|
[1] N. Agarwal, H. Liu, L. Tang, P.S. Yu, Identifying the influential bloggers in a community, Proceedings of the 2008 International Conference on Web Search and Data Mining, Palo Alto, California, USA, 2008, pp. 207-218. [2] N. Agarwal, H. Liu, L. Tang, P.S. Yu, Identifying the influential bloggers in a community, Proceedings of the 2008 international conference on web search and data mining, 2008, pp. 207-218. [3] L. Akritidis, D. Katsaros, P. Bozanis, Identifying the Productive and Influential Bloggers in a Community, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 41 (5), 2011, pp. 759-764. [4] E. Bakshy, J.M. Hofman, W.A. Mason, D.J. Watts, Everyone's an influencer: quantifying influence on twitter, Proceedings of the fourth ACM international conference on Web search and data mining, 2011, pp. 65-74. [5] L. Baltrunas, B. Ludwig, F. Ricci, Matrix factorization techniques for context aware recommendation, Proceedings of the fifth ACM conference on Recommender systems, 2011, pp. 301-304. [6] D.M. Blei, A.Y. Ng, M.I. Jordan, Latent dirichlet allocation, J. Mach. Learn. Res., 3, 2003, pp. 993-1022. [7] J.S. Breese, D. Heckerman, C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, 1998, pp. 43-52. [8] R. Burke, Hybrid recommender systems: Survey and experiments, User modeling and user-adapted interaction, 12 (4), 2002, pp. 331-370. [9] E. Castronova, T.L. Ross, M. Bell, J.J. Cummings, M. Falk, A Test of the Law of Demand in a Virtual World: Exploring the Petri Dish, Discoveries in Gaming and Computer-Mediated Simulations: New Interdisciplinary Applications, 2011, pp. 301. [10] K.-Y. Chen, H.-Y. Liao, J.-H. Chen, D.-R. Liu, Virtual Goods Recommendations in Virtual Worlds, The Scientific World Journal, 2015 (Article ID 523174), 2015, pp. [11] W. Chen, Y. Wang, S. Yang, Efficient influence maximization in social networks, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, Paris, France, 2009, pp. 199-208. [12] K.W. Church, P. Hanks, Word association norms, mutual information, and lexicography, Computational linguistics, 16 (1), 1990, pp. 22-29. [13] D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, S. Suri, Feedback effects between similarity and social influence in online communities, Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008, pp. 160-168. [14] P. Cui, F. Wang, S. Liu, M. Ou, S. Yang, L. Sun, Who should share what?: item-level social influence prediction for users and posts ranking, Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 2011, pp. 185-194. [15] T. Domina, S.-E. Lee, M. MacGillivray, Understanding factors affecting consumer intention to shop in a virtual world, Journal of Retailing and Consumer Services, 19 (6), 2012, pp. 613-620. [16] N.E. Friedkin, A structural theory of social influence, Cambridge University Press, 2006. [17] J. Golbeck, B. Parsia, J. Hendler, Trust networks on the semantic web, Springer, 2003. [18] A. Goyal, F. Bonchi, L.V. Lakshmanan, Learning influence probabilities in social networks, Proceedings of the third ACM international conference on Web search and data mining, 2010, pp. 241-250. [19] C. Gray, M. Beattie, H. Belay, S. Hill, N. Lerch, Personalized online search for fashion products, Systems and Information Engineering Design Symposium (SIEDS), 2015, 2015, pp. 91-96. [20] D. Gruhl, R. Guha, D. Liben-Nowell, A. Tomkins, Information diffusion through blogspace, Proceedings of the 13th international conference on World Wide Web, New York, NY, USA, 2004, pp. 491-501. [21] Y. Guo, S. Barnes, Purchase behavior in virtual worlds: An empirical investigation in Second Life, Information & Management, 48 (7), 2011, pp. 303-312. [22] P.O. Hoyer, Non-negative matrix factorization with sparseness constraints, The Journal of Machine Learning Research, 5, 2004, pp. 1457-1469. [23] C.-J. Hsieh, I.S. Dhillon, Fast coordinate descent methods with variable selection for non-negative matrix factorization, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011, pp. 1064-1072. [24] M. Jamali, M. Ester, Modeling and Comparing the Influence of Neighbors on the Behavior of Users in Social and Similarity Networks, Proceedings of the 2010 IEEE International Conference on Data Mining Workshops, 2010, pp. 336-343. [25] T. Jin-Tao, W. Ting, W. Ji, Measuring the influence of social networks on information diffusion on blogspheres, Machine Learning and Cybernetics, 2009 International Conference on, 2009, pp. 3492-3498. [26] T. Joachims, A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization, in, DTIC Document, 1996. [27] S. Junyi. Chinese text segmentation: built to be the best Python Chinese word segmentation module., https://github.com/fxsjy/jieba. [28] D. Kempe, J. Kleinberg, #201, v. Tardos, Influential nodes in a diffusion model for social networks, Proceedings of the 32nd international conference on Automata, Languages and Programming, Lisbon, Portugal, 2005, pp. 1127-1138. [29] B.