Chatbot-Based Movie Recommender System Using POS Tagging
- Muhammad Alwi Nugraha Telkom University, Bandung, Indonesia
-
Z K A Baizal
*
Telkom University, Bandung,
Indonesia
- Donni Richasdy Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
The movie recommender system is a technology designed to make it easier for users to provide recommendations quickly and among the many pieces of information. Because the number of movies is huge, it causes a person to be confused in determining the choice of the movie to watch. Many movie recommending systems have been developed, but users cannot interact intensively. Based on these problems, we developed a chatbot-based conversational recommender system, which can interact intensively with the system. The developed chatbot uses normal language handling to permit the framework to comprehend what the user enters as natural language. POS Tagging is used to find tags in the form of movie titles with patterns in the POS Tagging model. However, the algorithm of those used on POS Tagging does not pay attention to the sentence entity, so the predicted title must correspond to the provisions of POS Tagging. Multinomial Naive Bayes looks for similarities of user input to datasets on intents. The dataset with the highest probability value or almost equal to the sentence entered by the user can be used as a response to user input. The test results of the chatbot application showed that the match rate between response and user input was 89.1%. Thus, the developed chatbot can be used well to provide practical and interactive movie recommendations.
Downloads
References
P. Vilakone, D.-S. Park, K. Xinchang, and F. Hao, “An Efficient movie recommendation algorithm based on improved k-clique,” Human-centric Computing and Information Sciences, vol. 8, no. 1, p. 38, Dec. 2018, doi: 10.1186/s13673-018-0161-6.
S. Khafid and I. Azis, “Perbedaan Disney+ Dan Netflix, Dari Biaya Berlangganan Hingga Fitur,” https://tirto.id/perbedaan-disney-dan-netflix-dari-biaya-berlangganan-hingga-fitur-f4CS., Sep. 17, 2020.
Z. K. A. Baizal, A. Iskandar, and E. Nasution, “Ontology-based recommendation involving consumer product reviews,” in 2016 4th International Conference on Information and Communication Technology (ICoICT), May 2016, pp. 1–6. doi: 10.1109/ICoICT.2016.7571890.
Z. K. A. Baizal, D. H. Widyantoro, and N. U. Maulidevi, “Computational model for generating interactions in conversational recommender system based on product functional requirements,” Data & Knowledge Engineering, vol. 128, p. 101813, Jul. 2020, doi: 10.1016/j.datak.2020.101813.
M. Polato and F. Aiolli, “Boolean kernels for collaborative filtering in top-N item recommendation,” Neurocomputing, vol. 286, pp. 214–225, Apr. 2018, doi: 10.1016/j.neucom.2018.01.057.
S. Reddy, S. Nalluri, S. Kunisetti, S. Ashok, and B. Venkatesh, “Content-Based Movie Recommendation System Using Genre Correlation,” 2019, pp. 391–397. doi: 10.1007/978-981-13-1927-3_42.
A. Augello, G. Saccone, S. Gaglio, and G. Pilato, “Humorist Bot: Bringing Computational Humour in a Chat-Bot System,” in 2008 International Conference on Complex, Intelligent and Software Intensive Systems, 2008, pp. 703–708. doi: 10.1109/CISIS.2008.117.
E. M. Sibarani, Mhd. Nadial, E. Panggabean, and S. Meryana, “A Study of Parsing Process on Natural Language Processing in Bahasa Indonesia,” in 2013 IEEE 16th International Conference on Computational Science and Engineering, Dec. 2013, pp. 309–316. doi: 10.1109/CSE.2013.56.
S. Holmes, A. Moorhead, R. Bond, H. Zheng, V. Coates, and M. Mctear, “Usability testing of a healthcare chatbot: Can we use conventional methods to assess conversational user interfaces?,” in Proceedings of the 31st European Conference on Cognitive Ergonomics, Sep. 2019, pp. 207–214. doi: 10.1145/3335082.3335094.
