We use cookies to improve your experience with our site.

Indexed in:

SCIE, EI, Scopus, INSPEC, DBLP, CSCD, etc.

Submission System
(Author / Reviewer / Editor)
Qing-Mei Tan, Xu-Na Wang. Multi-Attribute Preferences Mining Method for Group Users with the Process of Noise Reduction[J]. Journal of Computer Science and Technology, 2021, 36(4): 944-960. DOI: 10.1007/s11390-021-0102-0
Citation: Qing-Mei Tan, Xu-Na Wang. Multi-Attribute Preferences Mining Method for Group Users with the Process of Noise Reduction[J]. Journal of Computer Science and Technology, 2021, 36(4): 944-960. DOI: 10.1007/s11390-021-0102-0

Multi-Attribute Preferences Mining Method for Group Users with the Process of Noise Reduction

Funds: This work was supported by the Major Project of National Social Science Foundation of China under Grant No. 20&ZD127.
More Information
  • Author Bio:

    Qing-Mei Tan received his Ph.D. degree in technology economy and management from Hohai University, Nanjing, in 1998. He is currently a professor at Nanjing University of Aeronautics and Astronautics, Nanjing. His research interests are in the areas of data mining, information systems, and technology innovation.

  • Corresponding author:

    Xu-Na Wang E-mail: Xuna@nuaa.edu.cn

  • Received Date: October 14, 2019
  • Revised Date: June 08, 2021
  • Published Date: July 04, 2021
  • Traditional researches on user preferences mining mainly explore the user’s overall preferences on the project, but ignore that the fundamental motivation of user preferences comes from their attitudes on some attributes of the project. In addition, traditional researches seldom consider the typical preferences combination of group users, which may have influence on the personalized service for group users. To solve this problem, a method with noise reduction for group user preferences mining is proposed, which focuses on mining the multi-attribute preference tendency of group users. Firstly, both the availability of data and the noise interference on preferences mining are considered in the algorithm design. In the process of generating group user preferences, a new path is used to generate preference keywords so as to reduce the noise interference. Secondly, the Gibbs sampling algorithm is used to estimate the parameters of the model. Finally, using the user comment data of several online shopping websites as experimental objects, the method is used to mine the multi-attribute preferences of different groups. The proposed method is compared with other methods from three aspects of predictive ability, preference mining ability and preference topic similarity. Experimental results show that the method is significantly better than other existing methods.
  • [1]
    Guo Y, Lu Z, Kuang H, Wang C. Information avoidance behavior on social network sites:Information irrelevance, overload, and the moderating role of time pressure. International Journal of Information Management, 2020, 52:Article No. 102067. DOI: 10.1016/j.ijinfomgt.2020.102067.
    [2]
    Saxena D, Lamest M. Information overload and coping strategies in the big data context:Evidence from the hospitality sector. Journal of Information Science, 2018, 44(3):287-297. DOI: 10.1177/0165551517693712.
    [3]
    Peng J, Wang T, Chen Y, Liu T, Xu W. User recommendation based on cross-platform online social networks. Journal on Communications, 2018, 39(03):147-158. DOI:10.11959/j.issn.1000-436x.2018044. (in Chinese)
    [4]
    Tao L, Cao J, Liu F. Dynamic feature weighting based on user preference sensitivity for recommender systems. Knowledge-Based Systems, 2018, 149:61-75. DOI: 10.1016/j.knosys.2018.02.019.
    [5]
    Chai H, Lei J, Fang M. Estimating Bayesian networks parameters using EM and Gibbs sampling. Procedia Computer Science, 2017, 111:160-166. DOI: 10.1016/j.procs.2017.06.023.
    [6]
    Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation. Journal of Machine Learning Research, 2003, 3(4):993-1022. DOI: 10.1162/jmlr.2003.3.4-5.993.
    [7]
    Vu H Q, Li G, Law R. Discovering implicit activity preferences in travel itineraries by topic modeling. Tourism Management, 2019, 75:435-446. DOI: 10.1016/j.tourman.2019.06.011.
    [8]
