The Journal of Information Systems (한국정보시스템학회지:정보시스템연구)

Korea Association of Information Systems (한국정보시스템학회)
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Developing the online reviews based recommender models for multi-attributes using deep learning

딥러닝을 이용한 온라인 리뷰 기반 다속성별 추천 모형 개발

  • Received : 2019.01.15
  • Accepted : 2019.03.29
  • Published : 2019.03.31
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Abstract

Purpose The purpose of this study is to deduct the factors for explaining the economic behavior of an Internet user who provides personal information notwithstanding the concern about an invasion of privacy based on the Information Privacy Calculus Theory and Communication Privacy Management Theory. Design/methodology/approach This study made a design of the research model by integrating the factors deducted from the computation theory of information privacy with the factors deducted from the management theory of communication privacy on the basis of the Dual-Process Theory. Findings According to the empirical analysis result, this study confirmed that the Privacy Concern about forms through the Perceived Privacy Risk derived from the Disposition to value Privacy. In addition, this study confirmed that the behavior of an Internet user involved in personal information offering occurs due to the Perceived Benefits contradicting the Privacy Concern.

Keywords

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<그림 1> RBM의 구조

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<그림 2> 딥러닝을 이용한 온라인 리뷰 기반 다속성별 추천 모형

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<그림 3> 토픽의 개수별 혼잡도

<표 1> 리뷰 기반의 추천시스템 연구

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<표 2> 다속성 기반 추천시스템 연구

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<표 3> 레스토랑 속성 및 키워드(일부)

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<표 4> 속성별로 분류된 리뷰 수

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<표 5> 속성별로 분류된 리뷰 결과(예시)