-k. Kim, D.-H. Lee, LSF: a new buffer replacement scheme for flash memory-based portable media players, IEEE Transactions on Consumer Electronics, 59 (1), 2013, pp. 130-135. [30] J.M. Kleinberg, Authoritative sources in a hyperlinked environment, J. ACM, 46 (5), 1999, pp. 604-632. [31] J.A. Konstan, B.N. Miller, D. Maltz, J.L. Herlocker, L.R. Gordon, J. Riedl, GroupLens: applying collaborative filtering to Usenet news, Communications of the ACM, 40 (3), 1997, pp. 77-87. [32] C.-H. Lai, D.-R. Liu, Integrating Knowledge Flow Mining and Collaborative Filtering to Support Document Recommendation, Journal of Systems and Software, 82, 2009, pp. 2023-2037. [33] C.-H. Lai, D.-R. Liu, C.-S. Lin, Novel personal and group-based trust models in collaborative filtering for document recommendation, Information Sciences, 239, 2013, pp. 31-49. [34] D.D. Lee, H.S. Seung, Algorithms for non-negative matrix factorization, Advances in neural information processing systems, 2001, pp. 556-562. [35] B. Li, Q. Yang, X. Xue, Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction, Proceedings of the 21st international jont conference on Artifical intelligence, Pasadena, California, USA, 2009, pp. 2052-2057. [36] B. Li, X. Zhu, R. Li, C. Zhang, Rating Knowledge Sharing in Cross-Domain Collaborative Filtering, IEEE Transactions on Cybernetics, 45 (5), 2015, pp. 1068-1082. [37] Q. Li, B.M. Kim, An approach for combining content-based and collaborative filters, Proceedings of the sixth international workshop on Information retrieval with Asian languages, Sapporo, Japan, 2003, pp. 17-24. [38] H.-Y. Liao, K.-Y. Chen, D.-R. Liu, Intensify Avatars' Immersion In The Virtual Worlds Through A Novel Friend Prediction Model, The 17th Asia-Pacific Decision Sciences Institute Conference Chiang Mai, Thailand, 2012, pp. [39] G. Linden, B. Smith, J. York, Amazon. com recommendations: Item-to-item collaborative filtering, Internet Computing, IEEE, 7 (1), 2003, pp. 76-80. [40] D.-R. Liu, K.-Y. Chen, Y.-C. Chou, J.-H. Lee, An Online Activity Recommendation Approach based on the Dynamic Adjustment of Recommendation Lists, AAI 2017 6th International Congress on Advanced Applied Informatics; 2017 8th International Conference on E-Service and Knowledge Management, Hamamatsu, Japan, 2017, pp. [41] D.-R. Liu, C.-H. Lai, Y.-T. Chen, Document recommendations based on knowledge flows: A hybrid of personalized and group-based approaches, J. Am. Soc. Inf. Sci. Technol., 63 (10), 2012, pp. 2100-2117. [42] D.-R. Liu, C.-H. Lai, H. Chiu, Sequence-based trust in collaborative filtering for document recommendation, International Journal of Human-Computer Studies, 69 (9), 2011, pp. 587-601. [43] D.-R. Liu, C.-H. Liou, C.-C. Peng, H.-C. Chi, Hybrid content filtering and reputation-based popularity for recommending blog articles, Online Information Review, 38 (6), 2014, pp. 788-805. [44] D.-R. Liu, H. Omar, C.-H. Liou, H.-C. Chi, C.-H. Hsu, Recommending blog articles based on popular event trend analysis, Information Sciences, 305, 2015, pp. 302-319. [45] D.-R. Liu, Y.-Y. Shih, Integrating AHP and data mining for product recommendation based on customer lifetime value, Information & Management, 42 (3), 2005, pp. 387-400. [46] D.-R. Liu, P.-Y. Tsai, P.-H. Chiu, Personalized recommendation of popular blog articles for mobile applications, Information Sciences, 181, 2011, pp. 1552-1572. [47] D.R. Liu, C.H. Lai, Y.T. Chen, Document recommendations based on knowledge flows: A hybrid of personalized and group‐based approaches, Journal of the American Society for Information Science and Technology, 63 (10), 2012, pp. 2100-2117. [48] L. Liu, J. Tang, J. Han, M. Jiang, S. Yang, Mining topic-level influence in heterogeneous networks, Proceedings of the 19th ACM international conference on Information and knowledge management, Toronto, ON, Canada, 2010, pp. 199-208. [49] J. Ludeña-Choez, A. Gallardo-Antolín, Acoustic Event Classification using spectral band selection and Non-Negative Matrix Factorization-based features, Expert Systems with Applications, 46, 2016, pp. 77-86. [50] X. Luo, M. Zhou, Y. Xia, Q. Zhu, An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems, IEEE Transactions on Industrial Informatics, 10 (2), 2014, pp. 1273-1284. [51] H. Ma, M. Jia, D. Zhang, X. Lin, Combining tag correlation and user social relation for microblog recommendation, Information Sciences, 385–386, 2017, pp. 