi, P. (2015). Toward Building a Content-Based Video Recommendation System Based on Low-Level Features. In: Stuckenschmidt, H., Jannach, D. (eds) E-Commerce and Web Technologies. EC-Web 2015. Lecture Notes in Business Information Processing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-27729-5_4
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