TOPIC MODELLING-ENHANCED RECOMMENDER SYSTEMS IN TOURISM

Faculty of Business Economics Bijeljina, University of East Sarajevo, Republic of Srpska, BiH
Bosnia and Herzegovina

http://orcid.org/0000-0002-6028-1153
Faculty of Economics in Subotica, University of Novi Sad, Subotica, Serbia
Serbia


Abstract

The development of information technologies has enabled the distribution and availability of information globally, which is reflected in all social spheres, especially tourism. A vast amount of information about tourist offers is available to travelers daily, often overwhelming users and leading to information overload, making it significantly difficult to find the offer that best matches their preferences. Recommender systems, an artificial intelligence-based technology, can help users make decisions by guiding them in a personalized way. By matching user preferences, they offer timely and relevant suggestions for destinations or tours and save time and cognitive load during the selection process. Recommender systems can be beneficial for tourists and tourist organizations, but their implementation is not straightforward. Numerous different challenges can occur. Some of them are caused by multipolar nature of tourism like language and spatial barriers, seasonality and preference variability.   Others can be common recommender system problems such as cold-start, sparsity, explainability, accuracy and others. Topic modeling can help address these challenges by extracting topics from textual data, which provide valuable insights and enhance effectiveness of recommender systems. This paper aims to describe the development challenges in tourist recommender systems and present how topic modeling can address these issues, benefiting both tourists and businesses in the tourism sector.

Keywords



Full Text


References


Abdelrazek, A., Eid, Y., Gawish, E., Medhat, W., & Hassan, A. (2023). Topic modeling algorithms and applications: A survey. Information Systems, 112, 102131. https://doi.org/10.1016/j.is.2022.102131

Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., & Steggles, P. (1999). Towards a Better Understanding of Context and Context-Awareness (pp. 304–307). https://doi.org/10.1007/3-540-48157-5_29

Aggarwal, C. C. (2016). Recommender Systems. Springer International Publishing. https://doi.org/10.1007/978-3-319-29659-3

Barde, B. V., & Bainwad, A. M. (2017). An overview of topic modeling methods and tools. 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), 745–750. https://doi.org/10.1109/ICCONS.2017.8250563

Burke, R. (2000). Knowledge-based recommender systems. Encyclopedia of Library and Information Systems.

Çano, E., & Morisio, M. (2017). Hybrid recommender systems: A systematic literature review. Intelligent Data Analysis, 21(6), 1487–1524. https://doi.org/10.3233/IDA-163209

Chowdhary, K. R. (2020). Fundamentals of Artificial Intelligence. Springer India. https://doi.org/10.1007/978-81-322-3972-7

Dareddy, M. R. (2016). Challenges in Recommender Systems for Tourism. CEUR Workshop Proceedings, 11–15.

De Croon, R., Van Houdt, L., Htun, N. N., Štiglic, G., Vanden Abeele, V., & Verbert, K. (2021). Health Recommender Systems: Systematic Review. Journal of Medical Internet Research, 23(6), e18035. https://doi.org/10.2196/18035

Grljević, O., & Marić, M. (2024). A Comprehensive Analysis of Online Reviews in the Srem Region through Topic Modeling (pp. 291–311). https://doi.org/10.31410/tmt.2023-2024.291

Hamid, R. A., Albahri, A. S., Alwan, J. K., Al-qaysi, Z. T., Albahri, O. S., Zaidan, A. A., Alnoor, A., Alamoodi, A. H., & Zaidan, B. B. (2021). How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management. Computer Science Review, 39, 100337. https://doi.org/10.1016/j.cosrev.2020.100337

Katsumi, H., Yamada, W., & Ochiai, K. (2020). Generic POI recommendation. Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers, 46–49. https://doi.org/10.1145/3410530.3414421

Kherwa, P., & Bansal, P. (2018). Topic Modeling: A Comprehensive Review. ICST Transactions on Scalable Information Systems, 0(0), 159623. https://doi.org/10.4108/eai.13-7-2018.159623

Kolahkaj, M., Harounabadi, A., Nikravanshalmani, A., & Chinipardaz, R. (2020). A hybrid context-aware approach for e-tourism package recommendation based on asymmetric similarity measurement and sequential pattern mining. Electronic Commerce Research and Applications, 42, 100978. https://doi.org/10.1016/j.elerap.2020.100978

Kuklina, V., Ruposov, V., Kuklina, M., Rogov, V., & Bayaskalanova, T. (2017). Multi-polar trajectories of tourism development within Russian Arctic. Proceedings of the International Conference on Trends of Technologies and Innovations in Economic and Social Studies 2017. https://doi.org/10.2991/ttiess-17.2017.63

Kumar, N., & Hanji, B. R. (2024). Combined sentiment score and star rating analysis of travel destination prediction based on user preference using morphological linear neural network model with correlated topic modelling approach. Multimedia Tools and Applications, 83(22), 61347–61378. https://doi.org/10.1007/s11042-023-17995-y

Laureate, C. D. P., Buntine, W., & Linger, H. (2023). A systematic review of the use of topic models for short text social media analysis. Artificial Intelligence Review, 56(12), 14223–14255. https://doi.org/10.1007/s10462-023-10471-x

Liu, G. (2022). Research on Personalized Minority Tourist Route Recommendation Algorithm Based on Deep Learning. Scientific Programming, 2022, 1–9. https://doi.org/10.1155/2022/8063652

