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NTIS 바로가기지능정보연구 = Journal of intelligence and information systems, v.29 no.4, 2023년, pp.31 - 49
김동언 (경희대학교 대학원 빅데이터응용학과) , 장동수 (경희대학교 대학원 빅데이터응용학과) , 엄금철 (가천대학교 경영대학 경영학과) , 이가은 (광운대학교 경영대학 국제통상학부)
With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rat...
Abdi, A., Shamsuddin, S. M., Hasan, S., & Piran,?J. (2019). Deep learning-based sentiment?classification of evaluative text based on?Multi-feature fusion. Information Processing?& Management, 56(4), 1245-1259.
Al-Shamri, M. Y. H. (2016). User profiling?approaches for demographic recommender?systems. Knowledge-based systems, 100, 175-187.
Asani, E., Vahdat-Nejad, H., & Sadri, J. (2021).?Restaurant recommender system based on?sentiment analysis. Machine Learning with?Applications, 6, 100114.
Bobadilla, J., Ortega, F., Hernando, A., & Gutierrez,?A. (2013). Recommender systems survey.?Knowledge-based systems, 46, 109-132.
Chai, T., & Draxler, R. R. (2014). Root mean square?error (RMSE) or mean absolute error (MAE)?-Arguments against avoiding RMSE in the?literature. Geoscientific model development, 7(3),?1247-1250.
Chen, H., Li, Z., & Hu, W. (2016). An improved?collaborative recommendation algorithm based?on optimized user similarity. The Journal of?Supercomputing, 72, 2565-2578.
Chen, S., & Peng, Y. (2018). Matrix factorization?for recommendation with explicit and implicit?feedback. Knowledge-based systems, 158, 109-117.
Chhipa, S., Berwal, V., Hirapure, T., & Banerjee,?S. (2022). Recipe Recommendation System?Using TF-IDF. ITM Web of Conferences,
Das, A. S., Datar, M., Garg, A., & Rajaram, S. (2007).?Google news personalization: scalable online?collaborative filtering. Proceedings of the 16th?international conference on World Wide Web,
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.
Gao, M., Wu, Z., & Jiang, F. (2011). Userrank for item-based collaborative filtering recommendation.?Information Processing Letters, 111(9), 440-446.
Glorot, X., & Bengio, Y. (2010). Understanding?the difficulty of training deep feedforward neural?networks. In Proceedings of the thirteenth?international conference on artificial intelligence?and statistics. JMLR Workshop and Conference?Proceedings, 249-256.
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D.?(1992). Using collaborative filtering to weave?an information tapestry. Communications of?the ACM, 35(12), 61-70.
Hassan, A., & Mahmood, A. (2018). Convolutional?recurrent deep learning model for sentence?classification. IEEE Access, 6, 13949-13957.
Hazrati, N., & Ricci, F. (2022). Recommender?systems effect on the evolution of users'?choices distribution. Information Processing?& Management, 59(1), 102766.
He, X., & Chua, T. S. (2017). Neural factorization?machines for sparse predictive analytics. In?Proceedings of the 40th International ACM?SIGIR conference on Research and Development?in Information Retrieval, 355-364.
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua,?T. S. (2017, April). Neural collaborative filtering.?In Proceedings of the 26th international?conference on world wide web, 173-182.
Hegde, S. B., Satyappanavar, S., & Setty, S. (2018).?Sentiment based food classification for restaurant?business. In 2018 International Conference?on Advances in Computing, Communications?and Informatics (ICACCI), 1455-1462.
Hlee, S., Lee, J., Yang, S. B., & Koo, C. (2019).?The moderating effect of restaurant type on?hedonic versus utilitarian review evaluations.?International Journal of Hospitality Management,?77, 195-206.
Horng, J. S., & Hsu, H. (2020). A holistic aesthetic?experience model: Creating a harmonious dining?environment to increase customers' perceived?pleasure. Journal of Hospitality and Tourism?Management, 45, 520-534.
Idrissi, N., & Zellou, A. (2020). A systematic literature?review of sparsity issues in recommender?systems. Social Network Analysis and Mining,?10(1), 1-23.
Jain, A., Nagar, S., Singh, P. K., & Dhar, J.?(2020). EMUCF: Enhanced multistage user-based collaborative filtering through non-linear?similarity for recommendation systems. Expert?Systems with Applications, 161, 113724.
Jin, B., Gao, C., He, X., Jin, D., & Li, Y. (2020, July).?Multi-behavior recommendation with graph?convolutional networks. In Proceedings of the?43rd International ACM SIGIR Conference?on Research and Development in Information?Retrieval, 659-668.
Kim, D., Park, C., Oh, J., Lee, S., & Yu, H.?(2016). Convolutional matrix factorization for?document context-aware recommendation. In?Proceedings of the 10th ACM conference on?recommender systems, 233-240.
Kingma, D. P., & Ba, J. (2015). Adam: A Method?for Stochastic Optimization. arXiv preprint?arXiv:1412.6980.
Koohi, H., & Kiani, K. (2016). User based collaborative?filtering using fuzzy C-means. Measurement,?91, 134-139.
Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix?factorization techniques for recommender?systems. Computer, 42(8), 30-37.