325-337. [52] P. Massa, P. Avesani, Trust-aware collaborative filtering for recommender systems, in: On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE, Springer, 2004, pp. 492-508. [53] P. Massa, P. Avesani, Trust-aware recommender systems, Proceedings of the 2007 ACM conference on Recommender systems, 2007, pp. 17-24. [54] P. Massa, P. Avesani, Trust metrics on controversial users: Balancing between tyranny of the majority, International Journal on Semantic Web and Information Systems (IJSWIS), 3 (1), 2007, pp. 39-64. [55] P. Massa, B. Bhattacharjee, Using trust in recommender systems: an experimental analysis, in: Trust Management, Springer, 2004, pp. 221-235. [56] P.R. Messinger, E. Stroulia, K. Lyons, M. Bone, R.H. Niu, K. Smirnov, S. Perelgut, Virtual worlds—past, present, and future: New directions in social computing, Decision Support Systems, 47 (3), 2009, pp. 204-228. [57] E. Moon, S. Han, A Qualitative Method to Find Influencers Using Similarity-based Approach in the Blogosphere, Proceedings of the 2010 IEEE Second International Conference on Social Computing, 2010, pp. 225-232. [58] R.J. Mooney, L. Roy, Content-based book recommending using learning for text categorization, Proceedings of the fifth ACM conference on Digital libraries, 2000, pp. 195-204. [59] J. O'Donovan, B. Smyth, Trust in recommender systems, Proceedings of the 10th international conference on Intelligent user interfaces, 2005, pp. 167-174. [60] E.J. O'neil, P.E. O'neil, G. Weikum, The LRU-K page replacement algorithm for database disk buffering, ACM SIGMOD Record, 22 (2), 1993, pp. 297-306. [61] W. Pan, E.W. Xiang, N.N. Liu, Q. Yang, Transfer Learning in Collaborative Filtering for Sparsity Reduction, Proceedings of the National Conference on Artificial Intelligence, 2010, pp. [62] M.J. Pazzani, D. Billsus, Content-based recommendation systems, in: B. Peter, K. Alfred, N. Wolfgang (Eds.) The adaptive web, LNCS 4321, Springer-Verlag, 2007, pp. 325-341. [63] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, J. Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, 1994, pp. 175-186. [64] C.v. Rijsbergen, Information Retrieval, Butterworth, 1979. [65] S. Sahebi, P. Brusilovsky, It Takes Two to Tango: An Exploration of Domain Pairs for Cross-Domain Collaborative Filtering, Proceedings of the 9th ACM Conference on Recommender Systems, Vienna, Austria, 2015, pp. 131-138. [66] B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, 2001, pp. 285-295. [67] U. Shardanand, P. Maes, Social information filtering: algorithms for automating "word of mouth", Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Denver, Colorado, United States, 1995, pp. 210-217. [68] H.A. Song, B.-K. Kim, T.L. Xuan, S.-Y. Lee, Hierarchical feature extraction by multi-layer non-negative matrix factorization network for classification task, Neurocomputing, 165, 2015, pp. 63-74. [69] A.S. Tanenbaum, Page Replacement Algorithms, in: Charpter 3.4, Modern Operating Systems (Third Edition), Pearson Prentice-Hall New Jersey, 2009. [70] J. Tang, J. Sun, C. Wang, Z. Yang, Social influence analysis in large-scale networks, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, Paris, France, 2009, pp. 807-816. [71] S. Utz, Social information processing in MUDs: The development of friendships in virtual worlds, Journal of Online behavior, 1 (1), 2000, pp. 2002-2002. [72] C. Wang, D.M. Blei, Collaborative topic modeling for recommending scientific articles, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, San Diego, California, USA, 2011, pp. 448-456. [73] J. Weng, E.-P. Lim, J. Jiang, Q. He, Twitterrank: finding topic-sensitive influential twitterers, Proceedings of the third ACM international conference on Web search and data mining, 2010, pp. 261-270. [74] Wikipedia contributors. Harmonic mean, 21 June 2015 15:59 UTC, https://en.wikipedia.org/w/index.php?title=Harmonic_mean&oldid=667149144 [75] X.-C. Xiong, X. Fang, Z. Ouyang, Y. Jiang, Z.-J. Huang, Y.-K. Zhang, Feature Extraction Approach for Mass Spectrometry Imaging Data Using Non-negative Matrix Factorization, Chinese Journal of Analytical Chemistry, 40 (5), 2012, pp. 663-669. [76] W. Xu, X. Liu, Y. Gong, Document clustering based on non-negative matrix factorization, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, Toronto, Canada, 2003, pp. 267-273. [77] J. Zahalka, S. Rudinac, M. Worring, Interactive Multimodal Learning for Venue Recommendation, IEEE Transactions on Multimedia, 17 (12), 2015, pp. 2235-2244. [78] C. Zeng, C.-X. Xing, L.-Z. Zhou, Similarity measure and instance selection for collaborative filtering, Proceedings of the 12th international conference on World Wide Web, 2003, pp. 652-658.
|