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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).

References

  1. Aggarwal, C.C.: Recommender Systems: The Textbook, 1st edn. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3

    Book  Google Scholar 

  2. Biancalana, C., Flamini, A., Gasparetti, F., Micarelli, A., Millevolte, S., Sansonetti, G.: Enhancing traditional local search recommendations with context-awareness. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 335–340. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22362-4_29

    Chapter  Google Scholar 

  3. Biancalana, C., Gasparetti, F., Micarelli, A., Miola, A., Sansonetti, G.: Context-aware movie recommendation based on signal processing and machine learning. In: Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation. CAMRa 2011, pp. 5–10. ACM, New York (2011)

    Google Scholar 

  4. Bologna, C., De Rosa, A.C., De Vivo, A., Gaeta, M., Sansonetti, G., Viserta, V.: Personality-based recommendation in e-commerce. In: CEUR Workshop Proceedings, Aachen, Germany, vol. 997. CEUR-WS.org (2013)

    Google Scholar 

  5. Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_12

    Chapter  Google Scholar 

  6. Caldarelli, S., Feltoni Gurini, D., Micarelli, A., Sansonetti, G.: A signal-based approach to news recommendation. In: CEUR Workshop Proceedings, Aachen, Germany, vol. 1618. CEUR-WS.org (2016)

    Google Scholar 

  7. Colombo-Mendoza, L.O., Paredes-Valverde, M.A., Salas-Zárate, M.P., Bustos-López, M., Sánchez-Cervantes, J.L., Alor-Hernández, G.: Recommender systems in the offline retailing domain: a systematic literature review. In: García-Alcaraz, J.L., Sánchez-Ramírez, C., Avelar-Sosa, L., Alor-Hernández, G. (eds.) Techniques, Tools and Methodologies Applied to Global Supply Chain Ecosystems. ISRL, vol. 166, pp. 383–409. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-26488-8_17

    Chapter  Google Scholar 

  8. D’Aniello, G., Gaeta, M., Orciuoli, F., Sansonetti, G., Sorgente, F.: Knowledge-based smart city service system. Electronics (Switzerland) 9(6), 1–22 (2020)

    Google Scholar 

  9. De Angelis, A., Gasparetti, F., Micarelli, A., Sansonetti, G.: A social cultural recommender based on linked open data. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. UMAP 2017, pp. 329–332, ACM, New York (2017)

    Google Scholar 

  10. Gurini, D.F., Gasparetti, F., Micarelli, A., Sansonetti, G.: iSCUR: interest and sentiment-based community detection for user recommendation on Twitter. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 314–319. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08786-3_27

    Chapter  Google Scholar 

  11. Feltoni Gurini, D., Gasparetti, F., Micarelli, A., Sansonetti, G.: Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization. Future Gener. Comput. Syst. 78, 430–439 (2018)

    Article  Google Scholar 

  12. Fogli, A., Sansonetti, G.: Exploiting semantics for context-aware itinerary recommendation. Pers. Ubiquit. Comput. 23(2), 215–231 (2019). https://doi.org/10.1007/s00779-018-01189-7

    Article  Google Scholar 

  13. Gasparetti, F., Micarelli, A., Sansonetti, G.: Exploiting web browsing activities for user needs identification. In: 2014 International Conference on Computational Science and Computational Intelligence, vol. 2, pp. 86–89, March 2014

    Google Scholar 

  14. Hassan, H.A.M., Sansonetti, G., Gasparetti, F., Micarelli, A.: Semantic-based tag recommendation in scientific bookmarking systems. In: Proceedings of the 12th ACM Conference on Recommender Systems. RecSys 2018, pp. 465–469. ACM, New York (2018)

    Google Scholar 

  15. Hassan, H.A.M., Sansonetti, G., Gasparetti, F., Micarelli, A., Beel, J.: BERT, ELMo, USE and InferSent sentence encoders: the panacea for research-paper recommendation? In: Tkalcic, M., Pera, S. (eds.) Proceedings of ACM RecSys 2019 Late-Breaking Results, vol. 2431, pp. 6–10. CEUR-WS.org (2019)

    Google Scholar 

  16. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.: Neural collaborative filtering. CoRR abs/1708.05031 (2017)

    Google Scholar 

  17. Idrissi, N., Zellou, A.: A systematic literature review of sparsity issues in recommender systems. Soc. Netw. Anal. Min. 10(1), 1–23 (2020). https://doi.org/10.1007/s13278-020-0626-2

    Article  Google Scholar 

  18. Isinkaye, F., Folajimi, Y., Ojokoh, B.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16(3), 261–273 (2015)

    Article  Google Scholar 

  19. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction, 1st edn. Cambridge University Press, New York (2010)

    Book  Google Scholar 

  20. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR) (2015)

    Google Scholar 

  21. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  22. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  23. Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: Proceedings of SIAM Data Mining (SDM 2005) (2005)

    Google Scholar 

  24. Micarelli, A., Neri, A., Sansonetti, G.: A case-based approach to image recognition. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS, vol. 1898, pp. 443–454. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44527-7_38

    Chapter  Google Scholar 

  25. Onori, M., Micarelli, A., Sansonetti, G.: A comparative analysis of personality-based music recommender systems. In: CEUR Workshop Proceedings, Aachen, Germany, vol. 1680, pp. 55–59. CEUR-WS.org (2016)

    Google Scholar 

  26. Ricci, F.: Recommender systems in tourism. In: Handbook of e-Tourism, pp. 1–18 (2020)

    Google Scholar 

  27. Saha, J., Chowdhury, C., Biswas, S.: Review of machine learning and deep learning based recommender systems for health informatics. In: Dash, S., Acharya, B.R., Mittal, M., Abraham, A., Kelemen, A. (eds.) Deep Learning Techniques for Biomedical and Health Informatics. SBD, vol. 68, pp. 101–126. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33966-1_6

    Chapter  Google Scholar 

  28. Sansonetti, G.: Point of interest recommendation based on social and linked open data. Pers. Ubiquit. Comput. 23(2), 199–214 (2019). https://doi.org/10.1007/s00779-019-01218-z

    Article  Google Scholar 

  29. Sansonetti, G., Feltoni Gurini, D., Gasparetti, F., Micarelli, A.: Dynamic social recommendation. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. ASONAM 2017, pp. 943–947. ACM, New York (2017)

    Google Scholar 

  30. Sansonetti, G., Gasparetti, F., Micarelli, A., Cena, F., Gena, C.: Enhancing cultural recommendations through social and linked open data. User Model. User Adap. Inter. 29(1), 121–159 (2019). https://doi.org/10.1007/s11257-019-09225-8

    Article  Google Scholar 

  31. Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D Nonlinear Phenom. 404, 132306 (2020)

    Article  MathSciNet  Google Scholar 

  32. Silveira, T., Zhang, M., Lin, X., Liu, Y., Ma, S.: How good your recommender system is? a survey on evaluations in recommendation. Int. J. Mach. Learn. Cybern. 10(5), 813–831 (2019). https://doi.org/10.1007/s13042-017-0762-9

    Article  Google Scholar 

  33. Tegmark, M.: Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf Publishing Group, New York City (2017)

    Google Scholar 

  34. Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. 52(1), 1–38 (2019)

    Google Scholar 

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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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