A hybrid feedback based book recommendation system using sentiment analysis

Authors(4) :-N. Rajganesh, C. Asha, A. T. Keerthana, K. Suriya

Recommender systems help the user to find accurate book from a large database. Sentiment analysis is an effective recommendation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar interests. The admin train a database which has sentiment based keywords with positivity or negativity weight. Implemented in Hybrid filtering technique in recommendation system with feedback analysis to improve the recommendation system. These feedbacks include reviews, ratings and emoticons which are implemented by stochastic learning algorithm. It analyze fake contextual information posted by online users with identifying the mac address along with review posting patterns.

Authors and Affiliations

N. Rajganesh
Assistant Professor, Department of Information Technology, A.V.C College of Engineering, Tamil Nadu, India
C. Asha
UG Students, Department of Information Technology, A.V.C College of Engineering, Tamil Nadu, India
A. T. Keerthana
UG Students, Department of Information Technology, A.V.C College of Engineering, Tamil Nadu, India
K. Suriya
UG Students, Department of Information Technology, A.V.C College of Engineering, Tamil Nadu, India

Collaborative Filtering; Hybrid Filtering Technique; Sentiment Analysis

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Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 322-327
Manuscript Number : CSEIT183349
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

N. Rajganesh, C. Asha, A. T. Keerthana, K. Suriya, "A hybrid feedback based book recommendation system using sentiment analysis", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.322-327, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT183349

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