<그림 1> RBM의 구조
<그림 2> 딥러닝을 이용한 온라인 리뷰 기반 다속성별 추천 모형
<그림 3> 토픽의 개수별 혼잡도
<표 1> 리뷰 기반의 추천시스템 연구
<표 2> 다속성 기반 추천시스템 연구
<표 3> 레스토랑 속성 및 키워드(일부)
<표 4> 속성별로 분류된 리뷰 수
<표 5> 속성별로 분류된 리뷰 결과(예시)
<표 6> 레스토랑 속성별 데이터 요약
<표 7> 실험 결과
References
- 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
- 김진화, 변현수, 이승훈, "온라인 리뷰를 활용한 사용자 이해 및 서비스 가치 증대," 정보시스템연구, 제20권, 제2호, 2011, pp. 21-36.
- 문혜선, "레스토랑 서비스 선택속성이 고객의 감정반응 및 만족도에 미치는 영향 연구 - 레스토랑 유형별 차이 비교 -," 호텔관광연구, 제17권, 제4호, 2016, pp. 203- 218.
- 양낙영, 김성근, 강주영, "텍스트마이닝 방법론과 메신저UI를 활용한 융합연구 촉진을 위한 연구자 및 연구분야 추천시스템의 제안", 정보시스템연구, 제27권 제4호, 2018, 71-96.
- 이정실, "AHP를 이용한 패밀리 레스토랑의 선택속성에 따른 선택 대안 평가에 관한 연구," 관광레저연구, 제25권, 제4호, 2013, pp. 153-168.
- 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
- 정남호, 엄태휘, "온라인 여행사의 추천정보가 구매의사결정과 재사용의도에 미치는 영향", 정보시스템연구, 제26권, 제3호, 2017, 149-169.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- Georgiev, K. and Nakov, P., "A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines," International conference on machine learning, 2013.
- 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.
- 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.
- 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
- Khusro, S., Ali, Z. and Ullah, I., "Recommender Systems: Issues, Challenges, and Research Opportunities," In Information Science and Applications (ICISA), 2016, pp. 1179-1180.
- 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
- 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
- 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
- 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.
- Shani, G. and Gunawardana, A., "Evaluating recommender systems," Recommender systems handbook, 2011, pp. 257-297.
- 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
- 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
- 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
- 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.
- 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
- 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
- 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.
- Zhang, Z., Zhang, D. and Lai, J., "urCF: User Review Enhanced Collaborative Filtering," Proceedings of the 20th Americas Conference on Information Systems, 2014.