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NTIS 바로가기지능정보연구 = Journal of intelligence and information systems, v.27 no.3, 2021년, pp.29 - 56
이청용 (경희대학교 빅데이터응용학과) , 이병현 (경희대학교 빅데이터응용학과) , 이흠철 (경희대학교 빅데이터응용학과) , 김재경 (경희대학교 경영대학 & 빅데이터응용학과)
Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service tha...
Abdollahi, B., and O. Nasraoui, "Using explainability for constrained matrix factorization", Proceedings of the Eleventh ACM Conference on Recommender Systems, (2017), 79~83.
Al-Bashiri, H., M. A. Abdulgabber, A. Romli, and H. Kahtan, "An improved memory-based collaborative filtering method based on the TOPSIS technique", PloS one, Vol. 13, No.10(2018), e0204434.
Ar, Y., and E. Bostanci, "A genetic algorithm solution to the collaborative filtering problem", Expert Systems with Applications, Vol.61 (2016), 122~128.
Baek, H., J. Ahn, and Y. Choi, "Helpfulness of online consumer reviews: Readers' objectives and review cues", International Journal of Electronic Commerce, Vol.17, No.2(2012), 99~126.
Barragans-Martinez, A. B., E. Costa-Montenegro, J. C. Burguillo, M. Rey-Lopez, F. A. Mikic-Fonte, and A. Peleteiro, "A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition", Information Sciences, Vol.180, No.22(2010), 4290~4311.
Bennett, J., and S. Lanning, "The netflix prize", Proceedings of KDD Cup and Workshop, Vol.2007, (2007), 35.
Bobadilla, J., F. Ortega, A. Hernando, and J. Alcala, "Improving collaborative filtering recommender system results and performance using genetic algorithms", Knowledge-Based Systems, Vol.24, No.8(2011), 1310~1316.
Bokde, D., S. Girase, and D. Mukhopadhyay, "Matrix factorization model in collaborative filtering algorithms: A survey", Procedia Computer Science, Vol.49, (2015), 136~146.
Cao, R., X. Zhang, and H. Wang, "A Review Semantics Based Model for Rating Prediction", IEEE Access, Vol.8, (2019), 4714~4723.
Castelli, M., L. Manzoni, L. Vanneschi, and A. Popovic, "An expert system for extracting knowledge from customers' reviews: The case of Amazon. com, Inc", Expert Systems with Applications, Vol.84, (2017), 117~126.
Chai, T., and R. R. Draxler, "Root mean square error (RMSE) or mean absolute error (MAE)?- Arguments against avoiding RMSE in the literature", Geoscientific Model Development, Vol.7, No.3(2014), 1247~1250.
Cheng, Z., Y. Ding, L. Zhu, and M. Kankanhalli, "Aspect-aware latent factor model: Rating prediction with ratings and reviews", Proceedings of the World Wide Web Conference, (2018), 639~648.
Choi, I. Y., M. G. Oh, J. K. Kim, and Y. U. Ryu, "Collaborative filtering with facial expressions for online video recommendation", International Journal of Information Management, Vol.35, No.3(2016), 397~402.
Chung, K. Y., D. Lee, and K. J. Kim, "Categorization for grouping associative items using data mining in item-based collaborative filtering", Multimedia Tools and Applications, Vol.71, No.2(2014), 889~904.
Cui, C., and T. Fearn, "Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration", Chemometrics and Intelligent Laboratory Systems, Vol.182, (2018), 9~20.
Das, A. S., M. Datar, A. Garg, and S. Rajaram, "Google news personalization: scalable online collaborative filtering", Proceedings of the 16th International Conference on World Wide Web, (2007), 271~280.
Elahi, M., F. Ricci, and N. Rubens, "A survey of active learning in collaborative filtering recommender systems", Computer Science Review, Vol.20, (2016), 29~50.
