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카드 데이터 기반 심층 관광 추천 연구

Card Transaction Data-based Deep Tourism Recommendation Study

  • 홍민성 (경희대학교 스마트관광연구소) ;
  • 김태경 (광운대학교 경영학부) ;
  • 정남호 (경희대학교 스마트관광원)
  • Hong, Minsung (Smart Tourism Research Center, Kyung-Hee University) ;
  • Kim, Taekyung (Division of Business Administration, Kwangwoon University) ;
  • Chung, Namho (Smart Tourism Education Platform, Kyung-Hee University)
  • 투고 : 2022.04.27
  • 심사 : 2022.06.02
  • 발행 : 2022.06.30

초록

관광산업에서 발생하는 방대한 카드 거래 데이터는 관광객의 소비 행태와 패턴을 암시하는 중요한 자원이 되었다. 거래 데이터에 기반을 둔 스마트 서비스 시스템을 개발하는 것은 관광산업과 지식관리시스템 개발자들의 주요한 목표들 중 하나이다. 그러나 기존 추천 기법의 근간이 되어 온 평점을 활용하기 어렵다는 점은 시스템 설계자들이 학습 과정을 평가하기 어렵게 한다. 또한 시간적, 공간적, 인구통계학적 정보와 같이 추천 성과를 높일 수 있는 보조 요소들을 적절히 활용하는 방법도 어려운 상황이다. 이러한 문제들에 대하여 본 논문은 카드 거래 데이터를 기반으로 관광 서비스를 추천하는 새로운 방식인 CTDDTR을 제안한다. 먼저 Doc2Vec를 이용하여 시간성 선호도를 임베딩하여 관광객 그룹과 서비스 벡터로 데이터를 표현하였다. 다음 단계로 딥러닝 기술 중 하나인 다중 계층 퍼셉트론을 도입하여 얻어진 벡터와 관광 RDF로부터 도출한 보조 요소를 통합하여 심층 추천 모듈을 구성하였다. 추가로, 지식경영 분야의 RFM 분석 기법을 심층 추천 모듈에 도입하여 심층 신경망을 학습하는데 사용되는 평점을 생성함으로써 평점 부재 문제에 대응하였다. 제안한 CTDDTR의 추천 성능을 평가하기 위해 제주도에서 8년 동안 발생한 카드 거래 데이터를 사용하였고, 제안된 방법의 우수한 추천 성능과 보조 요소의 효과를 증명하였다.

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

키워드

과제정보

이 논문은 2019년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2019S1A3A2098438) 이 논문은 2022년 광운대학교 교내학술연구비 지원에 의해 연구되었음(2022-0142)

