Efficient Mining and Recommendation of Extensive Data Through Collaborative Filtering in E-Commerce: A Survey

  • Authors

    • N Naveen
    • S Ganesh Kumar
    2018-04-25
    https://doi.org/10.14419/ijet.v7i2.24.12077
  • E-commerce, Group recommender system (GRS), recommendation based system, User Preferences.

  • E-Commerce is the most widely used technique nowadays. Buying and selling goods on the Internet has been most admired and frequently utilized. The humongous growth of the content available on the internet has made laborious for users to search and utilize information for classifying the products. Recommendation system regarded as the best way to help the customers in buying the related products. (GRS) group recommender system aims at enhancing the customer’s benefits for buying the products. This paper summarizes the fuzzy tree matching, modeling user preference dynamics, web page recommendation, uncertainty analysis for keywords, recommender system application, temporal topic model for friend recommendation, autocratic decision-making system based on (GRS),modeling user recommender, evaluating recommender system and enhancing (GRS).

     

     

  • References

    1. [1] D. Wu, J. Lu, and G. Zhang, “A fuzzy tree matching-based personalized E-learning recommender system,†IEEE Trans. Fuzzy Syst., vol. 23, no. 6,pp. 2412–2426, Dec. 2015.

      [2] D. Rafailidis and A. Nanopoulos, “Modeling users preference dynamics and side information in recommender systems,†IEEE Trans. Syst., Man, Cybern., Syst., vol. 46, no. 6, pp. 782–792, Jun. 2016.

      [3] T. T. S. Nguyen, H. Y. Lu, and J. Lu, “Web-page recommendation based on Web usage and domain knowledge,†IEEE Trans. Knowl. Data Eng., vol. 26, no. 10, pp. 2574–2587, Oct. 2014.

      [4] J. Xuan, X. Luo, G. Zhang, J. Lu, and Z. Xu, “Uncertainty analysis for the keyword system of Web events,†IEEE Trans. Syst., Man, Cybern.,Syst., vol. 46, no. 6, pp. 829–842, Jun. 2016.

      [5] J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A survey,†Decis. Support Syst., vol. 74,pp. 12–32, Jun. 2015.

      [6] N. Zheng, S. Song, and H. Bao, “A temporal-topic model for friend recommendations in Chinese microblogging systems,†IEEE Trans. Syst.,Man, Cybern., Syst., vol. 45, no. 9, pp. 1245–1253, Sep. 2015.

      [7] S.-M. Chen and B.-H. Tsai, “Autocratic decision making using group recommendations based on intervals of linguistic terms and likelihood based comparison relations,†IEEE Trans. Syst., Man, Cybern., Syst.,vol. 45, no. 2, pp. 250–259, Feb. 2015.

      [8] D. Yang, D. Zhang, V. W. Zheng, and Z. Yu, “Modeling user activity preference by leveraging user spatial-temporal characteristics in LBSNs,†IEEE Trans. Syst., Man, Cybern., Syst., vol. 45, no. 1, pp. 129–142,Jan. 2015.

      [9] A. Gunawardana and G. Sani, “Evaluating Recommender Systems,†in Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, Eds. New York, NY, USA: Springer, 2015, pp. 265–308.

      [10] M. Gartrellet al., “Enhancing Group Recommendation by Incorporating Social Relationship Interactions,†in Proc. 16th ACM Int. Conf. Supporting Group Work (GROUP), Sanibel, FL, USA, 2010, pp. 97–106.

      [11] X. Yang, H. Steck, and Y. Liu, “Circle-based recommendation in online social networks,†in Proc. 18th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2012, pp. 1267–1275.

      [12] X. Yang, Y. Guo, Y. Liu, and H. Steck, “A survey of collaborative filtering based social recommender systems,†Computer Communications, vol. 41, pp. 1–10, 2014.

      [13] J. Tang, X. Hu, and H. Liu, “Social recommendation: a review,†Social Network Analysis and Mining, vol. 3, no. 4, pp. 1113–1133, 2013.

      [14] Y.-M. Li, C.-T.Wu, and C.-Y. Lai, “A social recommender mechanism for e-commerce: combining similarity, trust, and relationship,†Decision Support Systems, vol. 55, no. 3, pp. 740–752, 2013.

      [15] H. Ma, I. King, and M. R. Lyu, “Learning to recommend with social trust ensemble,†in Proc. 32nd Int. ACM SIGIR Conf. Research and Development in Information Retrieval, 2009, pp. 203–210.

      [16] T. Padmapriya and V. Saminadan, “Improving Throughput for Downlink Multi user MIMO-LTE Advanced Networks using SINR approximation and Hierarchical CSI feedbackâ€, International Journal of Mobile Design Network and Innovation- Inderscience Publisher, ISSN : 1744-2850 vol. 6, no.1, pp. 14-23, May 2015.

      [17] S.V.Manikanthan and K.srividhya "An Android based secure access control using ARM and cloud computing", Published in: Electronics and Communication Systems (ICECS), 2015 2nd International Conference on 26-27 Feb. 2015, Publisher:IEEE,DOI: 10.1109/ECS.2015.7124833.

  • Downloads

  • How to Cite

    Naveen, N., & Ganesh Kumar, S. (2018). Efficient Mining and Recommendation of Extensive Data Through Collaborative Filtering in E-Commerce: A Survey. International Journal of Engineering & Technology, 7(2.24), 331-335. https://doi.org/10.14419/ijet.v7i2.24.12077