연구동향 분석

신경망을 사용한 편향 분석 기반의 추천 알고리즘

황태규 2022년

논문상세정보
인용/피인용
' 신경망을 사용한 편향 분석 기반의 추천 알고리즘' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • biasanalysis
  • collaborative filtering
  • convolutional neural network
  • rating classification
  • recommendation algorithm
  • 추천 알고리즘
  • 편향분석
  • 평점 분류
  • 합성곱 신경망
  • 협업 필터링
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
677 0

0.0%

' 신경망을 사용한 편향 분석 기반의 추천 알고리즘' 의 참고문헌

  • [9] Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. Beyond accuracy: evaluating recommender systems by coverage and serendipity, 2010.
    [2010]
  • [97] Sebastian Ruder. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747, 2016.
    [2016]
  • [96] John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of machine learning research, 12(7), 2011.
    [2011]
  • [94] Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. Mymedialite: A free recommender system library. In Proceedings of the fifth ACM conference on Recommender systems, pages 305–308.
  • [93] Alan M Turing. Computing machinery and intelligence, pages 23–65. Springer, 2009.
    [2009]
  • [91] Ron Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai, volume 14, pages 1137–1145. Montreal, Canada.
  • [8] Tong Zhao, Julian McAuley, and Irwin King. Leveraging social connections to improve personalized ranking for collaborative filtering, 2014.
    [2014]
  • [89] Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An introduction to statistical learning, volume 112. Springer, 2013.
    [2013]
  • [88] MBA experimental dataset, 2021. URL https://drive.google.com/file/d/ 14bOmNhtDD6KZ_zSgl4D4fghGkg82raT4/view?usp=sharing. (accessed on 21 Dec 2021)
  • [87] Yahoo! Research alliance Webscope program. Ratings and classification data. URL https://webscope.sandbox.yahoo.com/catalog.php?datatype= r. (accessed on 5 Oct 2021)
  • [86] Iv´an Cantador, Peter Brusilovsky, and Tsvi Kuflik. Second workshop on information heterogeneity and fusion in recommender systems (hetrec2011). In Proceedings of the fifth ACM conference on Recommender systems, pages 387–388.
  • [85] F Maxwell Harper and Joseph A Konstan. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1–19, 2015.
    [2015]
  • [84] Tae-Gyu Hwang and Sung Kwon Kim. Movie recommendation through bias analysis of metadata. KIISE Transactions on Computing Practices, 27(10):479–485, 2021. (in Korean)
  • [83] Tae-Gyu Hwang and Sung Kwon Kim. Bias-based predictor to improve the recommendation performance of the rating frequency weight-based baseline predictor. Journal of KIISE, 44(5):486–495, 2017. (in Korean)
    [2017]
  • [82] Linas Baltrunas and Francesco Ricci. Item Weighting Techniques for Collaborative Filtering, pages 109–126. 2009.
    [2009]
  • [81] Panagiotis Adamopoulos and Alexander Tuzhilin. On over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in collaborative filtering systems, 2014.
    [2014]
  • [80] Tae-Gyu Hwang and Sung Kwon Kim. Movie recommendation through multiple bias analysis. Applied Sciences, 11(6):1–18, 2021.
  • [7] Linas Baltrunas, Tadas Makcinskas, and Francesco Ricci. Group recommendations with rank aggregation and collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems, pages 119–126.
  • [79] Lin Xiao, Zhang Min, Zhang Yongfeng, Gu Zhaoquan, Liu Yiqun, and Ma Shaoping. Fairness-aware group recommendation with pareto-efficiency, 2017.
    [2017]
  • [78] Xiaoying Zhang, Junzhou Zhao, and John CS Lui. Modeling the assimilation-contrast effects in online product rating systems: Debiasing and recommendations. In Proceedings of the Eleventh ACM Conference on Recommender Systems, pages 98–106.
  • [77] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1):1929–1958, 2014.
    [2014]
  • [76] David H Hubel and TN Wiesel. Shape and arrangement of columns in cat’s striate cortex. The Journal of physiology, 165(3):559–568, 1963.
