Research on two popular recommendation algorithms for anime

Research Article
Open access

Research on two popular recommendation algorithms for anime

Zhefei Meng 1*
  • 1 University of Nottingham    
  • *corresponding author xmxhuihui@163.com
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230898
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Anime is a popular eastern art form whose audience is mainly young people. In recent years, many people choose to watch anime on the websites. The recommendation system plays a very important part in improving the user experience and saving time. This paper focuses on two basic recommendation algorithms based on machine learning and deep learning methods, including the content-based and collaborative filtering method. The data used in this paper was downloaded from a public dataset on Kaggle. This paper shows how the two methods perform in the anime dataset and compares the results of the two methods. However, two methods have their own advantages in different conditions. The content-based method works when the user wants some related contents. The collaborative filtering method works better while considering more factors other than contents. These two methods can be combined under a new algorithm to form an even more reliable and reasonable recommendation system in the future studies.

Keywords:

anime, recommendation system, content-based, collaborative filtering, deep learning.

Meng,Z. (2023). Research on two popular recommendation algorithms for anime. Applied and Computational Engineering,6,1430-1438.
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References

[1]. Aggarwal, C., 2018. Recommender System: The Textbook. SPRINGER.

[2]. Rajaraman, A.; Ullman, J.D. (2011). ”Data Mining” (PDF). Mining of Massive Datasets. pp. 1–17.

[3]. Lin, W.-C., Tsai, C.-F. and Chen, H. (2022) “Factors affecting text mining based stock prediction: Text feature representations, Machine Learning Models, and news platforms,” Applied Soft Computing, 130, p. 109673.

[4]. Galluccio, L. et al. (2012) “Graph based K-means clustering,” Signal Processing, 92(9), pp. 1970–1984.

[5]. MacKay, David (2003). ”Chapter 20. An Example Inference Task: Clustering” (PDF). Information Theory, Inference and Learning Algorithms. Cambridge University Press. pp. 284–292.

[6]. van der Maaten, Laurens & Hinton, Geoffrey. (2008). Viualizing data using t-SNE. Journal of Machine Learning Research. 9. 2579-2605.

[7]. Blei, David M.; Ng, Andrew Y.; Jordan, Michael I (January 2003). Lafferty, John (ed.). ”Latent Dirichlet Allocation”. Journal of Machine Learning Research. 3 (4–5): pp. 993–1022.

[8]. Tan, P., Steinbach, M., Karpatne, A. and Kumar, V., 2005. Introduction to data mining. p.500.

[9]. John S. Breese; David Heckerman & Carl Kadie (1998). Empirical analysis of predictive algorithms for collabo- rative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence.

[10]. Afoudi, Y., Lazaar, M. and Al Achhab, M. (2021) “Hybrid recommendation system combined content-based filtering and collaborative prediction using Artificial Neural Network,” Simulation Modelling Practice and Theory, 113, p. 102375.

[11]. Zhang, Y., Liu, Z. and Sang, C. (2021) “Unifying paragraph embeddings and neural collaborative filtering for hybrid recommendation,” Applied Soft Computing, 106, p. 107345.


Cite this article

Meng,Z. (2023). Research on two popular recommendation algorithms for anime. Applied and Computational Engineering,6,1430-1438.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN:978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor:Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN:2755-2721(Print) / 2755-273X(Online)

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References

[1]. Aggarwal, C., 2018. Recommender System: The Textbook. SPRINGER.

[2]. Rajaraman, A.; Ullman, J.D. (2011). ”Data Mining” (PDF). Mining of Massive Datasets. pp. 1–17.

[3]. Lin, W.-C., Tsai, C.-F. and Chen, H. (2022) “Factors affecting text mining based stock prediction: Text feature representations, Machine Learning Models, and news platforms,” Applied Soft Computing, 130, p. 109673.

[4]. Galluccio, L. et al. (2012) “Graph based K-means clustering,” Signal Processing, 92(9), pp. 1970–1984.

[5]. MacKay, David (2003). ”Chapter 20. An Example Inference Task: Clustering” (PDF). Information Theory, Inference and Learning Algorithms. Cambridge University Press. pp. 284–292.

[6]. van der Maaten, Laurens & Hinton, Geoffrey. (2008). Viualizing data using t-SNE. Journal of Machine Learning Research. 9. 2579-2605.

[7]. Blei, David M.; Ng, Andrew Y.; Jordan, Michael I (January 2003). Lafferty, John (ed.). ”Latent Dirichlet Allocation”. Journal of Machine Learning Research. 3 (4–5): pp. 993–1022.

[8]. Tan, P., Steinbach, M., Karpatne, A. and Kumar, V., 2005. Introduction to data mining. p.500.

[9]. John S. Breese; David Heckerman & Carl Kadie (1998). Empirical analysis of predictive algorithms for collabo- rative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence.

[10]. Afoudi, Y., Lazaar, M. and Al Achhab, M. (2021) “Hybrid recommendation system combined content-based filtering and collaborative prediction using Artificial Neural Network,” Simulation Modelling Practice and Theory, 113, p. 102375.

[11]. Zhang, Y., Liu, Z. and Sang, C. (2021) “Unifying paragraph embeddings and neural collaborative filtering for hybrid recommendation,” Applied Soft Computing, 106, p. 107345.