참고문헌

  1. 김태경, 구철모, 정남호 (2021). 스마트관광에서 웹 메타 데이터의 의미와 활용 방안. 관광연구, 35(3), 5-21.
  2. 문현실, 임진혁, 김도연, 조윤호 (2020). 시각 정보를 활용한 딥러닝 기반 추천 시스템. 지식경영연구, 21(3), 27-44. https://doi.org/10.15813/KMR.2020.21.3.002
  3. 정영진, 조윤호 (2017). 온라인 구매 행태를 고려한 토픽 모델링 기반 도서 추천. 지식경영연구, 18(4), 97-118. https://doi.org/10.15813/kmr.2017.18.4.004
  4. 최영제, 문현실, 조윤호 (2020). 트랜잭션 기반 추천 시스템에서 워드 임베딩을 통한 도메인 지식 반영. 지식경영연구, 21(1), 117-136. https://doi.org/10.15813/KMR.2020.21.1.007
  5. Al-Ghossein, M., Abdessalem, T., & Barre, A. (2018). Cross-domain recommendation in the hotel sector. In Proceedings of the Workshop on Recommenders in Tourism, Vancouver, Canada, 1-6.
  6. Awangga, R. M., Pane, S. F., & Wijayanti, D. A. (2019). GURILEM: A novel design of customer rating model using K-Means and RFM. EMITTER International Journal of Engineering Technology, 7(2), 404-422.
  7. Baek, J. W., & Chung, K. Y. (2020). Multimedia recommendation using Word2Vec-based social relationship mining. Multimedia Tools and Applications, 80(26), 1-17.
  8. Cai, G., Lee, K., & Lee, I. (2018). Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos. Expert Systems with Applications, 94, 32-40. https://doi.org/10.1016/j.eswa.2017.10.049
  9. Chaudhari, K., & Thakkar, A. (2019). A comprehensive survey on travel recommender systems. Archives of Computational Methods in Engineering, 27(5), 1-27.
  10. Chen, L., Wu, Z., Cao, J., Zhu, G., & Ge, Y. (2020). Travel recommendation via fusing multiauxiliary information into matrix factorization. ACM Transactions on Intelligent Systems and Technology, 11(2), 1-24.
  11. Chen, L., Yang, W., Li, K., & Li, K. (2021). Distributed matrix factorization based on fast optimization for implicit feedback recommendation. Journal of Intelligent Information Systems, 56(1), 49-72. https://doi.org/10.1007/s10844-020-00601-0
  12. Esmaeili, L., Mardani, S., Golpayegani, S. A. H., & Madar, Z. Z. (2020). A novel tourism recommender system in the context of social commerce. Expert Systems with Applications, 149, 113301. https://doi.org/10.1016/j.eswa.2020.113301
  13. Esmeli, R., Bader-El-Den, M., & Abdullahi, H. (2020). Using Word2Vec recommendation for improved purchase prediction. In Proceedings of the 2020 International Joint Conference on Neural Networks, Glasgow, United Kingdom, 1-8.
  14. Fessahaye, F., Perez, L., Zhan, T., Zhang, R., Fossier, C., Markarian, R., & Oh, P. Y. (2019). T-RECSYS: A novel music recommendation system using deep learning. In Proceedings of the IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA, 1-6.
  15. Fudholi, D. H., Rani, S., Arifin, D. M., & Satyatama, M. R. (2021). Deep learning-based mobile tourism recommender system. Scientific Journal of Informatics, 8(1), 111-118. https://doi.org/10.15294/sji.v8i1.29262
  16. Guo, L., Liang, J., Zhu, Y., Luo, Y., Sun, L., & Zheng, X. (2019). Collaborative filtering recommendation based on trust and emotion. Journal of Intelligent Information Systems, 53(1), 113-135. https://doi.org/10.1007/s10844-018-0517-4
  17. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 770-778.
  18. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. (2017). Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web, Perth, Australia, 173-182.
  19. Hong, M. (2021). Decrease and conquer-based parallel tensor factorization for diversity and real-time of multi-criteria recommendation. Information Sciences, 562, 259-278. https://doi.org/10.1016/j.ins.2021.02.005
  20. Hong, M., & Jung, J. J. (2021a). Multi-criteria tensor model consolidating spatial and temporal information for tourism recommendation. Journal of Ambient Intelligence and Smart Environments, 13(1), 5-19. https://doi.org/10.3233/AIS-200584
  21. Hong, M., & Jung, J. J. (2021b). Multi-criteria tensor model for tourism recommender systems. Expert Systems with Applications, 170, 114537. https://doi.org/10.1016/j.eswa.2020.114537
  22. Hong, M., & Jung, J. J. (2022). Sentiment aware tensor model for multi-criteria recommendation. Applied Intelligence, 1-20. https://doi.org/10.1007/s10489-022-03267-z
  23. Katarya, R., & Arora, Y. (2020). CAPSMF: A novel product recommender system using deep learning based text analysis model. Multimedia Tools and Applications, 79(47), 35927-35948. https://doi.org/10.1007/s11042-020-09199-5
  24. Kotiloglu, S., Lappas, T., Pelechrinis, K., & Repoussis, P. P. (2017). Personalized multi-period tour recommendations. Tourism Management, 62, 76-88. https://doi.org/10.1016/j.tourman.2017.03.005
  25. Le, Q. V., & Mikolov, T. (2014). Distributed representations of sentences and documents. In Proceedings of the 31th International Conference on Machine Learning, Beijing, China, 1188-1196.
