[1] ULLMAN J D. Principles of Database and Knowledge-Base Systems [M]. New York: Computer Science Press, 1988. [2] ALAVI M, LEIDNER D E. Knowledge management and knowledge management systems: Conceptual foundations and research issues[J]. MIS Quarterly, 2001, 25(1): 107-136. [3] GIBONEY J S, BROWN S A, LOWRY P B, et al. User acceptance of knowledge-based system recommendations: explanations, arguments, and fit[J]. Decision Support Systems, 2015, 72(C):1-10. [4] VELSQUEZ J D, PALADE V. Building a knowledge base for implementing a web-based computerized recommendation system[J]. International Journal on Artificial Intelligence Tools, 2007, 16(5): 793-828. [5] KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37. [6] SARWAR B, KARYPIS G, KONSTAN J, et al. Application of dimensionality reduction in recommender systems[EB/OL]. ACM WebKDD-2000, [2018-11-17] http://glaros.dtc.umn.edu/gkhome/node/122. [7] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]// International Conference on World Wide Web. Hong Kong, China: ACM Press, 2001: 285-295. [8] LINDEN G, SMITH B, YORK J. Amazon.com recommendations: item-to-item collaborative filtering[J]. IEEE Internet Computing, 2003, 7(1):76-80. [9] ZENG C, XING C X, ZHOU L Z. Survey of personalization technology[J]. Journal of Software, 2002, 13(10): 1952-1961. [10] RICCI F, ROKACH L, SHAPIRA B, et al. Recommender Systems Handbook [M]. Springer, 2011. [11] ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749. [12] BENNETT J, LANNING S, NETFLIX N. The Netflix Prize[C]// KDD Cup and Workshop in Conjunction with KDD. 2009. [13] MASSA P, AVESANI P. Trust-aware recommender systems[C]// Proceedings of the Conference on Recommender Systems. Minnesota, USA: ACM Press, 2007: 17-24. [14] GOLDBERG D, NICHOLS D, OKI B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992, 35(12): 61-70. [15] BURKE R. Hybrid recommender systems: Survey and experiments[J]. User Modeling and User-Adapted Interaction, 2002, 12(4): 331-370. [16] KOREN Y. Factorization meets the neighborhood: A multifaceted collaborative filtering model[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, USA: ACM Press, 2008: 426-434. [17] KOREN Y. Collaborative filtering with temporal dynamics[J]. Communications of the ACM, 2010, 53(4): 89-97. [18] JAMALI M, ESTER M. A matrix factorization technique with trust propagation for recommendation in social networks[C]// ACM Conference on Recommender Systems. Barcelona, Spain: ACM Press, 2010: 135-142. [19] CHEN T, ZHANG W, LU Q, et al. SVDFeature: A toolkit for feature-based collaborative filtering[J]. Journal of Machine Learning Research, 2012, 13(1): 3619-3622. [20] OSMANLI O N, 倫SMAIL HAKKI TOROSLU. Using tag similarity in SVD-based recommendation systems[C]// International Conference on Application of Information and Communication Technologies. Baku, Azerbaijan: IEEE Press, 2011: 1-4. [21] XU Z, CHANG X, XU F, et al. L1/2 regularization: A thresholding representation theory and a fast solver[J]. IEEE Transactions on Neural Networks & Learning Systems, 2012, 23(7): 1013-1027. [22] RUDER S. An overview of gradient descent optimization algorithms[J]. Machine Learning, 2016: arXiv:1609.04747 [cs.LG]. [23] SALAKHUTDINOV R, MNIH A. Probabilistic matrix factorization[C]// International Conference on Neural Information Processing Systems. Vancouver, Canada: Curran Associates Inc., 2007: 1257-1264. [24] LEE D D, SEUNG H S. Algorithms for non-negative matrix factorization[C]// International Conference on Neural Information Processing Systems. MIT Press, 2000:535-541. [25] SHARIFI Z, REZGHI M, NASIRI M. A new algorithm for solving data sparsity problem based-on Non negative matrix factorization in recommender systems[C]// International Conference on Computer and Knowledge Engineering. Mashhad, Iran: IEEE Press, 2014:56-61. [26] LUO X, ZHOU M, XIA Y, et al. An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems[J]. IEEE Transactions on Industrial Informatics, 2014, 10(2): 1273-1284. [27] WANG L, MENG X, ZHANG Y, et al. Applying HOSVD to alleviate the sparsity problem in context-aware recommender systems[J]. Chinese Journal of Electronics, 2013, 22(4): 773-778. [28] KUTTY S, CHEN L, NAYAK R. A people-to-people recommendation system using tensor space models[C]// ACM Symposium on Applied Computing. Trento, Italy: ACM Press, 2012: 187-192. [29] KARATZOGLOU A, AMATRIAIN X, BALTRUNAS L, et al. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering[C]// ACM Conference on Recommender Systems. Barcelona, Spain: ACM Press, 2010: 79-86. [30] CREMONESI P, TURRIN R, TURRIN R. Performance of recommender algorithms on top-n recommendation tasks[C]// ACM Conference on Recommender Systems. Barcelona, Spain: ACM Press, 2010: 39-46. [31] WILLMOTT C J, MATSUURA K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance [J]. Climate Research, 2005, 30(1): 79-82. [32] CHAI T, DRAXLER R R. Root mean square error (RMSE) or mean absolute error (MAE)?[J]. Geoscientific Model Development, 2014, 7(3):1247-1250. [33] TAK, CS G, SZY I, et al. Matrix factorization and neighbor based algorithms for the netflix prize problem[C]// ACM Conference on Recommender Systems. Lausanne, Switzerland: ACM Press, 2008: 267-274. [34] OTT P. Incremental Matrix Factorization for Collaborative Filtering[M]// Science, Technology and Design 01/2008, Anhalt University of Applied Sciences. [35] LEE D D, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788791.
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