Abstract
Previous works have shown the effectiveness of using stylistic visual features, indicative of the movie style, in content-based movie recommendation. However, they have mainly focused on a particular recommendation scenario, i.e., when a new movie is added to the catalogue and no information is available for that movie (New Item scenario). However, the stylistic visual features can be also used when other sources of information is available (Existing Item scenario).
In this work, we address the second scenario and propose a hybrid technique that exploits not only the typical content available for the movies (e.g., tags), but also the stylistic visual content extracted form the movie files and fuse them by applying a fusion method called Canonical Correlation Analysis (CCA). Our experiments on a large catalogue of 13 K movies have shown very promising results which indicates a considerable improvement of the recommendation quality by using a proper fusion of the stylistic visual features with other type of features.
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Notes
- 1.
Note that though textual in nature, we treat metadata as a separate modality which is added to a video by a community-user (tag) or an expert (genre). Refer to Table 1 for further illustration.
- 2.
The dataset is called Mise-en-scene Dataset and it is publicly available through the following link: http://recsys.deib.polimi.it.
References
Aggarwal, C.C.: Content-based recommender systems. In: Recommender Systems, pp. 139–166. Springer, Heidelberg (2016)
Aggarwal, C.C.: Recommender Systems: The Textbook. Springer, Heidelberg (2016)
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)
Cantador, I., Szomszor, M., Alani, H., Fernández, M., Castells, P.: Enriching ontological user profiles with tagging history for multi-domain recommendations (2008)
Celma, O.: Music recommendation. In: Music Recommendation and Discovery, pp. 43–85. Springer, Heidelberg (2010)
Cremonesi, P., Garzotto, F., Negro, S., Papadopoulos, A.V., Turrin, R.: Looking for “Good” recommendations: a comparative evaluation of recommender systems. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., Winckler, M. (eds.) INTERACT 2011. LNCS, vol. 6948, pp. 152–168. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23765-2_11
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the ACM Conference on Recommender Systems, RecSys 2010, Barcelona, Spain, pp. 39–46, 26–30 September 2010
de Gemmis, M., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Semantics-aware content-based recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 119–159. Springer, Heidelberg (2015)
Degemmis, M., Lops, P., Semeraro, G.: A content-collaborative recommender that exploits wordnet-based user profiles for neighborhood formation. User Model. User-Adap. Inter. 17(3), 217–255 (2007)
Deldjoo, Y., Elahi, M., Cremonesi, P.: Using visual features and latent factors for movie recommendation. In: Workshop on New Trends in Content-Based Recommender Systems (CBRecSys), in Conjugation with ACM Recommender Systems Conference (RecSys) (2016)
Deldjoo, Y., Elahi, M., Cremonesi, P., Garzotto, F., Piazzolla, P.: Recommending movies based on mise-en-scene design. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1540–1547. ACM (2016)
Deldjoo, Y., Elahi, M., Cremonesi, P., Garzotto, F., Piazzolla, P., Quadrana, M.: Content-based video recommendation system based on stylistic visual features. J. Data Semant. 5(2), 99–113 (2016)
Deldjoo, Y., Elahi, M., Quadrana, M., Cremonesi, P.: Toward building a content-based video recommendation system based on low-level features. In: Stuckenschmidt, H., Jannach, D. (eds.) EC-Web 2015. LNBIP, vol. 239, pp. 45–56. Springer, Heidelberg (2015). doi:10.1007/978-3-319-27729-5_4
Deldjoo, Y., Elahi, M., Quadrana, M., Cremonesi, P., Garzotto, F.: Toward effective movie recommendations based on mise-en-scène film styles. In: Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter, pp. 162–165. ACM (2015)
Elahi, M., Ricci, F., Rubens, N.: A survey of active learning in collaborative filtering recommender systems. Comput. Sci. Rev. 20, 29–50 (2016)
Fernández-Tobías, I., Braunhofer, M., Elahi, M., Ricci, F., Cantador, I.: Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model. User-Adap. Inter. 26(2), 221–255 (2016)
Guy, I., Zwerdling, N., Ronen, I., Carmel, D., Uziel, E.: Social media recommendation based on people and tags. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 194–201. ACM (2010)
Haghighat, M., Abdel-Mottaleb, M., Alhalabi, W.: Fully automatic face normalization and single sample face recognition in unconstrained environments. Expert Syst. Appl. 47, 23–34 (2016)
Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 16(12), 2639–2664 (2004)
Harper, F.M., Konstan, J.A.: The movielens datasets: History and context. ACM Trans. Interact. Intell. Syst. (TiiS) 5(4), 19 (2015)
Hotelling, H.: Relations between two sets of variates. Biometrika 28(3/4), 321–377 (1936)
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)
Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: Proceedings of the 17th International Conference on World Wide Web, pp. 675–684. ACM (2008)
Mei, T., Yang, B., Hua, X.-S., Li, S.: Contextual video recommendation by multimodal relevance and user feedback. ACM Trans. Inf. Syst. (TOIS) 29(2), 10 (2011)
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)
Nasery, M., Braunhofer, M., Ricci, F.: Recommendations with optimal combination of feature-based and item-based preferences. In: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 269–273. ACM (2016)
Nasery, M., Elahi, M., Cremonesi, P.: Polimovie: a feature-based dataset for recommender systems. In: ACM RecSys Workshop on Crowdsourcing and Human Computation for Recommender Systems (CrawdRec), vol. 3, pp. 25–30. ACM (2015)
Pereira, J.C., Coviello, E., Doyle, G., Rasiwasia, N., Lanckriet, G.R., Levy, R., Vasconcelos, N.: On the role of correlation and abstraction in cross-modal multimedia retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 36(3), 521–535 (2014)
Rasheed, Z., Shah, M.: Video categorization using semantics and semiotics. In: Rosenfeld, A., Doermann, D., DeMenthon, D. (eds.) Video Mining. The Springer International Series in Video Computing, vol. 6, pp. 185–217. Springer, Heidelberg (2003)
Rasheed, Z., Sheikh, Y., Shah, M.: On the use of computable features for film classification. IEEE Trans. Circuits Syst. Video Technol. 15(1), 52–64 (2005)
Rasiwasia, N., Costa Pereira, J., Coviello, E., Doyle, G., Lanckriet, G.R., Levy, R., Vasconcelos, N.: A new approach to cross-modal multimedia retrieval. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 251–260. ACM (2010)
Rubens, N., Elahi, M., Sugiyama, M., Kaplan, D.: Active learning in recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 809–846. Springer, Heidelberg (2015)
Szomszor, M., Cattuto, C., Alani, H., O’Hara, K., Baldassarri, A., Loreto, V., Servedio, V.D.: Folksonomies, the semantic web, and movie recommendation (2007)
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)
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)
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Deldjoo, Y., Elahi, M., Cremonesi, P., Moghaddam, F.B., Caielli, A.L.E. (2017). How to Combine Visual Features with Tags to Improve Movie Recommendation Accuracy?. In: Bridge, D., Stuckenschmidt, H. (eds) E-Commerce and Web Technologies. EC-Web 2016. Lecture Notes in Business Information Processing, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-319-53676-7_3
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