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Comparing LDA and LSA Topic Models for Content-Based Movie Recommendation Systems

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Web Information Systems and Technologies (WEBIST 2014)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 226))

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Abstract

We propose a plot-based recommendation system, which is based upon an evaluation of similarity between the plot of a video that was watched by a user and a large amount of plots stored in a movie database. Our system is independent from the number of user ratings, thus 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. The system implements and compares the two Topic Models, Latent Semantic Allocation (LSA) and Latent Dirichlet Allocation (LDA), on a movie database of two hundred thousand plots that has been constructed by integrating different movie databases in a local NoSQL (MongoDB) DBMS. The topic models behaviour has been examined on the basis of standard metrics and user evaluations, performance assessments with 30 users to compare our tool with a commercial system have been conducted.

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Notes

  1. 1.

    http://2011.camrachallenge.com/.

  2. 2.

    http://www.imdb.com/.

  3. 3.

    http://dbpedia.org/.

  4. 4.

    http://www.themoviedb.org/.

  5. 5.

    http://www.mongodb.org/.

  6. 6.

    http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/.

  7. 7.

    One database access, using MongoDB, takes about 0.3 ms while the extraction of keywords from a plot takes more than one second.

  8. 8.

    Thus, we store the matrix of document-topic vectors to represent the training set.

  9. 9.

    In [4] work we determined 500 as a good number of topic. This value allows to have reasonable computational costs, and to maintain an appropriate level of accuracy.

  10. 10.

    Several experiments where conducted on a subset of the test set.

  11. 11.

    http://radimrehurek.com/gensim/.

  12. 12.

    The cost refers to a virtual machine set up with VMWare Workstation 9.0.1, installed on a server that has the following features: OS: Ubuntu 12.04 LTS 64-bit; RAM: 8 GB; 20 GB dedicated to the virtual hard disk; 4 cores. The DataBase Management System used is MongoDB 2.4.1, and it was installed on a machine with the following characteristics: OS: Windows Server 2008 R2 64-bit; CPU: Intel (R) Xeon E5620 Ghz 2:40; RAM: 12 GB.

  13. 13.

    http://www.imdb.com/chart/top.

  14. 14.

    http://www.jinni.com/.

  15. 15.

    http://wordnet.princeton.edu/.

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Acknowledgements

The system has been developed in collaboration between the database group of the University of Modena and Reggio Emilia and vfree.tv (http://vfree.tv), a young and innovative German company focused on creating new ways of distributing television content and generating an unprecedented watching experience for the user.

We also want to express our gratitude to Tania Farinella, Matteo Abbruzzo and Olga Kryukova, master students in Computer Engineering and Science at the Department of Engineering “Enzo Ferrari” at University of Modena and Reggio Emilia for their contribution in term of implementation of the first and second version of the system (without and with LDA) and for their support during the evaluation of the system. Particular appreciation goes to Serena Sorrentino that helps us to integrate the LDA models in our system.

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Authors and Affiliations

  1. Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, 41125, Modena, Italy

    Sonia Bergamaschi & Laura Po

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  1. Sonia Bergamaschi
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  2. Laura Po
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Correspondence to Laura Po .

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Editors and Affiliations

  1. University of Paris, Paris, Paris, France

    Valérie Monfort

  2. RWTH Aachen University, Aachen, Germany

    Karl-Heinz Krempels

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Bergamaschi, S., Po, L. (2015). Comparing LDA and LSA Topic Models for Content-Based Movie Recommendation Systems. In: Monfort, V., Krempels, KH. (eds) Web Information Systems and Technologies. WEBIST 2014. Lecture Notes in Business Information Processing, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-27030-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-27030-2_16

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