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Neural model based collaborative filtering for movie recommendation system

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Abstract

Due to the availability of enormous number of products of same domain is increasing day by day the possibility of getting your desire product is getting less. Therefore, a recommendation not only helps you find the best probable product according to your preference but also increases the efficacy to find the product for you in less time interval. Artificial neural network has been producing a tremendous result in the practical solutions like image classification, speech recognitions and various AI problems. The use of neural network to build recommendations system can also be used as an auto encoder in various sector. The neural network contains many layers and each layer contains many perceptron which holds the weight. While the network gets trained, the weights of each perception are optimized and get adjusted. Building a simple neural network model for predicting recommendations with high accuracy is the objective of this work. The dataset used in this recommendations model is contributed by the Movie-lens archive. Manipulating the data into a right form and format is the most important part of the model. The whole work is performed, experimented and evaluated in python as it consists of many predefined useful libraries. The result of the recommender model is evaluated by finding the Hit-Ratio. The Hit-ratio obtained by this model is 87.

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References

  1. Xiangnan H, Lizi L, Hanwang Z, Liqiang N, Xia H, Tat SC (2017) Neural collaborative filtering. arXiv:1708.05031v2

  2. Ting B, Ji-Rong W, Jun Z, Wayne XZ (2017) A neural collaborative filtering model with interaction-based neighbourhood. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 1979–1982. https://doi.org/10.1145/3132847.3133083

  3. Liu YS (2018) A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Min Anal 1(3):211–221

    Article  Google Scholar 

  4. Chen WF (2019) Joint neural collaborative filtering for recommender systems. ACM Trans Inf Syst (TOIS) 37:1–30

    Google Scholar 

  5. Li EC (2019) Learning binary codes with neural collaborative filtering for efficient recommendation systems. Knowl-Based Syst 172:64–75

    Article  Google Scholar 

  6. Gao XW (2019) Context-aware QoS prediction with neural collaborative filtering for Internet-of-Things services. IEEE Internet Things J 7(5):4532–4542

    Article  Google Scholar 

  7. Krishnan A, Ashish S, Hari S(2018) An adversarial approach to improve long-tail performance in neural collaborative filtering. In: Proceedings of the 27th ACM International Conference on information and knowledge management, pp 1491–1494. https://doi.org/10.1145/3269206.3269264

  8. Khaki LW (2020) Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach. PLoS ONE 15(5):e0233382

    Article  Google Scholar 

  9. Li ZY, Bin G, Yan L, Yao J, Yi O, and Zhiwen Y (2018) Commercial site recommendation based on neural collaborative filtering. In: UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on pervasive and ubiquitous computing and wearable computers, pp 138–141. https://doi.org/10.1145/3267305.3267592

  10. Liu YS (2021) A neural collaborative filtering method for identifying miRNA-disease associations. Neurocomputing 422:176–185

    Article  Google Scholar 

  11. Ren Z, Xia N, Andrew SL, Huzefa R (2019) Grade prediction with neural collaborative filtering. In: 2019 IEEE International Conference on data science and advanced analytics (DSAA), pp 1–10. https://doi.org/10.1109/DSAA.2019.00014

  12. Islam RK (2021) Debiasing career recommendations with neural fair collaborative filtering. UMBC Faculty Collection

    Book  Google Scholar 

  13. Girsang AW (2021) Neural collaborative for music recommendation system. IOP Conf Ser Mater Sci Eng 1071(1):012021

    Article  Google Scholar 

  14. Bobadilla JS (2020) Deep learning architecture for collaborative filtering recommender systems. Appl Sci 10(7):2441

    Article  Google Scholar 

  15. Chen LA, Angyu Z, Yinglan F, Fenfang X, Zibin Z (2018) Software service recommendation base on collaborative filtering neural network model. In: International Conference on service-oriented computing, Springer, Cham, pp 388–403. https://doi.org/10.1007/978-3-030-03596-9_28