Liu, L., Tang, L., Dong, W., Yao, S., & Zhou, W. (2016). An overview of topic modeling and its current applications in bioinformatics. SpringerPlus, 5(1), 1608. https://doi.org/10.1186/s40064-016-3252-8

Lops, P., de Gemmis, M., & Semeraro, G. (2011). Content-based Recommender Systems: State of the Art and Trends. In Recommender Systems Handbook (pp. 73–105). Springer US. https://doi.org/10.1007/978-0-387-85820-3_3

Luyi Zou, & William Wei Song. (2016). LDA-TM: A two-step approach to Twitter topic data clustering. 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 342–347. https://doi.org/10.1109/ICCCBDA.2016.7529581

Lynn, N. D., & Emanuel, A. W. R. (2021). A review on Recommender Systems for course selection in higher education. IOP Conference Series: Materials Science and Engineering, 1098(3), 032039. https://doi.org/10.1088/1757-899X/1098/3/032039

Massimo, D., & Ricci, F. (2022). Building effective recommender systems for tourists. AI Magazine, 43(2), 209–224. https://doi.org/10.1002/aaai.12057

Mazarura, J., & de Waal, A. (2016). A comparison of the performance of latent Dirichlet allocation and the Dirichlet multinomial mixture model on short text. 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 1–6. https://doi.org/10.1109/RoboMech.2016.7813155

Mishra, R. K., Jothi, J. A. A., Urolagin, S., & Irani, K. (2023). Knowledge based topic retrieval for recommendations and tourism promotions. International Journal of Information Management Data Insights, 3(1), 100145. https://doi.org/10.1016/j.jjimei.2022.100145

Noorian, A. (2024). A BERT-Based Sequential POI Recommender system in Social Media. Computer Standards & Interfaces, 87, 103766. https://doi.org/10.1016/j.csi.2023.103766

Noorian Avval, A. A., & Harounabadi, A. (2023). A hybrid recommender system using topic modeling and prefixspan algorithm in social media. Complex & Intelligent Systems, 9(4), 4457–4482. https://doi.org/10.1007/s40747-022-00958-5

Rossetti, M., Stella, F., & Zanker, M. (2016). Analyzing user reviews in tourism with topic models. Information Technology & Tourism, 16(1), 5–21. https://doi.org/10.1007/s40558-015-0035-y

Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1), 59. https://doi.org/10.1186/s40537-022-00592-5

Sarkar, J. L., Majumder, A., Panigrahi, C. R., Roy, S., & Pati, B. (2023). Tourism recommendation system: a survey and future research directions. Multimedia Tools and Applications, 82(6), 8983–9027. https://doi.org/10.1007/s11042-022-12167-w

Shafqat, W., & Byun, Y.-C. (2019). A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis. Sustainability, 12(1), 320. https://doi.org/10.3390/su12010320

Sharaf, M., Hemdan, E. E.-D., El-Sayed, A., & El-Bahnasawy, N. A. (2022). A survey on recommendation systems for financial services. Multimedia Tools and Applications, 81(12), 16761–16781. https://doi.org/10.1007/s11042-022-12564-1

Sieg, A., Bamshad, M., & Robin, D. B. (2007). Learning ontology-based user profiles: A semantic approach to personalized web search. IEEE Intell. Informatics, 8(1).

Singh, M. (2020). Scalability and sparsity issues in recommender datasets: a survey. Knowledge and Information Systems, 62(1), 1–43. https://doi.org/10.1007/s10115-018-1254-2

Solano-Barliza, A., Arregocés-Julio, I., Aarón-Gonzalvez, M., Zamora-Musa, R., De-La-Hoz-Franco, E., Escorcia-Gutierrez, J., & Acosta-Coll, M. (2024). Recommender systems applied to the tourism industry: a literature review. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2367088

Tang, J., Meng, Z., Nguyen, X., Mei, Q., & Zhang. (2014). Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis. Proceedings of the 31 St International Conference on Machine Learning, 190–198.

Tussyadiah, I., & Miller, G. (2019). Perceived Impacts of Artificial Intelligence and Responses to Positive Behaviour Change Intervention. In Information and Communication Technologies in Tourism 2019 (pp. 359–370). Springer International Publishing. https://doi.org/10.1007/978-3-030-05940-8_28

Vargas-Calderón, V., Moros Ochoa, A., Castro Nieto, G. Y., & Camargo, J. E. (2021). Machine learning for assessing quality of service in the hospitality sector based on customer reviews. Information Technology & Tourism, 23(3), 351–379. https://doi.org/10.1007/s40558-021-00207-4

Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems, 94, 101582. https://doi.org/10.1016/j.is.2020.101582

Yan, X., Guo, J., Lan, Y., & Cheng, X. (2013). A biterm topic model for short texts. Proceedings of the 22nd International Conference on World Wide Web, 1445–1456. https://doi.org/10.1145/2488388.2488514

Zhang, Z., Lin, H., Liu, K., Wu, D., Zhang, G., & Lu, J. (2013). A hybrid fuzzy-based personalized recommender system for telecom products/services. Information Sciences, 235, 117–129. https://doi.org/10.1016/j.ins.2013.01.025

Zhou, Z. (2022). Critical shifts in the global tourism industry: perspectives from Africa. GeoJournal, 87(2), 1245–1264. https://doi.org/10.1007/s10708-020-10297-y