Exploration of Users Rating on Reputed Items on Recommender Systems

Authors(2) :-Madhavi Darsinala, D.Varalakshmi

Data mining is a subscription-based service where the networked storage space and computer resources can be obtained. Data mining economically enables the paradigm of data service outsourcing. However, to protect data privacy, sensitive DATA MINING data have to be encrypted before outsourced to the commercial public DATA MINING, which makes effective data utilization. In the proposed system, the problem of effective secure ranked keyword search over encrypted DATA MINING data is done. Ranked keyword search greatly enhances the system usability by returning the matching files in a ranked order. The existing technique resolves the optimization complexities in ranked keyword search and its effective utilization of remotely stored encrypted DATA MINING data. But it limits the further optimizations of the search results by preventing DATA MINING server to interact with DATA MINING users to maintain the integrity of actual owner’s keyword and the data associated with it. The aim is to define a framework which enhances the accuracy of the ranked keyword search by secured machine learning, which does not affect the data integrity.

Authors and Affiliations

Madhavi Darsinala
M.Tech Scholar, Department of CSE, NRI Institute of Technology Visadala (V&M), Guntur (Dt), Andhra Pradesh, India
Assistant Professor, Department of CSE, NRI Institute of Technology Visadala (V&M), Guntur (Dt), Andhra Pradesh, India

Efficient Ranked Keyword Search, Search engine in DATA MINING, Security in Search engine, confidential data, searchable encryption.

  1. B. Wang, Y. Min, Y. Huang, X. Li, F. Wu, "Review rating prediction based on the content and weighting strong social relation of reviewers," in Proceedings of the 2013 international workshop of Mining unstructured big data using natural language processing, ACM. 2013, pp. 23-30.
  2. D. Tang, Q. Bing, T. Liu, "Learning semantic representations of users and products for document level sentiment classification," in Proc. 53th Annual Meeting of the Association for Computational Linguistics and the 7thInternational Joint Conference on Natural Language Processing, Beijing, China, July 26-31, 2015, pp. 1014–1023.
  3. Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, S. Ma, "Explicit factor models for explainable recommendation based on phrase-level sentiment analysis," in proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014.
  4. W. Zhang, G. Ding, L. Chen, C. Li , and C. Zhang, "Generating virtual ratings from Chinese reviews to augment online recommendations," ACM TIST, vol.4, no.1. 2013, pp. 1-17.
  5. X. Lei, and X. Qian, "Rating prediction via exploring service reputation," 2015 IEEE 17th International Workshop on Multimedia
  6. X. Yang, H. Steck, and Y. Liu, "Circle-based recommendation in online social networks, " in Proc. 18th ACM SIGKDD Int. Conf. KDD, New York, NY, USA, Aug. 2012, pp. 1267–1275.
  7. M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu, and S. Yang, "Social contextual recommendation," in proc. 21st ACM Int. CIKM, 2012, pp. 45-54.
  8. Z. Fu, X. Sun, Q. Liu, et al., "Achieving Efficient DATA MINING Search Services: Multi-Keyword Ranked Search over Encrypted DATA MINING Data Supporting Parallel Computing," IEICE Transactions on Communications, 2015, 98(1):190-200.
  9. Y. Ren, J. Shen, J. Wang, J. Han, and S. Lee, "Mutual Verifiable Provable Data Auditing in Public DATA MINING Storage," Journal of Internet Technology, vol. 16, no. 2, 2015, pp. 317-323.
  10. W. Luo, F. Zhuang, X. Cheng, Q. H, Z. Shi, "Ratable aspects over sentiments: predicting ratings for unrated reviews," IEEE International Conference on Data Mining (ICDM), 2014, pp. 380-389.
  11. T. Nakagawa, K. Inui, and S. Kurohashi, "Dependency tree-based sentiment classification using CRFs with Hidden Variables," NAACL, 2010, pp.786-794.
  12. Xiaojiang Lei, Xueming Qian, Member, IEEE, and Guoshuai Zhao, "Rating Prediction based on Social Sentiment from Textual Reviews," IEEE Transactions On Multimedia, MANUSCRIPT ID: MM-006446

Publication Details

Published in : Volume 4 | Issue 9 | July-August 2018
Date of Publication : 2018-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 476-482
Manuscript Number : IJSRSET1849123
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Madhavi Darsinala, D.Varalakshmi, " Exploration of Users Rating on Reputed Items on Recommender Systems, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 9, pp.476-482, July-August-2018.
Journal URL : http://ijsrset.com/IJSRSET1849123

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