  16. Tran, Phong H, Nguyen HT, Ngoc-Thao N (2020) A hybrid approach for neural collaborative filtering. In: 2020 7th NAFOSTED Conference on information and computer science (NICS), IEEE, p 368–373. https://doi.org/10.1109/NICS51282.2020.9335910

  17. Li AB (2021) Hyperbolic neural collaborative recommender. arXiv preprint arXiv:2104.07414

  18. Zheng Y, Bangsheng T, Wenkui D, Hanning Z (2016) A neural autoregressive approach to collaborative filtering. In: International Conference on machine learning, PMLR, pp 764–773. http://proceedings.mlr.press/v48/zheng16.pdf

  19. He GD (2021) Dual-embedding based neural collaborative filtering for recommender systems. arXiv preprint arXiv: arXiv:2102-02549

  20. Faizin AIS (2020) Product recommender system using neural collaborative filtering for marketplace in Indonesia. IOP Conf Ser Mater Sci Eng 909(1):012072

    Article  Google Scholar 

  21. Zeng YZQ (2019) Trust-based neural collaborative filtering. J Phys Conf Ser 122(1):012051

    Article  Google Scholar 

  22. Grouplens M (2021) Movielens. https://grouplens.org/datasets/movielens/25m/. Accessed 01 Sep 2020

  23. Yehuda K, Bell R (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  24. Agarap AF (2018) Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375

  25. Kouretas I, Vassilis P (2019) Simplified hardware implementation of the softmax activation function. In: 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST), IEEE, p 1–4

  26. Areej ADQHAD (2020) Hit ratio: an evaluation metric for hashtag recommendation. arXiv:2010.01258

  27. Huang Z, Yu C, Ni J, Liu H, Zeng C, Tang Y (2019) An efficient hybrid recommendation model with deep neural networks. IEEE Access 7:137900–137912

    Article  Google Scholar 

  28. Afoudi Y, Lazaar M, Al Achhab M (2021) Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network. Simul Model Pract Theory 113:102375

    Article  Google Scholar 

  29. Riaz Y, Deep U, Darad A (2021) Collaborative filtering based movie recommendation system (No. 5697). EasyChair

    Google Scholar 

  30. Thakker U, Patel R, Shah M (2021) A comprehensive analysis on movie recommendation system employing collaborative filtering. Multimed Tools Appl 80:1–26

    Article  Google Scholar 

  31. Panda SK, Bhoi SK, Singh M (2020) A collaborative filtering recommendation algorithm based on normalization approach. J Ambient Intell Humaniz Comput 11:1–23

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Parala Maharaja Engineering College (Govt.), Berhampur, India

    Kalyan Kumar Jena, Sourav Kumar Bhoi & Soumya Ranjan Jena

  2. Department of Basic Science (Mathematics), Parala Maharaja Engineering College (Govt.), Berhampur, India

    Chittaranjan Mallick

  3. Department of Computer Science and Engineering, GIET University, Gunupur, India

    Raghvendra Kumar

  4. Faculty of Information Technology, University of Technology-Logistic of Public Security, Hanoi, Vietnam

    Hoang Viet Long

  5. Faculty of Natural Sciences, Hanoi Metropolitan University, Hanoi, Vietnam

    Nguyen Thi Kim Son

Authors
  1. Kalyan Kumar Jena
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  2. Sourav Kumar Bhoi
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  3. Chittaranjan Mallick
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  4. Soumya Ranjan Jena
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  5. Raghvendra Kumar
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  6. Hoang Viet Long
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  7. Nguyen Thi Kim Son
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Correspondence to Nguyen Thi Kim Son.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Jena, K.K., Bhoi, S.K., Mallick, C. et al. Neural model based collaborative filtering for movie recommendation system. Int. j. inf. tecnol. 14, 2067–2077 (2022). https://doi.org/10.1007/s41870-022-00858-4

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  • DOI: https://doi.org/10.1007/s41870-022-00858-4

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