    [1963]
  • [74] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
  • [73] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.
  • [72] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
    [2014]
  • [71] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6):84–90, 2017.
    [2017]
  • [70] Yann LeCun, L´eon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.
    [1998]
  • [6] Nathan N. Liu and Qiang Yang. Eigenrank: a ranking-oriented approach to collaborative filtering, 2008.
    [2008]
  • [69] Christopher M Bishop. Neural networks for pattern recognition. Oxford university press, 1995.
    [1995]
  • [66] Vilayanur S Ramachandran. Blind spots. Scientific American, 266(5):86–91, 1992.
    [1992]
  • [65] David Milner and Mel Goodale. The visual brain in action, volume 27. OUP Oxford, 2006.
    [2006]
  • [64] Yehuda Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 426–434.
  • [63] Pentti Paatero and Unto Tapper. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5(2):111–126, 1994.
    [1994]
  • [62] Aditya Krishna Menon and Charles Elkan. A log-linear model with latent features for dyadic prediction. In 2010 IEEE International Conference on Data Mining, pages 364– 373. IEEE.
  • [61] Mingrui Wu. Collaborative filtering via ensembles of matrix factorizations. In KDD Cup and Workshop 2007, pages 43–47.
  • [60] Andriy Mnih and Russ R Salakhutdinov. Probabilistic matrix factorization. In Advances in neural information processing systems, pages 1257–1264.
  • [5] Bibek Paudel, Thilo Haas, and Abraham Bernstein. Fewer flops at the top: Accuracy, diversity, and regularization in two-class collaborative filtering, 2017.
    [2017]
  • [59] Greg Linden, Brent Smith, and Jeremy York. Amazon. com recommendations: Item-toitem collaborative filtering. IEEE Internet computing, 7(1):76–80, 2003. ISSN 1089-7801.
    [2003]
  • [58] John S Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. arXiv preprint arXiv:1301.7363, 2013.
    [2013]
  • [57] Tae-Gyu Hwang, Chan-Soo Park, Jeong-Hwa Hong, and Sung Kwon Kim. An algorithm for movie classification and recommendation using genre correlation. Multimedia Tools and Applications, 75(20):12843–12858, 2016.
    [2016]
  • [56] Jeffrey Dean and Sanjay Ghemawat. Mapreduce: simplified data processing on large clusters. Communications of the ACM, 51(1):107–113, 2008.
    [2008]
  • [55] Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. The google file system. In Proceedings of the nineteenth ACM symposium on Operating systems principles, pages 29–43.
  • [54] Max Bramer. Principles of data mining, volume 180. Springer, 2007.
    [2007]
  • [53] Steven S Skiena. The data science design manual. Springer, 2017.
    [2017]
  • [52] Miroslav Kubat. An introduction to machine learning. Springer, 2017.
    [2017]
  • [51] Mike Fleckenstein, Lorraine Fellows, and Krista Ferrante. Modern data strategy. Springer, 2018.
    [2018]
  • [50] Charu C Aggarwal. Recommender systems, volume 1. Springer, 2016.
    [2016]
  • [4] Mi Zhang and Neil Hurley. Avoiding monotony: improving the diversity of recommendation lists. In Proceedings of the 2008 ACM conference on Recommender systems, pages 123–130.
  • [49] Rama Syamala Sreepada, Bidyut Kr Patra, Avijit Chakrabarty, and Satyadev Chandak. Revisiting tendency based collaborative filtering for personalized recommendations. In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pages 230–239.
  • [48] Tobias Schnabel and Paul N Bennett. Debiasing item-to-item recommendations with small annotated datasets. In Fourteenth ACM Conference on Recommender Systems, pages 73– 81.
  • [47] Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. The impact of popularity bias on fairness and calibration in recommendation. arXiv preprint arXiv:1910.05755, 2019.
    [2019]
  • [46] Tian Wang and Yuyangzi Fu. Item-based collaborative filtering with bert. In Proceedings of The 3rd Workshop on e-Commerce and NLP, pages 54–58.
