Prediction of Online Products using Recommendation Algorithm

Authors(2) :-M. Mubsira, Prof. K. Syed Kousar Niasi

Online purchasing has been known as a rapidly growing commercial enterprise, and even though on line mobile purchasing has no longer accompanied those identical boom patterns within the beyond, it's miles now being diagnosed for its capability. As such, the focal point of previous on-line shopping research has seldom encompassed this specific retail marketplace, with the present research focusing basically on purchasersí motivations and attitudes, as opposed to how consumers actually save for groceries on line. Sentiment evaluation is one of the current research subjects in the subject of textual content mining. Opinions and sentiments mining from natural language are very difficult task. Sentiment analysis is the best solution. This gives important information for decision making in various domains. Various sentiment detection methods are available which affect the quality of result. In this paper we are finding the sentiments of people related to the services of E-shopping websites. The sentiments include reviews, ratings and emoticons. The main goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best. For this we use stochastic learning algorithm which analyze various feedbacks related to the services. Text mining algorithm is used to find scores of each word. Then sentiments are classified as negative, positive and neutral. It has been observed that the pre-processing of the data is greatly affecting the quality of detected sentiments. Finally analysis takes place based on classification. To find out fake review in the website can be analyzed. This device will discover fake critiques made via posting fake remarks about a product via figuring out the MAC deal with in conjunction with assessment posting styles. User will login to the device using his consumer identification and password and could view various merchandise and will give assessment approximately the product. To discover the evaluation is fake or authentic, system will find out the MAC address of the consumer if the machine observes fake assessment send by way of the identical MAC Address many a times it'll inform the admin to do away with that overview from the device. This gadget uses information mining technique. This machine allows the user to find out accurate overview of the product.

Authors and Affiliations

M. Mubsira
Research Scholar, Department of Computer Science, Jamal Mohamed College (Autonomous), Tiruchirapalli, Tamil Nadu, India
Prof. K. Syed Kousar Niasi
Assistant Professor, Department of Computer Science, Jamal Mohamed College (Autonomous), Tiruchirapalli, Tamil Nadu, India

Sentiment Analysis, Fake Assessment System, MAC Address, Hybrid Feedback System, Text Mining

  1. M Jahrer, A. Toscher, and R. Legenstein, "Combining predictions for accurate recommender systems," in KDD'10, pp. 693-702, 2010.
  2. Y Zhang, B. Cao, and D. Y. Yeung, "Multi-domain collaborative filtering," in Proc.UAI, pp. 725-732, 2010.
  3. R Keshavan, A. Montanari, and S. Oh, "Matrix completion from noisy entries," Journal of Machine Learning Research, vol. 11, pp. 2057-2078, 2010.
  4. M Harvey, M. Carman, I. Ruthven, and F. Crestani, "Bayesian latent variable models for collaborative item rating prediction," in CIKM'11, pp. 699-708, 2011.
  5. H Ma, D. Zhou, C. Liu, M. Lyu, and I. King, "Recommender Systems with Social Regularization," in ACM WSDM, pp. 287-296, 2011.
  6. SE. Madnick, R.Y. Wang, Y.W. Lee, H. Zhu, "Overview and framework for data and information quality research," Journal of Data and Information Quality, vol. 1(1, Article No 2: 22p.), 2009.
  7. JR.C. Nurse, S.S. Rahman, S. Creese, M. Goldsmith, K. Lamberts, "Information quality and trustworthiness: A topical state-of-the-art review," International Conference on Computer Applications and Network Security (ICCANS), Male, The Maldives, pp. 492-500, 2011.
  8. Gorton, J. Klein, "Distribution, data, deployment: Software architecture convergence in big data systems," IEEE Software, vol. 32(3), pp. 78-85, 2015.
  9. JR.C. Nurse, Agrafiotis-Ioannis, Creese-Sadie, M. Goldsmith, K. Lamberts, "Building confidence in Information-Trustworthiness metrics for decision support," The 12th IEEE International Conference on Trust’ Security and Privacy in Computing and Communications (IEEE TrustCom-13), Melbourne, VIC, pp. 535-543, 2013.
  10. S.S. Rahman, S. Creese, M. Goldsmith, "Accepting information with a pinch of salt: Handling untrusted information sources," Security and Trust Management, Lecture Notes in Computer Science Volume 7170, pp. 223-238, 2011.

Publication Details

Published in : Volume 3 | Issue 7 | September-October 2018
Date of Publication : 2018-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 63-70
Manuscript Number : CSEIT183710
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

M. Mubsira, Prof. K. Syed Kousar Niasi, "Prediction of Online Products using Recommendation Algorithm", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.63-70, September-October-2018.
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