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International Conference on Electronic Commerce and Web Technologies

EC-Web 2016: E-Commerce and Web Technologies pp 34–45Cite as

How to Combine Visual Features with Tags to Improve Movie Recommendation Accuracy?

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 278))

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. 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. 2.

    The dataset is called Mise-en-scene Dataset and it is publicly available through the following link: http://recsys.deib.polimi.it.

References

  1. Aggarwal, C.C.: Content-based recommender systems. In: Recommender Systems, pp. 139–166. Springer, Heidelberg (2016)

    Google Scholar 

  2. Aggarwal, C.C.: Recommender Systems: The Textbook. Springer, Heidelberg (2016)

    Book  Google Scholar 

  3. 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)

    Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. Cantador, I., Szomszor, M., Alani, H., Fernández, M., Castells, P.: Enriching ontological user profiles with tagging history for multi-domain recommendations (2008)

    Google Scholar 

  7. Celma, O.: Music recommendation. In: Music Recommendation and Discovery, pp. 43–85. Springer, Heidelberg (2010)

    Google Scholar 

  8. 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

    Chapter  Google Scholar 

  9. 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

    Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. 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)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. 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

    Chapter  Google Scholar 

  16. 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)

    Google Scholar 

  17. Elahi, M., Ricci, F., Rubens, N.: A survey of active learning in collaborative filtering recommender systems. Comput. Sci. Rev. 20, 29–50 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. 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)

    Article  MATH  Google Scholar 

  22. Harper, F.M., Konstan, J.A.: The movielens datasets: History and context. ACM Trans. Interact. Intell. Syst. (TiiS) 5(4), 19 (2015)

    Google Scholar 

  23. Hotelling, H.: Relations between two sets of variates. Biometrika 28(3/4), 321–377 (1936)

    Article  MATH  Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. 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)

    Google Scholar 

  26. 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)

    Article  Google Scholar 

  27. 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)

    Google Scholar 

  28. 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)

    Google Scholar 

  29. 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)

    Google Scholar 

  30. 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)

    Article  Google Scholar 

  31. 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)

    Chapter  Google Scholar 

  32. 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)

    Article  Google Scholar 

  33. 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)

    Google Scholar 

  34. 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)

    Chapter  Google Scholar 

  35. Szomszor, M., Cattuto, C., Alani, H., O’Hara, K., Baldassarri, A., Loreto, V., Servedio, V.D.: Folksonomies, the semantic web, and movie recommendation (2007)

    Google Scholar 

  36. 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)

    Google Scholar 

  37. 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)

    Google Scholar 

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Authors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi, Farshad Bakhshandegan Moghaddam & Andrea Luigi Edoardo Caielli

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  1. Yashar Deldjoo
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  2. Mehdi Elahi
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  3. Paolo Cremonesi
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  4. Farshad Bakhshandegan Moghaddam
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  5. Andrea Luigi Edoardo Caielli
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Corresponding author

Correspondence to Yashar Deldjoo .

Editor information

Editors and Affiliations

  1. The Insight Centre for Data Analytics, University College Cork , Cork, Ireland

    Derek Bridge

  2. Data and Web Science Group, University of Mannheim, Mannheim, Germany

    Heiner Stuckenschmidt

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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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  • DOI: https://doi.org/10.1007/978-3-319-53676-7_3

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