  26. Li, M., Wu, H., & Zhang, H. (2019). Matrix factorization for personalized recommendation with implicit feedback and temporal information in social ecommerce networks. IEEE Access, 7, 141268-141276. https://doi.org/10.1109/access.2019.2943959
  27. Liu, Q., Chen, E., Xiong, H., Ge, Y., Li, Z., & Wu, X. (2014). A cocktail approach for travel package recommendation. IEEE Transactions on Knowledge and Data Engineering, 26(2), 278-293. https://doi.org/10.1109/TKDE.2012.233
  28. Liu, Q., Zeng, Y., Mokhosi, R., & Zhang, H. (2018). STAMP: Short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, 1831-1839.
  29. Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12-32. https://doi.org/10.1016/j.dss.2015.03.008
  30. Misztal-Radecka, J., Indurkhya, B., & SmywinskiPohl, A. (2021). Meta-User2Vec model for addressing the user and item cold-start problem in recommender systems. User Modeling and User-Adapted Interaction, 31(2), 261-286. https://doi.org/10.1007/s11257-020-09282-4
  31. Musto, C., Semeraro, G., De Gemmis, M., & Lops, P. (2015). Word embedding techniques for content-based recommender systems: An empirical evaluation. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys), Vienna, Austria.
  32. Nassar, N., Jafar, A., & Rahhal, Y. (2020). A novel deep multi-criteria collaborative filtering model for recommendation system. Knowledge-Based Systems, 187, 104811. https://doi.org/10.1016/j.knosys.2019.06.019
  33. Ozsoy, M. G. (2016). From word embeddings to item recommendation. CoRR. abs/1601.01356
  34. Park, S. T., & Liu, C. (2020). A study on topic models using Lda and word2vec in travel route recommendation: Focus on convergence travel and tours reviews. Personal and Ubiquitous Computing, 26, 1-17.
  35. Pessemier, T. D., Dhondt, J., & Martens, L. (2017). Hybrid group recommendations for a travel service. Multimedia Tools and Applications, 76(2), 2787-2811. https://doi.org/10.1007/s11042-016-3265-x
  36. Rakesh, V., Jadhav, N., Kotov, A., & Reddy, C. K. (2017). Probabilistic social sequential model for tour recommendation. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, Cambridge, United Kingdom, 631-640.
  37. Shambour, Q. (2021). A deep learning based algorithm for multi-criteria recommender systems. Knowledge-Based Systems, 211, 106545. https://doi.org/10.1016/j.knosys.2020.106545
  38. Sharifihosseini, A. (2019). A case study for presenting bank recommender systems based on bon card transaction data. In Proceedings of the 9th International Conference on Computer and Knowledge Engineering, Iran, 72-77.
  39. Tahmasebi, H., Ravanmehr, R., & Mohamadrezaei, R. (2021). Social movie recommender system based on deep autoencoder network using Twitter data. Neural Computing and Applications, 33(5), 1607-1623. https://doi.org/10.1007/s00521-020-05085-1
  40. Tan, C., Liu, Q., Chen, E., Xiong, H., & Wu, X. (2014). Object-oriented travel package recommendation. ACM Transactions on Intelligent Systems and Technology, 5(3), 1-26.
  41. Thasal, R., Yelkar, S., Tare, A., & Gaikwad, S. (2018). Information retrieval and de-duplication for tourism recommender system. International Research Journal of Engineering and Technology, 5(03), 1683-1687.
  42. Vuong Nguyen, L., Nguyen, T. H., Jung, J. J., & Camacho, D. (2021). Extending collaborative filtering recommendation using word embedding: A hybrid approach. Concurrency and Computation: Practice and Experience, e6232. https://doi.org/10.1002/cpe.6232
  43. Wang, M. (2020). Applying internet information technology combined with deep learning to tourism collaborative recommendation system. Plos One, 15(12), e0240656. https://doi.org/10.1371/journal.pone.0240656
  44. Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, 52(1), 1-38. https://doi.org/10.1145/3158369
  45. Zhao, P., Xu, C., Liu, Y., Sheng, V. S., Zheng, K., Xiong, H., & Zhou, X. (2021). Photo2Trip: Exploiting visual contents in geo-tagged photos for personalized tour recommendation. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1708-1721. https://doi.org/10.1109/TKDE.2019.2943854
  46. Zheng, X., Luo, Y., Sun, L., Zhang, J., & Chen, F. (2018). A tourism destination recommender system using users' sentiment and temporal dynamics. Journal of Intelligent Information Systems, 51(3), 557-578. https://doi.org/10.1007/s10844-018-0496-5
  47. Zhu, G., Cao, J., Li, C., & Wu, Z. (2017). A recommendation engine for travel products based on topic sequential patterns. Multimedia Tools and Applications, 76(16), 17595-17612. https://doi.org/10.1007/s11042-017-4406-6