Fu, M., H. Qu, D. Moges, and L. Lu, "Attention based collaborative filtering", Neurocomputing, Vol.311, (2018), 88~98.
Garcia-Cumbreras, M. A., A. Montejo-Raez, and M. C. Diaz-Galiano, "Pessimists and optimists: Improving collaborative filtering through sentiment analysis", Expert Systems with Applications, Vol.40, No.17(2013), 6758~6765.
Ge, S., T. Qi, C. Wu, F. Wu, X. Xie, and Y. Huang, "Helpfulness-aware review based neural recommendation", CCF Transactions on Pervasive Computing and Interaction, Vol.1, No.4(2019), 285~295.
Goldberg, D., D. Nichols, B. M. Oki, and D. Terry, "Using collaborative filtering to weave an information tapestry", Communications of the ACM, (1992), 61~70.
Guy, I., M. Avihai, A. Nus, and F. Raiber, "Extracting and Ranking Travel Tips from User-Generated Reviews", Proceedings of the 26th International Conference on World Wide Web, (2017), 987~996.
Hammou, B. A., and A. A. Lahcen, "FRAIPA: A fast recommendation approach with improved prediction accuracy", Expert Systems with Applications, Vol.87, (2017), 90~97.
He, X., L. Liao, H. Zhang, L. Nie, X. Hu, and T.S. Chua, "Neural collaborative filtering", Proceedings of the 26th International Conference on World Wide Web, (2017), 173~182.
Herlocker, J. L., J. A. Konstan, L. G. Terveen, and J. T. Riedl, "Evaluating collaborative filtering recommender systems", ACM Transactions on Information Systems, Vol.22, No.1(2004), 5~53.
Hu, Y. H., Y. L. Chen, and H. L. Chou, "Opinion mining from online hotel reviews-a text summarization approach", Information Processing & Management, Vol.53, No.2(2017), 436~449.
Isinkaye, F. O., Y. O. Folajimi, and B. A. Ojokoh, "Recommendation systems: Principles, methods and evaluation", Egyptian Informatics Journal, Vol.16, No.3(2015), 261~273.
Janke, J., M. Castelli, and A. Popovic, "Analysis of the proficiency of fully connected neural networks in the process of classifying digital images. Benchmark of different classification algorithms on high-level image features from convolutional layers", Expert Systems with Applications, Vol.135, (2019), 12~38.
Jeong, B., J. Lee, and H. Cho, "Improving memory-based collaborative filtering via similarity updating and prediction modulation", Information Sciences, Vol.180, No.5(2010), 602~612.
Johnson, R., and T. Zhang, "Effective use of word order for text categorization with convolutional neural networks", arXiv preprint arXiv:1412.1058, (2014).
Kaushik, K., R. Mishra, N. P. Rana, and Y. K. Dwivedi, "Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon", Journal of Retailing and Consumer Services, Vol.45, (2018), 21~32.
Khan, Z. Y., and Z. Niu, "CNN with Depthwise Separable Convolutions and Combined Kernels for Rating Prediction", Expert Systems with Applications, (2020), 114528.
Kim, H. K., J. K. Kim., and Y. U. Ryu, "Personalized recommendation over a customer network for ubiquitous shopping", IEEE Transactions on Services Computing, Vol.2, No.2(2009), 140~151.
Kim, J. K., H. K. Kim, H. Y. Oh, and Y. U. Ryu, "A group recommendation system for online communities", International Journal of Information Management, Vol.30, No.3(2010), 212~219.
Knees, P., D. Schnitzer, and A. Flexer, "Improving neighborhood-based collaborative filtering by reducing hubness", Proceedings of International Conference on Multimedia Retrieval, (2014), 161~168.
Koren, Y., and R. Bell, Recommender systems handbook, Springer, New York, USA, 2015.
Krishnamoorthy, S, "Linguistic features for review helpfulness prediction", Expert Systems with Applications, Vol.42, No.7(2015), 3751~3759.