    Zhang Y, Wei H, Ran Y, Deng Y, Liu D. Drawing openness to experience from user generated contents:An interpretable data-driven topic modeling approach. Expert Systems with Applications, 2020, 144:Article No. 113073. DOI: 10.1016/j.eswa.2019.113073.
    [9]
    Schwarz C. Ldagibbs:A command for topic modeling in Stata using latent Dirichlet allocation. The Stata Journal, 2018, 18(1):101-117. DOI: 10.1177/1536867X1801800107.
    [10]
    Abdar M, Yen N Y. Analysis of user preference and expectation on shared economy platform:An examination of correlation between points of interest on Airbnb. Computers in Human Behavior, 2018, 107:Article No. 105730. DOI: 10.1016/j.chb.2018.09.039.
    [11]
    Kim J E, Kessler L, McCauley Z, Niiyama I, Boyle L N. Human factors considerations in designing a personalized mobile dialysis device:An interview study. Applied Ergonomics, 2020, 85:103003. DOI: 10.1016/j.apergo.2019.103003.
    [12]
    Li Z, Hensher D A, Ho C. An empirical investigation of values of travel time savings from stated preference data and revealed preference data. Transportation Letters, 2020, 12(3):166-171. DOI: 10.1080/19427867.2018.1546806.
    [13]
    Feng C, Liang J, Song P, Wang Z. A fusion collaborative filtering method for sparse data in recommender systems. Information Sciences, 2020, 521:365-379. DOI: 10.1016/j.ins.2020.02.052.
    [14]
    Hong M, Jung J J. Multi-sided recommendation based on social tensor factorization. Information Sciences, 2018, 447:140-156. DOI: 10.1016/j.ins.2018.03.019.
    [15]
    Geng Y, Li Q, Liang M, Chi C Y, Tan J, Huang H. Local-density subspace distributed clustering for highdimensional data. IEEE Transactions on Parallel and Distributed Systems, 2020, 31(8):1799-1814. DOI: 10.1109/TPDS.2020.2975550.
    [16]
    Mowlaei M E, Abadeh M S, Keshavarz H. Aspect-based sentiment analysis using adaptive aspect-based lexicons. Expert Systems with Applications, 2020, 148:Article No. 113234. DOI: 10.1016/j.eswa.2020.113234.
    [17]
    Pujahari A, Sisodia D S. Pair-wise preference relation based probabilistic matrix factorization for collaborative filtering in recommender system. Knowledge-Based Systems, 2020, 196:Article No. 105798. DOI: 10.1016/j.knosys.2020.105798.
    [18]
    Logesh R, Subramaniyaswamy V, Malathi D, Sivaramakrishnan N, Vijayakumar V. Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Computing and Applications, 2020, 32:2141-2164. DOI: 10.1007/s00521-018-3891-5.
    [19]
    Yang X, Zhou S, Cao M. An approach to alleviate the sparsity problem of hybrid collaborative filtering based recommendations:The product-attribute perspective from user reviews. Mobile Networks and Applications, 2020, 25:376-390. DOI: 10.1007/s11036-019-01246-2.
    [20]
    Li W, Li J, Liu X, Dong L. Two fast vector-wise update algorithms for orthogonal nonnegative matrix factorization with sparsity constraint. Journal of Computational and Applied Mathematics, 2020, 375:Article No. 112785. DOI: 10.1016/j.cam.2020.112785.
    [21]
    Lu H, Sang X, Zhao Q, Lu J. Community detection algorithm based on nonnegative matrix factorization and pairwise constraints. Physica A:Statistical Mechanics and its Applications, 2020, 545:Article No. 123491. DOI: 10.1016/j.physa.2019.123491.
    [22]
    Khan Z, Iltaf N, Afzal H, Abbas H. Enriching non-negative matrix factorization with contextual embeddings for recommender systems. Neurocomputing, 2020, 380:246-258. DOI: 10.1016/j.neucom.2019.09.080.
    [23]
    Kim H, Kim H K, Cho S. Improving spherical k-means for document clustering:Fast initialization, sparse centroid projection, and efficient cluster labeling. Expert Systems with Applications, 2020, 150:Article No. 113288. DOI: 10.1016/j.eswa.2020.113288.
    [24]
    Zhou B, Funaki Y, Horiuchi H, Tohsaki A. Nonlocalized clustering and evolution of cluster structure in nuclei. Frontiers of Physics, 2020, 15(1):Article No. 14401. DOI: 10.1007/s11467-019-0917-0.
    [25]
    Luarn P, Kuo H C, Lin H W, Chiu Y P, Jhan Y C. Analyzing user preferences using Facebook fan pages. Interfaces, 2018, 48(2):166-175. DOI: 10.1287/inte.2017.0919.
    [26]
    Zhang X, Liu H, Chen X, Zhong J, Wang D. A novel hybrid deep recommendation system to differentiate user's preference and item's attractiveness. Information Sciences, 2020, 519:306-316. DOI: 10.1016/j.ins.2020.01.044.