S. K. Mishra, B. Dhirendra, and N. Mishra, “Dr.Vdoc: A Medical Chatbot that Acts as a virtual Doctor,” Journal of Medical Science and Technoslogy, vol. 6, no. 3, 2017.
L. Cui, S. Huang, F. Wei, C. Tan, C. Duan, and M. Zhou, “SuperAgent: A Customer Service Chatbot for E-commerce Websites,” in Proceedings of ACL 2017, System Demonstrations, 2017, pp. 97–102. doi: 10.18653/v1/P17-4017.
D. P. Jati and M. R. Maarif, “The Development Of Chatbot Application On Line Messaging Platform For Customer Service In Jogja Sewa Kamera,” Compiler, vol. 7, no. 2, p. 91, Oct. 2018, doi: 10.28989/compiler.v7i2.368.
D. Munandar, E. Suryawati, D. Riswantini, A. F. Abka, R. Wijayanti, and A. Arisal, “POS-tagging for non-english tweets: An automatic approach: (Study in Bahasa Indonesia),” in 2017 1st International Conference on Informatics and Computational Sciences (ICICoS), Nov. 2017, pp. 219–224. doi: 10.1109/ICICOS.2017.8276365.
S. Warjri, P. Pakray, S. Lyngdoh, and A. K. Maji, “Identification of POS Tag for Khasi Language based on Hidden Markov Model POS Tagger,” Computación y Sistemas, vol. 23, no. 3, Oct. 2019, doi: 10.13053/cys-23-3-3248.
A. Kaushal, L. White, M. Innes, and R. Kumar, “WordTokenizers.jl: Basic tools for tokenizing natural language in Julia,” Journal of Open Source Software, vol. 5, no. 46, p. 1956, Feb. 2020, doi: 10.21105/joss.01956.
D. Griol and Z. Callejas, “A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents,” Computational Intelligence and Neuroscience, vol. 2016, pp. 1–11, 2016, doi: 10.1155/2016/8402127.
S. Deepu, R. Pethuru, and S. Rajaraajeswari, “A Framework for Text Analytics using the Bag of Words (BoW) Model for Prediction,” International Journal of Advanced Networking & Applications (IJANA), pp. 320–323, 2017.
A. S. Budi, “Klasifikasi Opini Green And Clean Kabupaten Lamongan Menggunakan Algoritma Multinomial Naive Bayes,” Joutica, vol. 3, no. 1, p. 125, Apr. 2018, doi: 10.30736/jti.v3i1.198.
D. H. Kalokasari, I. M. Shofi, and A. H. Setyaningrum, “Implementasi Algoritma Multinomial Naive Bayes Classifier Pada Sistem Klasifikasi Surat Keluar (Studi Kasus : Diskominfo Kabupaten Tangerang),” Jurnal Teknik Informatika, vol. 10, no. 2, Oct. 2017, doi: 10.15408/jti.v10i2.6199.
A. Yajnik, “General Regression Neural Network Based PoS Tagging for Nepali Text,” in Computer Science & Information Technology, Apr. 2018, pp. 35–40. doi: 10.5121/csit.2018.80603.
M. Chiny, M. Chihab, O. Bencharef, and Y. Chihab, “Netflix Recommendation System based on TF-IDF and Cosine Similarity Algorithms." ,” 2021. doi: 10.5220/001072750000310.
R. A. Sekarwati, A. Sururi, R. Rakhmat, M. Arifin, and A. Wibowo, “Survey of Chatbot Testing Methods on Social Media to Measure Accuracy,” Sisfotenika, vol. 11, no. 2, p. 172, Jul. 2021, doi: 10.30700/jst.v11i2.1099.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Chatbot-Based Movie Recommender System Using POS Tagging
Pages: 624-630
Copyright (c) 2022 Muhammad Alwi Nugraha, Z K A Baizal, Donni Richasdy
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
