Abstract The existing microblog user-tag recommendation methods mostly rely on friends relationship or content to realize recommendation, and the bandwagon relationship existing in microblog (noise problem) can not be discovered and the user label sparse problem is not solved. Therefore, a microblog user-tag recommendation algorithm based on noise reduction relation regularization is presented. The similarity of the user′s and his friends′ interests is measured by the micro-blog theme of users extracted by LDA to reduce the influence of those friends without interests in common with the target user. The noise reduction relationship is taken as the regularization item and it is introduced into user-tag nonnegative matrix factorization model to solve the user-tag sparse problem. The model is optimized and constrained via the Lagrange multiplier method and the KKT conditions, and finally the approximate user-tag matrix for recommended users′ tag is obtained. The experimental results show the proposed method exposes the high quality in recommendation.
Fund:Supported by National Natural Science Foundation of China(No.61602004,61202227)
About author:: (LIU Huiting, born in 1978, Ph.D., associate professor. Her research interests include machine learning and data mining.) (GUO Xiaoxue(Corresponding author), born in 1991, master student. Her research interests include data mining.) (CHENG Lei, born in 1991, master student. His research interests include data mining.) (ZHAO Peng, born in 1976, Ph.D., associate professor. Her research interests include intelligent information processing and machine learning.)
[1] KOPEINIK S, LEX E, SEITLINGER P, et al. Supporting Collaborative Learning with Tag Recommendations: A Real-World Study in an Inquiry-Based Classroom Project // Proc of the 7th International Learning Analytics & Knowledge Conference. New York, USA: ACM, 2017: 409-418. [2] PENG J, ZENG D, ZHAO H M, et al. Collaborative Filtering in Social Tagging Systems Based on Joint Item-Tag Recommendations // Proc of the 19th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2010: 809-818. [3] WU L, YANG L J, YU N H, et al. Learning to Tag // Proc of the International Conference on World Wide Web. New York, USA: ACM, 2009: 361-370. [4] SYMEONIDIS P, NANOPOULOS A, MANOLOPOULOS Y. A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(2): 179-192. [5] SCHKE R, MARINHO L, HOTHO A, et al. Tag Recommendations in Folksonomies // Proc of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases. Berlin, Germany: Springer-Verlag, 2007: 506-514. [6] GUAN Z Y, BU J J, MEI Q Z, et al. Personalized Tag Recommendation Using Graph-Based Ranking on Multi-type Interrelated Objects // Proc of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2009: 540-547. [7] LIU D, HUA X S, YANG L, et al. Tag Ranking // Proc of the International Conference on World Wide Web. New York, USA: ACM, 2009: 351-360. [8] 陈 渊,林 磊,孙承杰,等.一种面向微博用户的标签推荐方法.智能计算机与应用, 2011, 1(5): 21-26. (CHEN Y, LIN L, SUN C J, et al. A Tag Recommendation Me-thod for Microblog Users. Intelligent Computer and Applications, 2011, 1(5): 21-26.) [9] 徐 彬,杨 丹,张 昱,等.面向微博用户标签推荐的关系约束主题模型.计算机科学与探索, 2014, 8(3): 288-295. (XU B, YANG D, ZHANG Y, et al. Relationship Bind Topic Model toward Tag Recommendation for Micro-blog Users. Journal of Frontiers of Computer Science and Technology, 2014, 8(3): 288-295.) [10] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-Based Co-llaborative Filtering Recommendation Algorithms // Proc of the 10th International Conference on World Wide Web. New York, USA: ACM, 2001: 285-295. [11] PAGE L. The PageRank Citation Ranking: Bringing Order to the Web[J/OL]. [2016-12-25]. http://home.iitk.ac.in/~rsundar/cs300/3C.pdf. [12] LEE D D, SEUNG H S. Algorithms for Nonnegative Matrix Facto-rization[J/OL]. [2016-12-25]. http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf. [13] SHEPISEN A, GEMMELL J, MOBASHER B, et al. Personalized Recommendation in Social Tagging Systems Using Hierarchical Clustering // Proc of the ACM Conference on Recommender Systems. New York, USA: ACM, 2008: 171-179. [14] 黄 波,严宣辉,林建辉.基于联合非负矩阵分解的协同过滤推荐算法.模式识别与人工智能, 2016, 29(8): 725-734. (HUANG B, YAN X H, LIN J H. Collaborative Filtering Recommendation Algorithm Based on Joint with Nonnegative Matrix Factorization. Pattern Recognition and Artificial Intelligence, 2016, 29(8): 725-734.) [15] CAI D, HE X F, HAN J W, et al. Graph Regularized Non-negative Matrix Factorization for Data Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1548-1560. [16] LI P, BU J J, CHEN C, et al. Relational Multimanifold Coclu-stering. IEEE Transactions on Cybernetics, 2013, 43(6): 1871-1881. [17] CHOI S M, LEE H, HAN Y S, et al. A Recommendation Model Using the Bandwagon Effect for E-marketing Purposes in IoT. International Journal of Distributed Sensor Networks, 2015. DOI: 10.1155/2015/475163. [18] 邢千里,刘 列,刘奕群,等.微博中用户标签的研究.软件学报, 2015, 26(7): 1626-1637. (XING Q L, LIU L, LIU Y Q, et al. Study on User Tags in Weibo. Journal of Software, 2015, 26(7): 1626-1637.) [19] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003, 3: 993-1022.
[20] VENUGOPAL D, GOGATA V. On Lifting the Gibbs Sampling Algorithm // Proc of the 25th International Conference on Neural Information Processing Systems. Cambridge, USA: The MIT Press, 2012, III: 1655-1663. [21] HAMMOND D K, VANDERGHEYNST P, GRIBONVAL R. Wavelets on Graphs via Spectral Graph Theory. Applied and Computational Harmonic Analysis, 2009, 30(2): 129-150. [22] 朱郁筱,吕琳媛.推荐系统评价指标综述.电子科技大学学报,2012, 41(2): 163-175. (ZHU Y X, L L Y. Evaluation Metrics for Recommender Systems. Journal of University of Electronic Science and Technology of China, 2012, 41(2): 163-175.)
2017, Vol. 30 
