ZHENG Xiaoyao, LUO Yonglong, SUN Liping, et al., “A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques,” Chinese Journal of Electronics, vol. 25, no. 2, pp. 334-340, 2016, doi: 10.1049/cje.2016.03.021
Citation: ZHENG Xiaoyao, LUO Yonglong, SUN Liping, et al., “A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques,” Chinese Journal of Electronics, vol. 25, no. 2, pp. 334-340, 2016, doi: 10.1049/cje.2016.03.021

A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques

doi: 10.1049/cje.2016.03.021
Funds:  This work is supported by National Natural Science Foundation of China (No.61370050), and University Natural Science Research Project of Anhui Province (No.KJ2015A067, No.KJ2014A084).
More Information
  • Corresponding author: LUO Yonglong (corresponding author) was born in 1972. He received his Ph.D. degree on Computer Science and Technology in 2005. Currently, he is the professor and Ph.D. supervisor of Anhui Normal University. His research interests include information security and spatial data processing. (Email:ylluo@ustc.edu.cn)
  • Received Date: 2015-05-07
  • Rev Recd Date: 2015-07-23
  • Publish Date: 2016-03-10
  • Recommender system can efficiently alleviate the information overload problem, but it has been trapped in the recommendation accuracy. We proposed a new recommender system which based on matrix factorization techniques. More factors including contextual information, user ratings and item feature are all taken into consideration. Meanwhile the k-modes algorithm is used to reduce the complexity of matrix operations and increase the relevance of the user-item ratings sub-matrix. Compared with several major existing recommendation approaches, extensive experimental evaluation on publicly available dataset demonstrates that our method enjoys improved recommendation accuracy.
  • loading
  • G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions", IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.6, pp.734-749, 2005.
    G. Adomavicius, R. Sankaranarayanan, S. Sen, et al., "Incorporating contextual information in recommender systems using a multidimensional approach", ACM Transactions on Information Systems (TOIS), Vol.23, No.1, pp.103-145, 2005.
    J. Lu, D.Wu, M. Mao, et al., "Recommender system application developments: A survey", Decision Support Systems, Vol.74, pp.12-32, 2015.
    Y. Koren, R. Bell and C. Volinsky, "Matrix factorization techniques for recommender systems", Computer, Vol.42, No.8, pp.30-37, 2009.
    B. Liu, H. Xiong, S. Papadimitriou, et al., "A general geographical probabilistic factor model for point of interest recommendation", IEEE Transactions on Knowledge and Data Engineering, Vol.27, No.5, pp.1167-1179, 2015.
    B. Li, X. Zhu, R. Li, et al., "Rating knowledge sharing in crossdomain collaborative filtering", IEEE Transactions on Cybernetics, Vol.45, No.5, pp.1054-1068, 2015.
    Q. Liu, Y. Xiong and W. Huang, "Combining user-based and item-based models for collaborative filtering using stacked regression", Chinese Journal of Electronics, Vol.23, No.4, pp.712-717, 2014
    X. Luo, Y. Xia and Q. Zhu, "Applying the learning rate adaptation to the matrix factorization based collaborative filtering", Knowledge-Based Systems, Vol.37, pp.154-164, 2013.
    X. Liu and K. Aberer, "SoCo: A social network aided contextaware recommender system", Proceedings of the 22nd international conference on World Wide Web, Rio de Janeiro, Brazil, pp.781-802, 2013.
    Z. Zhang, K. Zhao and H. Zha, "Inducible regularization for low-rank matrix factorizations for collaborative filtering", Neurocomputing, Vol.97, pp.52-62, 2012.
    A. Mnih and R. Salakhutdinov, "Probabilistic matrix factorization", Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada, pp.1257-1264, 2007.
    Q. Xueming, F. He, Z. Guoshuai, et al., "Personalized recommendation combining user interest and social circle", IEEE Transactions on Knowledge and Data Engineering, Vol.26, No.7, pp.1763-1777, 2014.
    Y. Shi, M. Larson and A. Hanjalic, "Mining contextual movie similarity with matrix factorization for context-aware recommendation", ACM Transactions on Intelligent Systems and Technology (TIST), Vol.4, No.1, pp.16, 2013.
    M. Jamali and M. Ester, "A matrix factorization technique with trust propagation for recommendation in social networks", Proceedings of the Fourth ACM Conference on Recommender Systems, pp.135-142, 2010.
    X. Luo, H. Liu, G. Gou, et al., "A parallel matrix factorization based recommender by alternating stochastic gradient decent", Engineering Applications of Artificial Intelligence, Vol.25, No.7, pp.1403-1412, 2012.
    L. Xin, Z. MengChu, X. Yunni, et al., "An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems", IEEE Transactions on Industrial Informatics, Vol.10, No.2, pp.1273-1284, 2014.
    X. Yang, H. Steck and Y. Liu, "Circle-based recommendation in online social networks", Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, pp.1267-1275, 2012.
    M. Jiang, P. Cui, R. Liu, et al., "Social contextual recommendation", Proceedings of the 21st ACM International Conference on Information and Knowledge Management, Maui, Hawaii, USA, pp.45-54, 2012.
    A.L. Vizine Pereira and E.R. Hruschka, "Simultaneous coclustering and learning to address the cold start problem in recommender systems", Knowledge-Based Systems, Vol.82, pp.11-19, 2015.
    L. Wang, X. Meng and Y. Zhang, "Applying HOSVD to alleviate the sparsity problem in context-aware recommender systems", Chinese Journal of Electronics, Vol.22, No.4, pp.773-778, 2013.
    H. Ma, I. King and M.R. Lyu, "Learning to recommend with social trust ensemble", Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Boston, MA, USA, pp.203-210, 2009.
    G. Linden, B. Smith and J. York, "Amazon.com recommendations: Item-to-item collaborative filtering", IEEE Internet Computing, Vol.7, No.1, pp.76-80, 2003.
    E. Zhong, W. Fan, J. Wang, et al., "ComSoc: Adaptive transfer of user behaviors over composite social network", Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, pp.696-704, 2012.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (681) PDF downloads(1062) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return