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<표 6> 레스토랑 속성별 데이터 요약

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<표 7> 실험 결과

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References

  1. 구민정, 안현철, "종합 평점과 다기준 평점을 선택적으로 활용하는 협업필터링 기반 하이브리드 추천시스템," 지능정보연구, 제24권, 제2호, 2018, pp.85-109. https://doi.org/10.13088/JIIS.2018.24.2.085
  2. 김진화, 변현수, 이승훈, "온라인 리뷰를 활용한 사용자 이해 및 서비스 가치 증대," 정보시스템연구, 제20권, 제2호, 2011, pp. 21-36.
  3. 문혜선, "레스토랑 서비스 선택속성이 고객의 감정반응 및 만족도에 미치는 영향 연구 - 레스토랑 유형별 차이 비교 -," 호텔관광연구, 제17권, 제4호, 2016, pp. 203- 218.
  4. 양낙영, 김성근, 강주영, "텍스트마이닝 방법론과 메신저UI를 활용한 융합연구 촉진을 위한 연구자 및 연구분야 추천시스템의 제안", 정보시스템연구, 제27권 제4호, 2018, 71-96.
  5. 이정실, "AHP를 이용한 패밀리 레스토랑의 선택속성에 따른 선택 대안 평가에 관한 연구," 관광레저연구, 제25권, 제4호, 2013, pp. 153-168.
  6. 전병국, 안현철, "사용자 리뷰 마이닝을 결합한 협업 필터링 시스템: 스마트폰 앱 추천에의 응용," 지능정보연구, 제21권, 제2호, 2015, pp. 1-18. https://doi.org/10.13088/jiis.2015.21.2.01
  7. 정남호, 엄태휘, "온라인 여행사의 추천정보가 구매의사결정과 재사용의도에 미치는 영향", 정보시스템연구, 제26권, 제3호, 2017, 149-169.
  8. 조승연, 최지은, 이규현, 김희웅, "고객 온라인 구매후기를 활용한 추천시스템 개발 및 적용," Information Systems Review, 제17권, 제3호, 2015, pp. 77-93. https://doi.org/10.14329/isr.2015.17.3.077
  9. 최준연, 이석기, 조영빈, "추천 시스템의 예측 정확도 향상을 위한 고객 평가정보의 신뢰도 활용법," 한국콘텐츠학회논문지, Vol. 13, No. 7, 2013, pp. 379-385. https://doi.org/10.5392/JKCA.2013.13.07.379
  10. Adomavicius, G. and Tuzhilin, A., "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions," IEEE Transactions on Knowledge & Data Engineering, Vol. 17, No. 6, 2005, pp. 734-749. https://doi.org/10.1109/TKDE.2005.99
  11. Adomavicius, G., and Kwon, Y. O., "New recommendation techniques for multicriteria rating systems," IEEE Intelligent Systems, Vol. 22, No. 3, 2007, pp. 48-55. https://doi.org/10.1109/MIS.2007.58
  12. Blazevic, V., Hammedi, W., Garnefeld, I, Rust, R. T., Keiningham, T., Andreassen, T. W., Donthu, N. and Carl, Walter., "Beyond Traditional Word-of-Mouth: An Expanded Model of Customer-Driven Influence," Journal of Service Management, Vol. 24, No. 3, 2013, pp. 294-313. https://doi.org/10.1108/09564231311327003
  13. Chai, T. and Draxler, R. R., "Root mean square error (RMSE) or mean absolute error (MAE)?-. Arguments against avoiding RMSE in the literature," Geosci Model Dev, Vol. 7, 2014, pp. 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
  14. Chen, L., Chen, G. and Wang, F., "Recommender systems based on user reviews: the state of the art," User Modeling and User-Adapted Interaction, Vol. 25, No. 2, 2015, pp. 99-154. https://doi.org/10.1007/s11257-015-9155-5
  15. Cremonesi, P., and Koren, Y. and Turrin, R., "Performance of Recommender Algorithms on Top-N Recommendation," Proc. Fourth ACM Conference on Recommender Systems, 2010, pp. 39-46.
  16. Georgiev, K. and Nakov, P., "A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines," International conference on machine learning, 2013.
  17. Guy, I., Avihai, M., Nus, A. and Raiber, F., "Extracting and Ranking Travel Tips from User-Generated Reviews," Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 987-996.
  18. Hariri, N., Mobasher, B., Burke, R. and Zheng, Y, "Context-aware recommendation based on review mining", Proceedings of the 9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, 2011, pp. 30-36.
  19. Herlocker, J. L., Konstan, J. A., Terveen, L. G. and Riedl, J. T., "Evaluating Collaborative Filtering Recommender Systems," ACM Trans. Information Systems, Vol. 22, No. 1, 2004, pp. 5-53. https://doi.org/10.1145/963770.963772
  20. Khusro, S., Ali, Z. and Ullah, I., "Recommender Systems: Issues, Challenges, and Research Opportunities," In Information Science and Applications (ICISA), 2016, pp. 1179-1180.
  21. Maslowska, E. and Malthouse, E.. C., and Viswanathan, V., "Do customer reviews drive purchase decisions? The moderating roles of review exposure and price," Decision Support Systems, Vol. 98, 2017, pp. 1-9. https://doi.org/10.1016/j.dss.2017.03.010
  22. Nilashi, M., Ibrahim, O. B., Ithnin, N. and Sarmin, N. H., "A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA-ANFIS," Electronic Commerce Research and Applications, Vol. 14, No. 6, 2015, pp. 542-562. https://doi.org/10.1016/j.elerap.2015.08.004
  23. Rhee, H. T., Yang, S. B., Koo, C. M. and Chung, N. H., "How Does Restaurant Attribute Importance Differ by the Type of Customer and Restaurant? Exploring TripAdvisor Reviews," E-Review of Tourism Research, 2015
  24. Salakhutdinov, R., Mnih, A. and Hinton, G. E., "Restricted Boltzmann machines for collaborative filtering," in Proceedings of the Twenty-fourth International Conference on Machine Learning, 2007, pp. 791-798.
  25. Shani, G. and Gunawardana, A., "Evaluating recommender systems," Recommender systems handbook, 2011, pp. 257-297.
  26. Shi, Z., Lee, M. G. and Whinston, A. B., "Toward a better measure of business proximity: topic modeling for industry intelligence," MIS Quarterly, Vol. 40, No. 4, 2016, pp. 1035-1056. https://doi.org/10.25300/MISQ/2016/40.4.11
  27. Siering, M., Deokar, A. V. and Janze, C., "Disentangling consumer recommendations: explaining and predicting airline recommendations based on online reviews," Decision Support System, Vol. 107, 2018, pp. 52-63. https://doi.org/10.1016/j.dss.2018.01.002
  28. Son, J. E. and Kim, S. B., "Content-based filtering for recommendation systems using multiattribute networks," Expert Systems with Applications, Vol. 89, 2017, pp. 404-412. https://doi.org/10.1016/j.eswa.2017.08.008
  29. Teh, Y., W. and Hinton, G., E., "Rate-coded restricted boltzmann machines for face recognition," In Advances in Neural Information Processing Systems, Vol. 13, 2001.
  30. Yang, C., Yu, X., Liu, Y., Nie, Y. and Wang, Y., "Collaborative ltering with weighted opinion aspects," Neurocomputing, Vol. 210, 2016, pp. 185-196. https://doi.org/10.1016/j.neucom.2015.12.136
  31. Zhang, N., Ding, S., Zhang, J. and Xue, Y., "An overview on restricted Boltzmann machines," Neurocomputing, Vol. 275, 2018, pp. 1186-1199. https://doi.org/10.1016/j.neucom.2017.09.065
  32. Zhang, W., Ding, G., Chen, L., Li, C. and Zhang, C., "Generating virtual ratings from Chinese reviews to augment online recommendations," ACM Transactions on Intelligent System Technology, Vol. 4, 2013, pp. 1-17.
  33. Zhang, Z., Zhang, D. and Lai, J., "urCF: User Review Enhanced Collaborative Filtering," Proceedings of the 20th Americas Conference on Information Systems, 2014.