  • [45] Jieun Son, Seoung Bum Kim, Hyunjoong Kim, and Sungzoon Cho. Review and analysis of recommender systems. Journal of Korean Institute of Industrial Engineers, 41(2):185– 208, 2015. (in Korean)
    [2015]
  • [44] Ian Roberts. Text analytics for news and social media: The development of a cloud-based marketplace for text analytics. Journal of Digital Media Management, 3(2):158–170, 2014.
    [2014]
  • [43] Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson. Neural collaborative filtering vs. matrix factorization revisited. In Fourteenth ACM Conference on Recommender Systems, page 240–248. Association for Computing Machinery.
  • [42] Jesus Bobadilla, Santiago Alonso, and Antonio Hernando. Deep learning architecture for collaborative filtering recommender systems. Applied Sciences, 10(7):2441, 2020.
    [2020]
  • [41] Sheng Li, Jaya Kawale, and Yun Fu. Deep collaborative filtering via marginalized denoising auto-encoder. In Proceedings of the 24th ACM international on conference on information and knowledge management, pages 811–820.
  • [40] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web, pages 173–182. International World Wide Web Conferences Steering Committee.
  • [3] Tao Zhou, Zolt´an Kuscsik, Jian-Guo Liu, Mat´uˇs Medo, Joseph Rushton Wakeling, and Yi- Cheng Zhang. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences, 107(10):4511–4515, 2010.
    [2010]
  • [39] Dun Liu and Xiaoqing Ye. A matrix factorization based dynamic granularity recommendation with three-way decisions. Knowledge-Based Systems, 191:105243, 2020.
    [2020]
  • [38] Sebastian Schelter, Christoph Boden, and Volker Markl. Scalable similarity-based neighborhood methods with mapreduce, 2012.
    [2012]
  • [37] H. Yu, C. Hsieh, S. Si, and I. Dhillon. Scalable coordinate descent approaches to parallel matrix factorization for recommender systems. In 2012 IEEE 12th International Conference on Data Mining, pages 765–774.
  • [36] Robin Anil, Gokhan Capan, Isabel Drost-Fromm, Ted Dunning, Ellen Friedman, Trevor Grant, Shannon Quinn, Paritosh Ranjan, Sebastian Schelter, and Ozg¨ur Yılmazel. Apache mahout: Machine learning on distributed dataflow systems. Journal of Machine Learning Research, 21(127):1–6, 2020.
    [2020]
  • [35] Pigi Kouki, Shobeir Fakhraei, James Foulds, Magdalini Eirinaki, and Lise Getoor. Hyper: A flexible and extensible probabilistic framework for hybrid recommender systems, 2015.
    [2015]
  • [34] Mehrbakhsh Nilashi, Othman Ibrahim, and Karamollah Bagherifard. A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications, 92:507–520, 2018.
    [2018]
  • [33] Augusto Q. Macedo, Leandro B. Marinho, and Rodrygo L.T. Santos. Context-aware event recommendation in event-based social networks, 2015.
    [2015]
  • [32] Gediminas Adomavicius and Alexander Tuzhilin. Context-aware recommender systems, pages 217–253. Springer, 2011.
    [2011]
  • [31] Tae-Gyu Hwang and Sung Kwon Kim. Movie recommendation algorithm using genre correlation. KIISE Transactions on Computing Practices, 26(9):429–434, 2020. (in Korean)
    [2020]
  • [30] Tae-Gyu Hwang and Sung Kwon Kim. Multi-label bias-based predictor. In 2019 International Conference on Platform Technology and Service (PlatCon), pages 1–5. IEEE.
  • [2] Deuk Hee Park, Hyea Kyeong Kim, Il Young Choi, and Jae Kyeong Kim. A literature review and classification of recommender systems research. Expert systems with applications, 39(11):10059–10072, 2012.
    [2012]
  • [29] Michael J Pazzani and Daniel Billsus. Content-based recommendation systems, pages 325–341. Springer, 2007.
    [2007]
  • [28] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Incremental singular value decomposition algorithms for highly scalable recommender systems. In Fifth international conference on computer and information science, volume 1, pages 1–6.
