Toward building a content-based video recommendation system based on low-level features

Lecture Notes in Business Information Processing

Volumn 239, Issue , 2015, Pages 45-56

  Deldjoo, Yashar ^a   Elahi, Mehdi ^a   Quadrana, Massimo ^a   Cremonesi, Paolo ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

Content based; Low level; Recommender systems; Video

Indexed keywords

COMMERCE; RECOMMENDER SYSTEMS; VIDEO RECORDING;

AUTOMATIC ANALYSIS; CONTENT-BASED; LOW LEVEL; LOW-LEVEL FEATURES; PERSONALIZED CONTENT; VIDEO; VIDEO CONTENTS; VISUAL FEATURE;

ELECTRONIC COMMERCE;

EID: 84955295218     PISSN: 18651348     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-27729-5_4     Document Type: Conference Paper

References (32)

1. Datasets - grouplens. http://grouplens.org/datasets/, Accessed: 01 May, 2015
2. Youtube. http://www.youtube.com. Accessed: 01 April, 2015
3. 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)
4. 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)
5. Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66-72 (1997)
6. Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. J. Comput. Vis. 12(1), 43-77 (1994)
7. Billsus, D., Pazzani, M.J.: User modeling for adaptive news access. User Model. User-Adap. Inter. 10(2-3), 147-180 (2000)
8. Bogdanov, D., Herrera, P.: How much metadata do we need in music recommenda- tion? a subjective evaluation using preference sets. In: ISMIR, pp. 97-102 (2011)
9. 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)
10. 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)
11. Burke, R.: Hybrid recommender systems: Survey and experiments. User Model. User-Adap. Inter. 12(4), 331-370 (2002)
12. Cantador, I., Szomszor, M., Alani, H., Fernández, M., Castells, P.: Enriching onto-logical user profiles with tagging history for multi-domain recommendations (2008)
13. Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143-177 (2004)
14. 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)
15. Horn, B.K., Schunck, B.G.: Determining optical flow. In: 1981 Technical Symposium East, pp. 319-331. International Society for Optics and Photonics (1981)
16. 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)
17. 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)
18. Lehinevych, T., Kokkinis-Ntrenis, N., Siantikos, G., Dogruöz, A.S., Giannakopou-los, T., Konstantopoulos, S.: Discovering similarities for content-based recommendation and browsing in multimedia collections
19. 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)
20. 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)
21. 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)
22. 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)
23. 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)
24. 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)
25. 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)
26. 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)
27. 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)
28. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4:2 (2009)
29. Radu, V.: Application. In: Radu, V. (ed.) Stochastic Modeling of Thermal Fatigue Crack Growth. ACM, vol. 1, pp. 63-70. Springer, Heidelberg (2015)
30. Yang, B., Mei, T., Hua, X.-S., Yang, L., Yang, S.-Q., Li, M.: Online video recom- mendation 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)
31. 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)
32. 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)