Abstract
Several recommendation systems have been developed to support the user in choosing an interesting movie from multimedia repositories. The widely utilized collaborative-filtering systems focus on the analysis of user profiles or user ratings of the items. However, these systems decrease their performance at the start-up phase and due to privacy issues, when a user hides most of his personal data. On the other hand, content-based recommendation systems compare movie features to suggest similar multimedia contents; these systems are based on less invasive observations, however they find some difficulties to supply tailored suggestions.
In this paper, we propose a plot-based recommendation system, which is based upon an evaluation of similarity among the plot of a video that was watched by the user and a large amount of plots that is stored in a movie database. Since it is independent from the number of user ratings, it is able to propose famous and beloved movies as well as old or unheard movies/programs that are still strongly related to the content of the video the user has watched.
We experimented different methodologies to compare natural language descriptions of movies (plots) and evaluated the Latent Semantic Analysis (LSA) to be the superior one in supporting the selection of similar plots. In order to increase the efficiency of LSA, different models have been experimented and in the end, a recommendation system that is able to compare about two hundred thousands movie plots in less than a minute has been developed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)
Balabanovic, M., Shoham, Y.: Fab: Content-based, collaborative recommendation. Communications of the ACM 40, 66–72 (1997)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: Using social and content-based information in recommendation. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 714–720. AAAI Press (1998)
Bennett, J., Lanning, S., Netflix, N.: The netflix prize. In: KDD Cup and Workshop in conjunction with KDD (2007)
Brand, M.: Fast online svd revisions for lightweight recommender systems. In: SIAM International Conference on Data Mining (2003)
Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)
Debnath, S., Ganguly, N., Mitra, P.: Feature weighting in content based recommendation system using social network analysis. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, pp. 1041–1042. ACM, New York (2008)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society for Information Science 41(6), 391–407 (1990)
Dumais, S.T.: Improving the retrieval of information from external sources. Behavior Research Methods, Instruments, & Computers 23(2), 229–236 (1991)
Fellbaum, C.(ed.) WordNet: An Electronic Lexical Database (Language, Speech, and Communication), illustrated edn. The MIT Press (May 1998)
Gemulla, R., Nijkamp, E., Haas, P.J., Sismanis, Y.: Large-scale matrix factorization with distributed stochastic gradient descent. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 69–77. ACM, New York (2011)
Kontostathis, A., Pottenger, W.M.: A framework for understanding latent semantic indexing (lsi) performance. Inf. Process. Manage. 42, 56–73 (2006)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Landauer, T.K., Dutnais, S.T.: A solution to platos problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 211–240 (1997)
Landauer, T.K., Laham, D., Foltz, P.: Learning Human-like Knowledge by Singular Value Decomposition: A Progress Report. In: Jordan, M.I., Kearns, M.J., Solla, S.A. (eds.) Advances in Neural Information Processing Systems, vol. 10, The MIT Press (1998)
Lang, K.: Newsweeder: Learning to filter netnews. In: Proceedings of the 12th International Machine Learning Conference, ML 1995 (1995)
Lee, M.D., Welsh, M.: An empirical evaluation of models of text document similarity. In: CogSci 2005, pp. 1254–1259. Erlbaum (2005)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Miller, B.N., Albert, I., Lam, S.K., Konstan, J.A., Riedl, J.: Movielens unplugged: Experiences with an occasionally connected recommender system. In: Proceedings of ACM 2003 Conference on Intelligent User Interfaces (IUI 2003), Chapel Hill, North Carolina. ACM (2003)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries, DL 2000, pp. 195–204. ACM, New York (2000)
Navigli, R.: Word sense disambiguation: A survey. ACM Comput. Surv. 41(2) (2009)
Po, L., Sorrentino, S.: Automatic generation of probabilistic relationships for improving schema matching. Inf. Syst. 36(2), 192–208 (2011)
Řehůřek, R., Sojka, P.: Software Framework for Topic Modelling with Large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Valletta, Malta, pp. 45–50. ELRA (May 2010), http://is.muni.cz/publication/884893/en
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18, 613–620 (1975)
Schmid, H.: Probabilistic Part-of-Speech Tagging Using Decision Trees. In: Proceedings of the International Conference on New Methods in Language Processing, pp. 44–49 (1994)
Sorrentino, S., Bergamaschi, S., Gawinecki, M., Po, L.: Schema label normalization for improving schema matching. Data Knowl. Eng. 69(12), 1254–1273 (2010)
Trillo, R., Po, L., Ilarri, S., Bergamaschi, S., Mena, E.: Using semantic techniques to access web data. Inf. Syst. 36(2), 117–133 (2011)
Yunhua, G., Shu, S., Guansheng, Z.: Application of nosql database in web crawling. International Journal of Digital Content Technology and its Applications (JDCTA) 5(6), 261–266 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Farinella, T., Bergamaschi, S., Po, L. (2012). A Non-intrusive Movie Recommendation System. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2012. OTM 2012. Lecture Notes in Computer Science, vol 7566. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33615-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-33615-7_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33614-0
Online ISBN: 978-3-642-33615-7
eBook Packages: Computer ScienceComputer Science (R0)