Lee, M., Jeong, M., & Lee, J. (2017). Roles of negative?emotions in customers' perceived helpfulness?of hotel reviews on a user-generated review?website: A text mining approach. International?Journal of Contemporary Hospitality Management,?29(2), 762-783.
Li, Q., Li, X., Lee, B., & Kim, J. (2021). A Hybrid?CNN-Based Review Helpfulness Filtering Model?for Improving E-Commerce Recommendation?Service. Applied Sciences, 11(18), 8613. https://www.mdpi.com/2076-3417/11/18/8613
Li, X., Wang, M., & Liang, T.-P. (2014). A multitheoretical kernel-based approach to social?network-based recommendation. Decision Support?Systems, 65, 95-104.
Lima, G. R., Mello, C. E., Lyra, A., & Zimbrao,?G. (2020). Applying landmarks to enhance?memory-based collaborative filtering. Information?Sciences, 513, 412-428.
Loureiro, S. M. C., Almeida, M., & Rita, P.?(2013). The effect of atmospheric cues and?involvement on pleasure and relaxation: The?spa hotel context. International Journal of?Hospitality Management, 35, 35-43.
Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015).?Recommender system application developments:?a survey. Decision Support Systems, 74, 12-32.
Mahadi, M., Zainuddin, N., Shah, N., Naziron, N. A.,?& Rum, S. (2018). E-halal restaurant recommender?system using collaborative filtering algorithm.?Journal of Advanced Research in Computing?and Applications, 12(1), 22-34.
Miao, X., Gao, Y., Chen, G., Cui, H., Guo, C., & Pan,?W. (2016). SI2P: A restaurant recommendation?system using preference queries over incomplete?information. Proceedings of the VLDB Endowment,?9(13), 1509-1512.
Nemade, G., Deshmane, R., Thakare, P., Patil, M., &?Thombre, V. (2017). Smart tourism recommender?system. International Research Journal of?Engineering and Technology (IRJET), 4(11),?601-603.
Nilashi, M., Ibrahim, O., & Bagherifard, K. (2018). A?recommender system based on collaborative?filtering using ontology and dimensionality?reduction techniques. Expert Systems with?Applications, 92, 507-520.
Onan, A. (2021). Sentiment analysis on product?reviews based on weighted word embeddings?and deep neural networks. Concurrency and?Computation: Practice and Experience, 33(23), e5909.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom,?P., & Riedl, J. (1994, October). Grouplens: An?open architecture for collaborative filtering of?netnews. In Proceedings of the 1994 ACM?conference on Computer supported cooperative?work, 175-186.
Sak, H., Senior, A., & Beaufays, F. (2014). Long?short-term memory based recurrent neural?network architectures for large vocabulary speech?recognition. arXiv preprint arXiv:1402.1128.
Salehan, M., & Kim, D. J. (2016). Predicting the?performance of online consumer reviews: A?sentiment mining approach to big data analytics.?Decision Support Systems, 81, 30-40.
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J.?(2001, April). Item-based collaborative filtering?recommendation algorithms. In Proceedings?of the 10th international conference on World?Wide Web, 285-295.
Saumya, S., Singh, J. P., & Dwivedi, Y. K. (2020).?Predicting the helpfulness score of online?reviews using convolutional neural network.?Soft Computing, 24(15), 10989-11005.
Singh, M. (2020). Scalability and sparsity issues in?recommender datasets: a survey. Knowledge?and Information Systems, 62(1), 1-43.
Unger, M., Tuzhilin, A., & Livne, A. (2020). Context-aware recommendations based on deep learning?frameworks. ACM Transactions on Management?Information Systems (TMIS), 11(2), 1-15.
Xue, H. J., Dai, X., Zhang, J., Huang, S., & Chen,?J. (2017). Deep matrix factorization models for?recommender systems. In IJCAI, 17, 3203-3209.
Yang, S., Yao, J., & Qazi, A. (2020). Does the?review deserve more helpfulness when its?title resembles the content? Locating helpful?reviews by text mining. Information Processing?& Management, 57(2), 102179.
Yoo, S., Song, J., & Jeong, O. (2018). Social media?contents based sentiment analysis and prediction?system. Expert Systems with Applications, 105,?102-111.
Yu, B., Zhou, J., Zhang, Y., & Cao, Y. (2017).?Identifying restaurant features via sentiment?analysis on yelp reviews. arXiv preprint arXiv:1709.08698.
Yu, K., Schwaighofer, A., Tresp, V., Xu, X., &?Kriegel, H.-P. (2004). Probabilistic memory-based collaborative filtering. IEEE Transactions?on Knowledge and Data Engineering, 16(1),?56-69.
Yue, W., Wang, Z., Liu, W., Tian, B., Lauria, S., &?Liu, X. (2021). An optimally weighted user-and?item-based collaborative filtering approach to?predicting baseline data for Friedreich's Ataxia?patients. Neurocomputing, 419, 287-294.
Zhang, H., Ganchev, I., Nikolov, N. S., Ji, Z., &?O'Droma, M. (2021). FeatureMF: an item?feature enriched matrix factorization model?for item recommendation. IEEE Access, 9,?65266-65276.
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