References
- 고건, 김보연 (2015). 베스트셀러 표지디자인과 소비자의 구매결정에 관한연구-온라인 서점을 중심으로. 커뮤니케이션 디자인학연구, (53), 84-93.
- 김경재, 안현철 (2009). 개인화된 추천시스템을 위한 사용자-상품 매트릭스 축약기법. Journal of Information Technology Applications & Management, 16(1), 97-113.
- 김민정, 조윤호 (2015). 빅데이터 기반 추천 시스템 구현을 위한 다중 프로파일 앙상블 기법. 지능정보연구, 21(4), 93-110. https://doi.org/10.13088/jiis.2015.21.4.093
- 김영준, 김용희, 김응모 (2015). 협업필터링과 데이터마이닝을 통한 도서추천 시스템 제안. 2015 한국통신학회 추계종합학술발표회 논문집, 58-60.
- 이석원, 임세희, 양지훈 (2016). 협력적 필터링과 연관규칙 알고리즘을 활용한 도서추천시스템. 한국정보과학회 2016년 동계학술대회 논문집, 1818-1820.
- 대한출판문화협회 (2020). 2019년 출판시장 통계(주요 출판사와 서점의 매출액, 영업이익 현황). 서울: 대한출판문화협회.
- 손지은, 김성범, 김현중, 조성준 (2015). 추천 시스템 기법 연구동향 분석. 대한산업공학회지, 41(2), 185-208. https://doi.org/10.7232/JKIIE.2015.41.2.185
- 심재문, 강지욱, 권오병 (2010). 상황인식 기술을 이용한 운전자 선호도 기반 교통상세정보 추천 시스템. 지식경영연구, 11(2), 75-93.
- 조승연, 최지은, 이규현, 김희웅 (2015). 고객 온라인 구매후기를 활용한 추천시스템 개발 및 적용. Information Systems Review, 17(3), 77-93. https://doi.org/10.14329/isr.2015.17.3.077
- 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
- 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
- Caselles-Dupre, H., Lesaint, F., & Royo-Letelier, J. (2018). Word2vec applied to recommendation: Hyperparameters matter. In Proceedings of the 12th ACM Conference on Recommender Systems, 352-356.
- Cheng, H. T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., Anil, R., Haque, Z., Hong, L., Jain, V., Liu, X., & Shah, H. (2016). Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 7-10.
- Chen, K., Wang, J., Chen, L. C., Gao, H., Xu, W., & Nevatia, R. (2015). Abc-cnn: An attention based convolutional neural network for visual question answering. arXiv preprint arXiv: 1511.05960.
- Gao, C., He, X., Gan, D., Chen, X., Feng, F., Li, Y., Chua, T. S., Yao, L., Song, Y., & Jin, D. (2018). Learning recommender systems from multi-behavior data. arXiv preprint arXiv:1809.08161.
- Haucap, J., & Heimeshoff, U. (2014). Google, Facebook, Amazon, eBay: Is the Internet driving competition or market monopolization? International Economics and Economic Policy, 11(1), 49-61. https://doi.org/10.1007/s10368-013-0247-6
- Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5-53. https://doi.org/10.1145/963770.963772
- He, R., & McAuley, J. (2016). VBPR: Visual bayesian personalized ranking from implicit feedback. In Thirtieth AAAI Conference on Artificial Intelligence, 144-150.
- He, X., Du, X., Wang, X., Tian, F., Tang, J., & Chua, T. S. (2018). Outer product-based neural collaborative filtering. arXiv preprint arXiv:1808.03912.
- He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural collaborative filtering. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web, 173-182.
- Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2015). Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939.
- Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37. https://doi.org/10.1109/MC.2009.263
- Kurt, Z., & Ozkan, K. (2017). An image-based recommender system based on feature extraction techniques. In 2017 International Conference on Computer Science and Engineering (UBMK), 769-774.
- Lathia, N., Hailes, S., Capra, L., & Amatriain, X. (2010). Temporal diversity in recommender systems. In Proceedings of the 33rd International Acm Sigir Conference on Research and Development in Information Retrieval, 210-217.
- Lei, C., Liu, D., Li, W., Zha, Z. J., & Li, H. (2016). Comparative deep learning of hybrid representations for image recommendations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2545-2553.
- Low, Y. H., Yap, W. S., & Tee, Y. K. (2018). Convolutional neural network-based collaborative filtering for recommendation systems. In International Conference on Robot Intelligence Technology and Applications, 117-131.
- Ouyang, Y., Liu, W., Rong, W., & Xiong, Z. (2014). Autoencoder-based collaborative filtering. In International Conference on Neural Information Processing, 284-291.
- Rawat, Y. S., & Kankanhalli, M. S. (2016). ConTagNet: Exploiting user context for image tag recommendation. In Proceedings of the 24th ACM International Conference on Multimedia, 1102-1106.
- Persson, P. (2018). Attention manipulation & information overload. Behavioural Public Policy, 2(1), 78-106. https://doi.org/10.1017/bpp.2017.10
- Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, 175-186.
- Salakhutdinov, R., Mnih, A., & Hinton, G. (2007). Restricted Boltzmann machines for collaborative filtering. In Proceedings of the 24th International Conference on Machine Learning, 791-798.
- Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web, 285-295.
- Sase, A., Varun, K., Rathod, S., & Patil, D. (2015). A proposed book recommender system. International Journal of Advanced Research in Computer and Communication Engineering, 4(2), 481-483.
- Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web (pp. 291-324). Berlin: Springer.
- Sedhain, S., Menon, A. K., Sanner, S., & Xie, L. (2015). Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web, 111-112.
- Wang, S., Wang, Y., Tang, J., Shu, K., Ranganath, S., & Liu, H. (2017). What your images reveal: Exploiting visual contents for point-of-interest recommendation. In Proceedings of the 26th International Conference on World Wide Web, 391-400.
- Xu, Z., Chen, C., Lukasiewicz, T., Miao, Y., & Meng, X. (2016). Tag-aware personalized recommendation using a deep-semantic similarity model with negative sampling. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 1921-1924.
- Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1), 1-38.