    [27]
    Guo W, Liu F. Research on collaborative filtering personalized recommendation algorithm based on deep learning optimization. In Proc. the 2019 International Conference on Robots & Intelligent System, June 2019, pp.90-93. DOI: 10.1109/ICRIS.2019.00031.
    [28]
    Han J, Zheng L, Xu Y, Zhang B. Adaptive deep modeling of users and items using side information for recommendation. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(3):737-748. DOI: 10.1109/TNNLS.2019.2909432.
    [29]
    Chambua J, Niu Z, Zhu Y. User preferences prediction approach based on embedded deep summaries. Expert Systems with Applications, 2019, 132:87-98. DOI: 10.1016/j.eswa.2019.04.047.
    [30]
    Laohakiat S, Phimoltares S, Lursinsap C. A Clustering algorithm for stream data with LDA-based unsupervised localized dimension reduction. Information Sciences, 2017, 381:104-123. DOI: 10.1016/j.ins.2016.11.018.
    [31]
    Kyaw N E E, Wai T T. Inferring user preferences using reviews for rating prediction. In Proc. the 2019 International Conference on Advanced Information Technologies, November 2019, pp.194-199. DOI: 10.1109/AITC.2019.8921179.
    [32]
    Chen L, Yan D, Wang F. User perception of sentimentintegrated critiquing in recommender systems. International Journal of Human-Computer Studies, 2019, 121:4-20. DOI: 10.1016/j.ijhcs.2017.09.005.
    [33]
    Lei X, Qian X, Zhao G. Rating prediction based on social sentiment from textual reviews. IEEE Transactions on Multimedia, 2016, 18(9):1910-1921. DOI: 10.1109/TMM.2016.2575738.
    [34]
    Chen C T, Ren J T. Forum latent Dirichlet allocation for user interest discovery. Knowledge-Based Systems, 2017, 126:1-7. DOI: 10.1016/j.knosys.2017.04.006.
    [35]
    Pu X, Wu G, Yuan C. User-aware topic modeling of online reviews. Multimedia Systems, 2019, 25(1):59-69. DOI: 10.1007/s00530-017-0557-6.
    [36]
    Liang S, Yilmaz E, Kanoulas E. Collaboratively tracking interests for user clustering in streams of short texts. IEEE Transactions on Knowledge and Data Engineering, 2018, 31(2):257-272. DOI: 10.1109/TKDE.2018.2832211.
    [37]
    Li J, Ma X. Research on hot news discovery model based on user interest and topic discovery. Cluster Computing, 2019, 22(4):8483-8491. DOI: 10.1007/s10586-018-1880-1.
    [38]
    Ma X, Lei X, Zhao G, Qian X. Rating prediction by exploring user's preference and sentiment. Multimedia Tools and Applications, 2018, 77(6):6425-6444. DOI: 10.1007/s11042-017-4550-z.
    [39]
    Chen Z, Liu B. Mining topics in documents:Standing on the shoulders of big data. In Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2014, pp.1116-1125. DOI: 10.1145/2623330.2623622.
    [40]
    Hofmann T. Probabilistic latent semantic indexing. In Proc. the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 1999, pp.50-57. DOI: 10.1145/312624.312649.
    [41]
    Ge B, Zheng W, Yang G M, Lu Y, Zheng H J. Microblog topic mining based on a combined TF-IDF and LDA topic model. In Proc. the 2018 International Conference on Automatic Control, Mechatronics and Industrial Engineering, October 2018, pp.29-31. DOI: 10.1201/9780429468605-40.
    [42]
    Baek J W, Chung K Y. Multimedia recommendation using Word2Vec-based social relationship mining. Multimedia Tools and Applications. DOI: 10.1007/s11042-019-08607-9.
    [43]
    Landauer T K, McNamara D S, Dennis S, Kintsch W. Handbook of Latent Semantic Analysis (1st edition). Routledge, 2014.
    [44]
    Peng Y, Wan C X, Jiang T J, Liu D X, Liao G Q. Extracting product aspect and user opinions based on semantic constrained LDA model. Journal of Software, 2017, 28(03):676-693. DOI:10.13328/j.cnki.jos.005154. (in Chinese)
    [45]
    Bu Y, Zou S, Liang Y, Venugopal V. Estimation of KL divergence:Optimal minimax rate. IEEE Transactions on Information Theory, 2018, 64(4):2648-2674. DOI: 10.1109/TIT.2018.2805844.
    [46]
    Grosse I, Bernaola-Galván P, Carpena P, Román-Roldán R, Oliver J, Stanley, H E. Analysis of symbolic sequences using the Jensen-Shannon divergence. Physical Review E, 2002, 65(4):Article No. 041905. DOI: 10.1103/PhysRevE.65.041905.
  • Others

Catalog

    Article views (34) PDF downloads (0) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return