Lee, D., and K. Hosanagar, "How do recommender systems affect sales diversity? A crosscategory investigation via randomized field experiment", Information Systems Research, Vol.30, No.1(2019), 239~259.
Lei, X., X. Qian, and G. Zhao, "Rating prediction based on social sentiment from textual reviews", IEEE Transactions on Multimedia, Vol.18, No.9(2016), 1910~1921.
Leung, C. W., S. C. Chan, and F. Chung, "Integrating Collaborative Filtering and Sentiment Analysis: A Rating Inference Approach", Proceedings of the ECAI Workshop on Recommender Systems, (2006), 62~68.
Li, X., M. Wang, and T. P. Liang, "A multi-theoretical kernel-based approach to social network-based recommendation", Decision Support Systems, Vol.65, (2014), 95~104.
Linden, G., B. Smith, and J. York, "Amazon.com recommendations: Item-to-item collaborative filtering", IEEE Internet Computing, Vol.7, No.1(2003), 76~80.
Liu, Y., X. Huang, A. An, and X. Yu, "Modeling and predicting the helpfulness of online reviews", 8th IEEE International Conference on Data Mining, (2008), 443~452.
Lu, J., D. Wu, M. Mao, W. Wang, and G. Zhang, "Recommender system application developments: a survey", Decision Support Systems, Vol.74, (2015), 12~32.
Mandal, S., and A. Maiti, "Deep collaborative filtering with social promoter score-based user-item interaction: a new perspective in recommendation", Applied Intelligence, (2021), 1~26.
Mishra, R., P. Kumar, and B. Bhasker, "A web recommendation system considering sequential information", Decision Support Systems, Vol.75, (2015), 1~10.
Moon, H. S., J. H. Yoon, I. Y. Choi, and J. K. Kim, "An Exploratory Study of Collaborative Filtering Techniques to Analyze the Effect of Information Amount", Asia Pacific Journal of Information Systems, Vol.27, No.2(2017), 126~138.
Moore, S. G., "Attitude predictability and helpfulness in online reviews: The role of explained actions and reactions", Journal of Consumer Research, Vol.42, No.1(2015), 30~44.
Nassirtoussi, A. K., S. Aghabozorgi, T. Y. Wah, and D. C. L. Ngo, "Text mining for market prediction: A systematic review", Expert Systems with Applications, Vol.41, No.16(2014), 7653~7670.
Ngo-Ye, T. L., and A. P. Sinha, "The influence of reviewer engagement characteristics on online review helpfulness: A text regression model", Decision Support Systems, Vol.61, (2014), 47~58.
Paradarami, T. K., N. D. Bastian, and J. L. Wightman, "A hybrid recommender system using artificial neural networks", Expert Systems with Applications, Vol.83, (2017), 300~313.
Park, D. H., H. K. Kim, I. Y. Choi and J. K. Kim, "A literature review and classification of recommender systems research", Expert Systems with Applications, Vol.39, No.11(2012), 10059~10072.
Polatidis, N., and C. K. Georgiadis "A multi-level collaborative filtering method that improves recommendations", Expert Systems with Applications, Vol.48, (2016), 100~110.
Postmus, S., and S. Bhulai, "Recommender system techniques applied to Netflix movie data", Research Paper Business Analytics, Vrije Universiteit Amsterdam, Netherlands, 2018.
Qiu, L., S. Gao, W. Cheng, and J. Guo, "Aspect-based latent factor model by integrating ratings and reviews for recommender system", Knowledge-Based Systems, Vol.110, (2016), 233~243.
Ricci, F., L. Rokach and B. Shapira, Introduction to recommender systems handbook, Springer, Boston, USA, 2011.
Sanchez-Moreno, D., A. B. G. Gonzalez, M. D. M. Vicente, V. F. L. Batista, and M. N. M. Garcia, "A collaborative filtering method for music recommendation using playing coefficients for artists and users", Expert Systems with Applications, Vol.66, (2016), 234~244.
Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms", Proceedings of the 10th International Conference on World Wide Web, (2001), 285~295.
Saumya, S., and J. P. Singh, "Detection of spam reviews: a sentiment analysis approach", CSI Transactions on ICT, Vol.6, No.2(2018), 137~148.
Siering, M., A. V. Deokar, and C. Janze, "Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews", Decision Support Systems, Vol.107, (2018), 52~63.
Song, C., X. K. Wang, P. F. Cheng, J. Q. Wang, and L. Li, "SACPC: A framework based on probabilistic linguistic terms for short text sentiment analysis", Knowledge-Based Systems, Vol.194, (2020), 105572.
Srifi, M., A. Oussous, A. A. Lahcen, and S. Mouline, "Recommender Systems Based on Collaborative Filtering Using Review Texts-A Survey", Information, Vol.11, No.6(2020), 317.
Su, X., and T. M. Khoshgoftaar, "A survey of collaborative filtering techniques", Advances in Artificial Intelligence, (2009).
Ullah, I., M. Hussain, and H. Aboalsamh, "An automated system for epilepsy detection using EEG brain signals based on deep learning approach", Expert Systems with Applications, Vol.107, (2018), 61~71.
Wang, X., X. Lin, and M. K. Spencer, "Exploring the effects of extrinsic motivation on consumer behaviors in social commerce: Revealing consumers' perceptions of social commerce benefits", International Journal of Information Management, Vol.45, (2019), 163~175.
Wang, X., Z. Dai., H. Li, and J. Yang, "Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis", Complexity, (2021).
Wei, S., N. Ye, S. Zhang, X. Huang, and J. Zhu, "Item-based collaborative filtering recommendation algorithm combining item category with interestingness measure", International Conference on Computer Science and Service System, (2012), 2038~2041.
Wu, P., X. Li., S. Shen, and D. He, "Social media opinion summarization using emotion cognition and convolutional neural networks", International Journal of Information Management, Vol.51, (2020), 101978.
Yoo, S., J. Song, and O. Jeong, "Social media contents based sentiment analysis and prediction system", Expert Systems with Applications, Vol.105, (2018), 102~111.
Yun, Y., D, Hooshyar, J. Jo, and H. Lim, "Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review", Journal of Information Science, Vol.44, No.3(2018), 331~344.
Zafari, F., I. Moser, and T. Sellis, "ReEx: An integrated architecture for preference model representation and explanation", Expert Systems with Applications, Vol.161, (2020), 113706.
Zhang, Y., and B. Wallace, "A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification", arXiv preprint arXiv:1510.03820, (2015).
Zhang, Z., and B. Varadarajan, "Utility scoring of product reviews", Proceedings of the 15th ACM International Conference on Information and Knowledge Management, (2006), 51~57.
Zhang, Z., D. Zhang, and J. Lai, "urCF: User Review Enhanced Collaborative Filtering", Proceedings of the 20th Americas Conference on Information Systems, (2014).
Zhang, Z., H. Lin, K. Liu, D. Wu, G. Zhang, and J. Lu, "A hybrid fuzzy-based personalized recommender system for telecom products/services", Information Sciences, Vol.235, (2013), 117~129.
Zheng, L., V. Noroozi, and S. Yu, "Joint deep modeling of users and items using reviews for recommendation", Proceedings of the 10th ACM International Conference on Web Search and Data Mining, (2017), 425~434.
Zhou, L., and P. Chaovalit, "Ontology-Supported Polarity Mining", Journal of the American Society for Information Science and Technology, Vol.59, No.1(2008), 98~110.
Zhu, T., Y. Ren, W. Zhou, J. Rong, and P. Xiong, "An effective privacy preserving algorithm for neighborhood-based collaborative filtering", Future Generation Computer Systems, Vol.36, (2014), 142~155.
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