Conversational Recommender Systems Based on Mobile Chatbot for Culinary

Ghazi Ahmad Fadhlullah, Z K Abdurahman Baizal, Nurul Ikhsan

Abstract


Culinary places are one of the tourists attractions in a place that makes many new culinary places appear. Various types of new foods and drinks are present along with the addition of culinary places. However, this can be a problem when tourists visit a new destination and look for a culinary place that suits their tastes. In the previous research on the recommendation system for culinary places, users only gave their preferences at the beginning of the recommendation process and ignored the operating hours of the recommended culinary places. Therefore, we developed a recommendation system for culinary places by utilizing the context of time from users. We use the Conversational Recommender System on the chatbot platform with the Personalized PageRank algorithm to generate recommendations. In addition, we also use the explanation facility to get an explanation of the recommended items. We use questionnaires and the accuracy of recommendation results to measure user satisfaction and system performance. The evaluation results with a questionnaire involving 81 respondents concluded that users are pretty satisfied with the system built. However, testing with accuracy yields a value of 40%, proving that the system performance is low

Keywords


Culinary; Conversational Recommender Systems; Chatbot; PageRank Personalized; Explanation

Full Text:

PDF

References


D. T. Untari, “The development strategy of betawi eco-culinary tourism as a potential business in DKI Jakarta, Indonesia,†African Journal of Hospitality, Tourism and Leisure, vol. 2019, no. Special Issue, pp. 1–9, 2019.

R. Komaladewi, “The Representation of Culinary Experience as the Future of Indonesian Tourism Cases in Bandung City, West Java,†International Journal of Business and Economic Affairs, vol. 2, no. 5, pp. 268–275, 2017, doi: 10.24088/ijbea-2017-25001.

E. Riswanto, B. Robi’In, and Suparyanto, “Mobile Recommendation System for Culinary Tourism Destination using KNN (K-nearest neighbor),†in Journal of Physics: Conference Series, 2019, vol. 1201, no. 1. doi: 10.1088/1742-6596/1201/1/012039.

S. Malathy and P. Kantha, “Application of mobile technologies to libraries,†DESIDOC Journal of Library and Information Technology, vol. 33, no. 5, pp. 361–366, 2013, doi: 10.14429/djlit.33.5098.

Maeve. Duggan, “Cell Phone Activities 2013.†https://www.pewresearch.org/internet/2013/09/19/cell-phone-activities-2013/ (accessed Aug. 11, 2021).

F. Ricci, L. Rokach, and B. Shapira, “Introduction to Recommender Systems Handbook,†in Recommender Systems Handbook, Springer, Boston, MA, 2011, pp. 1–35. doi: 10.1007/978-0-387-85820-3_1.

C. C. Aggarwal, Recommender Systems, vol. 1. Cham: Springer International Publishing, 2016. doi: 10.1007/978-3-319-29659-3.

R. K. Dewi, M. T. Ananta, and L. Fanani, “The Development of Mobile Culinary Recommendation System Based on Group Decision Support System,†Int. J. Interact. Mob. Technol., vol. 12, no. 3, pp. 209–216, 2018.

F. Effendy, B. Nuqoba, and Taufik, “Culinary recommendation application based on user preferences using fuzzy topsis,†IIUM Engineering Journal, vol. 20, no. 2, pp. 163–175, 2019, doi: 10.31436/iiumej.v20i2.1023.

A. Pinandito, M. T. Ananta, K. C. Brata, and L. Fanani, “Alternatives weighting in analytic hierarchy process of mobile culinary recommendation system using fuzzy,†ARPN Journal of Engineering and Applied Science, vol. 10, no. 19, pp. 8791–8798, 2015.

A. A. Fakhri, Z. K. A. Baizal, and E. B. Setiawan, “Restaurant Recommender System Using User-Based Collaborative Filtering Approach: A Case Study at Bandung Raya Region,†Journal of Physics: Conference Series, vol. 1192, no. 1, 2019, doi: 10.1088/1742-6596/1192/1/012023.

A. Pinandito, C. Putri, and R. Kartika, “Culinary Recommendation Systems Using Analytical Hierarchy Process on Google Android Platform,†in International Conference on Engineering and Information Technology (ICEIT), 2015, pp. 138–147.

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.

Z. K. A. Baizal, D. Tarwidi, and B. Wijaya, “Tourism Destination Recommendation Using Ontology-based Conversational Recommender System,†International Jounal of Computing and Digital System, vol. 1, no. 1, 2021.

F. Narducci, M. de Gemmis, P. Lops, and G. Semeraro, “Improving the User Experience with a Conversational Recommender System,†Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11298 LNAI, pp. 528–538, Nov. 2018, doi: 10.1007/978-3-030-03840-3_39.

F. Narducci, P. Basile, M. de Gemmis, P. Lops, and G. Semeraro, “An investigation on the user interaction modes of conversational recommender systems for the music domain,†User Modeling and User-Adapted Interaction 2019 30:2, vol. 30, no. 2, pp. 251–284, Nov. 2019, doi: 10.1007/S11257-019-09250-7.

N. Sardella, C. Biancalana, A. Micarelli, and G. Sansonetti, “An Approach to Conversational Recommendation of Restaurants,†Communications in Computer and Information Science, vol. 1034, pp. 123–130, Jul. 2019, doi: 10.1007/978-3-030-23525-3_16.

Z. K. Abdurahman Baizal, Y. R. Murti, and Adiwijaya, “Evaluating functional requirements-based compound critiquing on conversational recommender system,†2017 5th International Conference on Information and Communication Technology, ICoIC7 2017, pp. 1–6, Oct. 2017, doi: 10.1109/ICOICT.2017.8074656.

P. Christen, “A comparison of personal name matching: Techniques and practical issues,†Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 290–294, 2006, doi: 10.1109/ICDMW.2006.2.

B. M. Changmai, D. Nagaraju, D. P. Mohanty, K. Singh, K. Bansal, and S. Moharana, “On-Device User Intent Prediction for Context and Sequence Aware Recommendation,†Sep. 2019, Accessed: Aug. 12, 2021. [Online]. Available: https://arxiv.org/abs/1909.12756v1

N. Tintarev and J. Masthoff, “Evaluating the effectiveness of explanations for recommender systems,†User Modeling and User-Adapted Interaction 2012 22:4, vol. 22, no. 4, pp. 399–439, Feb. 2012, doi: 10.1007/S11257-011-9117-5.

C. Musto, F. Narducci, P. Lops, M. de Gemmis, and G. Semeraro, “ExpLOD: A framework for explaining recommendations based on the linked open data cloud,†in RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, Sep. 2016, pp. 151–154. doi: 10.1145/2959100.2959173.

A. Iovine, F. Narducci, and G. Semeraro, “Conversational Recommender Systems and natural language:: A study through the ConveRSE framework,†Decision Support Systems, vol. 131, no. January, 2020, doi: 10.1016/j.dss.2020.113250.

F. Hernández del Olmo and E. Gaudioso, “Evaluation of recommender systems: A new approach,†Expert Systems with Applications, vol. 35, no. 3, pp. 790–804, Oct. 2008, doi: 10.1016/J.ESWA.2007.07.047.




DOI: https://doi.org/10.30865/mib.v5i4.3242

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


JURNAL MEDIA INFORMATIKA BUDIDARMA
Universitas Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.