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Impulsion of Movie’s Content-Based Factors in Multi-modal Movie Recommendation System

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1969))

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Abstract

Nowadays the Recommendation System, a subclass of information filtering system does not require any introduction, and the movie recommendation system plays an important role in the streaming platform where a huge number of movies are required to be analyzed before showcasing a perfectly matched subset of them to its users.

The existing works in this domain focus only on the output and consider the model’s input similar for all users. But actually, the movie embedding input vector varies on a user basis. A user’s perception of a movie depends on the movie’s genre as well as its meta information (story, director, and cast). To formulate the fact, we have introduced two scores, (i) TextLike_score (TL_score) and (ii) GenreLike_score (GL_score). Our proposed Cross-Attention-based Model outperforms the SOTA (state-of-the-art) by leveraging the effect of the scores and satisfying our factual notion.

In this paper, we have evaluated our model’s performance over two different datasets, (i) MovieLens-100K(ML-100K) and (ii) MFVCD-7K. Regarding multi-modality, the audio-video information of movies’ are used and textual information has been employed for score calculation. Finally, it is experimentally proved that the Cross-Attention-based multi-modal movie recommendation system with the proposed Meta_score successfully covers all the analytical queries supporting the purpose of the experiment.

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Notes

  1. 1.

    https://grouplens.org/datasets/movielens/100k/.

  2. 2.

    https://grouplens.org/datasets/movielens/20m/.

References

  1. Baevski, A., Zhou, Y., Mohamed, A., Auli, M.: wav2vec 2.0: A framework for self-supervised learning of speech representations. In: Advances in Neural Information Processing Systems 33, pp. 12449–12460 (2020)

    Google Scholar 

  2. Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: ICML, vol. 2, p. 4 (2021)

    Google Scholar 

  3. Chakder, D., Mondal, P., Raj, S., Saha, S., Ghosh, A., Onoe, N.: Graph network based approaches for multi-modal movie recommendation system. In: 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 409–414. IEEE (2022)

    Google Scholar 

  4. Choudhury, S.S., Mohanty, S.N., Jagadev, A.K.: Multimodal trust based recommender system with machine learning approaches for movie recommendation. Int. J. Inf. Technol. 13(2), 475–482 (2021). https://doi.org/10.1007/s41870-020-00553-2

    Article  Google Scholar 

  5. Deldjoo, Y.: Enhancing video recommendation using multimedia content. Spec. Top. Inf. Technol. pp. 77–89 (2020). https://doi.org/10.1007/978-3-030-32094-2_6

  6. Deldjoo, Y., Schedl, M.: Retrieving relevant and diverse movie clips using the mfvcd-7k multifaceted video clip dataset. In: 2019 International Conference on Content-Based Multimedia Indexing (CBMI), pp. 1–4. IEEE (2019)

    Google Scholar 

  7. Feng, C., Liu, Z., Lin, S., Quek, T.Q.: Attention-based graph convolutional network for recommendation system. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7560–7564. IEEE (2019)

    Google Scholar 

  8. Goldberg, Y., Levy, O.: word2vec explained: deriving Mikolov et al’.s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722 (2014)

  9. Kaklauskas, A., et al.: An affect-based multimodal video recommendation system. Stud. Inf. Control 25(1), 6 (2016)

    Google Scholar 

  10. Kumar, S., De, K., Roy, P.P.: Movie recommendation system using sentiment analysis from microblogging data. IEEE Trans. Comput. Soc. Syst. 7(4), 915–923 (2020)

    Article  Google Scholar 

  11. Lam, X.N., Vu, T., Le, T.D., Duong, A.D.: Addressing cold-start problem in recommendation systems. In: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, pp. 208–211 (2008)

    Google Scholar 

  12. Lavanya, R., Bharathi, B.: Movie recommendation system to solve data sparsity using collaborative filtering approach. Trans. Asian Low-Resour. Lang. Inf. Process. 20(5), 1–14 (2021)

    Article  Google Scholar 

  13. Mondal, P., Chakder, D., Raj, S., Saha, S., Onoe, N.: Graph convolutional neural network for multimodal movie recommendation. In: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, pp. 1633–1640 (2023)

    Google Scholar 

  14. Mondal, P., Kapoor, P., Singh, S., Saha, S., Singh, J.P., Onoe, N.: Task-specific and graph convolutional network based multi-modal movie recommendation system in Indian setting. Procedia Comput. Sci. 222, 591–600 (2023)

    Article  Google Scholar 

  15. Mu, Y., Wu, Y.: Multimodal movie recommendation system using deep learning. Mathematics 11(4), 895 (2023)

    Article  Google Scholar 

  16. Pingali, S., Mondal, P., Chakder, D., Saha, S., Ghosh, A.: Towards developing a multi-modal video recommendation system. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2022)

    Google Scholar 

  17. Pradeep, N., Rao Mangalore, K., Rajpal, B., Prasad, N., Shastri, R.: Content based movie recommendation system. Int. J. Res. Ind. Eng. 9(4), 337–348 (2020)

    Google Scholar 

  18. Raj, S., Mondal, P., Chakder, D., Saha, S., Onoe, N.: A multi-modal multi-task based approach for movie recommendation. In: 2023 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2023)

    Google Scholar 

  19. Reddy, S.R.S., Nalluri, S., Kunisetti, S., Ashok, S., Venkatesh, B.: Content-based movie recommendation system using genre correlation. In: Satapathy, S.C., Bhateja, V., Das, S. (eds.) Smart Intelligent Computing and Applications. SIST, vol. 105, pp. 391–397. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1927-3_42

    Chapter  Google Scholar 

  20. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)

    Google Scholar 

  21. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. Stat 1050(20), 10–48550 (2017)

    Google Scholar 

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Acknowledgement

The authors gratefully acknowledge the support from Sony Research India for conducting this research.

Author information

Authors and Affiliations

  1. National Institue of Technology Patna, Patna, India

    Prabir Mondal

  2. Indian Institute of Technology Patna, Patna, India

    Prabir Mondal, Pulkit Kapoor & Sriparna Saha

  3. Indian Institute of Technology Jodhpur, Jodhpur, India

    Siddharth Singh

  4. Sony Research India, Bengaluru, India

    Naoyuki Onoe & Brijraj Singh

Authors
  1. Prabir Mondal
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  2. Pulkit Kapoor
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  3. Siddharth Singh
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  4. Sriparna Saha
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  5. Naoyuki Onoe
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  6. Brijraj Singh
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Corresponding author

Correspondence to Prabir Mondal .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Mondal, P., Kapoor, P., Singh, S., Saha, S., Onoe, N., Singh, B. (2024). Impulsion of Movie’s Content-Based Factors in Multi-modal Movie Recommendation System. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1969. Springer, Singapore. https://doi.org/10.1007/978-981-99-8184-7_18

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  • DOI: https://doi.org/10.1007/978-981-99-8184-7_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8183-0

  • Online ISBN: 978-981-99-8184-7

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