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A Comparative Analysis of State-of-the-Art Recommendation Techniques in the Movie Domain

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

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Abstract

Recommender systems (RSs) represent one of the manifold applications in which Machine Learning can unfold its potential. Nowadays, most of the major online sites selling products and services provide users with RSs that can assist them in their online experience. In recent years, therefore, we have witnessed an impressive series of proposals for novel recommendation techniques that claim to ensure significative improvements compared to classic techniques. In this work, we analyze some of them from a theoretical and experimental point of view and verify whether they can deliver tangible real improvements in terms of performance. Among others, we have experimented with traditional model-based and memory-based collaborative filtering, up to the most recent recommendation techniques based on deep learning. We have chosen the movie domain as an application scenario, and a version of the classic MovieLens as a dataset for training and testing our models.

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Notes

  1. 1.

    grouplens.org/datasets/movielens/100k/ (Accessed: June 23, 2020).

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Authors and Affiliations

  1. Department of Engineering, Roma Tre University, Via della Vasca Navale, 79, 00146, Rome, Italy

    Dalia Valeriani, Giuseppe Sansonetti & Alessandro Micarelli

Authors
  1. Dalia Valeriani
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  2. Giuseppe Sansonetti
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  3. Alessandro Micarelli
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Corresponding author

Correspondence to Giuseppe Sansonetti .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Valeriani, D., Sansonetti, G., Micarelli, A. (2020). A Comparative Analysis of State-of-the-Art Recommendation Techniques in the Movie Domain. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-58811-3_8

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