  • [27] Yehuda Koren. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 447–456.
  • [26] Xin Luo, Mengchu Zhou, Yunni Xia, and Qingsheng Zhu. An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Transactions on Industrial Informatics, 10(2):1273–1284, 2014.
    [2014]
  • [25] Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37, 2009.
    [2009]
  • [24] Jing Jiang, Jie Lu, Guangquan Zhang, and Guodong Long. Scaling-up item-based collaborative filtering recommendation algorithm based on hadoop. In 2011 IEEEWorld Congress on Services, pages 490–497. IEEE.
  • [23] Yi Ding and Xue Li. Time weight collaborative filtering. In Proceedings of the 14th ACM international conference on Information and knowledge management, pages 485–492.
  • [22] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285–295.
  • [21] Zhi-Dan Zhao and Ming-Sheng Shang. User-based collaborative-filtering recommendation algorithms on hadoop. In 2010 third international conference on knowledge discovery and data mining, pages 478–481. IEEE.
  • [20] Jian Wei, Jianhua He, Kai Chen, Yi Zhou, and Zuoyin Tang. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 69:29–39, 2017.
    [2017]
  • [1] Hinrich Sch¨utze, Christopher D Manning, and Prabhakar Raghavan. Introduction to information retrieval, volume 39. Cambridge University Press Cambridge, 2008.
    [2008]
  • [19] Jes´uS Bobadilla, Fernando Ortega, Antonio Hernando, and Jes´us Bernal. A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-based systems, 26:225–238, 2012.
    [2012]
  • [18] Zan Huang, Hsinchun Chen, and Daniel Zeng. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems (TOIS), 22(1):116–142, 2004.
    [2004]
  • [17] Monika Singh. Scalability and sparsity issues in recommender datasets: a survey. Knowledge and Information Systems, 62(1):1–43, 2020.
    [2020]
  • [16] Sebastian Schelter and Sean Owen. Collaborative filtering with apache mahout. In Proc. of ACM RecSys Challenge.
  • [15] Konstantin Shvachko, Hairong Kuang, Sanjay Radia, and Robert Chansler. The hadoop distributed file system. In 2010 IEEE 26th symposium on mass storage systems and technologies (MSST), pages 1–10. IEEE.
  • [14] Fidel Cacheda, V´ıctor Carneiro, Diego Fern´andez, and Vreixo Formoso. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Transactions on the Web (TWEB), 5 (1):1–33, 2011.
    [2011]
  • [13] G´abor Tak´acs, Istv´an Pil´aszy, Botty´an N´emeth, and Domonkos Tikk. Scalable collaborative filtering approaches for large recommender systems. The Journal of Machine Learning Research, 10:623–656, 2009.
    [2009]
  • [12] Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, and Rong Pan. Large-scale parallel collaborative filtering for the netflix prize. In International conference on algorithmic applications in management, pages 337–348. Springer.
  • [11] Yongjian Yang, Yuanbo Xu, EnWang, Jiayu Han, and Zhiwen Yu. Improving existing collaborative filtering recommendations via serendipity-based algorithm. IEEE Transactions on Multimedia, 20(7):1888–1900, 2017.
    [2017]
  • [10] Yuan Cao Zhang, Diarmuid ´O S´eaghdha, Daniele Quercia, and Tamas Jambor. Auralist: introducing serendipity into music recommendation, 2012.
    [2012]
  • The orbitofrontal cortex and reward . Cerebral Cortex , 10 ( 3 ) :284 ? 294
  • Randomized methods for computing low-rank approximations of matrices
  • Nn-svg : Publication-ready neural network architecture schematics
    Alexander LeNail 4 ( 33 ) :747 [2019]
  • Linear model selection by cross-validation
    Jun Shao 88 ( 422 ) :486 ? 494 [1993]
  • Cross-validatory choice and assessment of statistical predictions
    Mervyn Stone 36 ( 2 ) :111 ? 133 [1974]
  • Artificial neural networks
    John J Hopfield 4 ( 5 ) : 3 ? 10 [1988]