Implementation of Hybrid Method in Tourism Place Recommendation System Based on Image Features

Steven Christ Pinantyo Arwidarasto; Desti Fitriati

doi:10.34288/jri.v5i3.235

Authors

Steven Christ Pinantyo Arwidarasto Universitas Pancasila
Desti Fitriati Universitas Pancasila

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v5i3.235

Keywords:

industrial, image, recommendation, similar, varied

Abstract

In the industrial 4.0 era, there is an explosion of unstructured and structured data that produces broad and varied knowledge information that humans cannot process quickly. This issue makes the existence of recommendation systems meaningful. This system studies the existing information and provides suggestions according to the user's will. In the past, many recommendation systems have focused more on content-based filtering methods where recommendation results are similar based on the features of the Content that match the user's personality. This method limits the variety of information that is relevant to users. In addition, in the context of tourist attractions, many studies have not used image data that can contain many objects in one frame as a determining factor in providing recommendations. Therefore, in this study, the authors propose to add image features as one of the parameters of the recommendation system to determine the impact of using image features on the model performance. The best performance obtained is 0.364 RMSE metric using the Hybrid Image method.

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References

Biswas, P. K., & Liu, S. (2022). A hybrid recommender system for recommending smartphones to prospective customers. Expert Systems with Applications, 208(1). https://doi.org/10.1016/j.eswa.2022.118058

Çakır, M., Öğüdücü, Ş. G., & Tugay, R. (2019). A Deep Hybrid Model for Recommendation Systems. AI*IA 2019: AI*IA 2019 – Advances in Artificial Intelligence, 321–335. https://doi.org/10.1007/978-3-030-35166-3_23

Elsayed, S., Brinkmeyer, L., & Schmidt-Thieme, L. (2022). End-to-end image-based fashion recommendation. Workshop on Recommender Systems in Fashion and Retail, 109–119. https://doi.org/10.1007/978-3-031-22192-7_7

Geetha, G., Safa, M., Fancy, C., & Saranya, D. (2018). A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System. Journal of Physics: Conference Series, 1000(1). https://doi.org/10.1088/1742-6596/1000/1/012101

Hrinchuk, O., Khrulkov, V., Mirvakhabova, L., Orlova, E., & Oseledets, I. (2019). Tensorized Embedding Layers for Efficient Model Compression (pp. 1–13). arXiv. https://doi.org/10.48550/arXiv.1901.10787

Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. https://doi.org/10.1109/MC.2009.263

Lubis, Y. I., Napitupulu, D. J., & Dharma, A. S. (2020). Implementasi Metode Hybrid Filtering (Collaborative dan Content-based) untuk Sistem Rekomendasi Pariwisata. 12th Conference on Information Technology and Electrical Engineering, 28–35. https://citee.ft.ugm.ac.id/2020/download51.php?f=TI-5 - Implementasi Metode Hybrid Filtering.pdf

Mohammadpour, T., Bidgoli, A. M., Enayatifar, R., & Javadi, H. H. S. (2019). Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm. Genomics, 111(6), 1902–1912. https://doi.org/10.1016/J.YGENO.2019.01.001

Ng, Y. K. (2020). CBREC: A book recommendation system for children using the matrix factorisation and content-based filtering approaches. International Journal of Business Intelligence and Data Mining, 16(2), 129–149. https://doi.org/10.1504/IJBIDM.2020.104738

Ortega, F., Lara-Cabrera, R., González-Prieto, Á., & Bobadilla, J. (2021). Providing reliability in recommender systems through Bernoulli matrix factorization. Information Sciences, 553, 110–128. https://doi.org/10.1016/j.ins.2020.12.001

Rian, Z., Christanti, V., & Hendryli, J. (2019). Content-Based Image Retrieval using Convolutional Neural Networks. Proceedings - 2019 IEEE International Conference on Signals and Systems, ICSigSys 2019, 1–7. https://doi.org/10.1109/ICSIGSYS.2019.8811089

Walek, B., & Fojtik, V. (2020). A hybrid recommender system for recommending relevant movies using an expert system. Expert Systems with Applications, 158, 113452. https://doi.org/10.1016/J.ESWA.2020.113452

Wu, Z., Tian, H., Zhu, X., & Wang, S. (2018). Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality (pp. 1–12). arXiv. https://doi.org/10.1007/978-3-319-93803-5_11

Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into Imaging, 9, 611–629. https://doi.org/10.1007/s13244-018-0639

Zhang, Y. (2022). An Introduction to Matrix factorization and Factorization Machines in Recommendation System, and Beyond (pp. 1–15). arXiv. https://doi.org/10.48550/arXiv.2203.11026