Publication Type : Conference Paper
Publisher : 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015
Source : 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, Institute of Electrical and Electronics Engineers Inc., p.745-752 (2015)
Url : http://www.scopus.com/inward/record.url?eid=2-s2.0-84946210906&partnerID=40&md5=8d69e6810baf7c7e00a8803f2f307cf7
ISBN : 9781479987917
Keywords : Collaborative filtering, Collaborative filtering techniques, decision making, Electronic commerce, Feedback analysis, Feedback systems, Hybrid approach, Information science, Internal feedback, Item-based collaborative filtering, Multiple parameters, Online systems, Recommender systems, Sales, Sentiment, Social networking (online), Websites
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2015
Abstract : With the increasing E-Commerce and online shopping there is a need for recommendation systems which help the customers in decision making and to suggest potential goods of purchase. In domains such as automobiles there are many websites but most of them are not having enhanced recommendation systems to enable easy decision making. Thus we have taken the initiative of building a dataset with multiple parameters based on a survey of the communities needs using potential blogs and created a recommendation system using user based and item based collaborative filtering. In addition to the combined collaborative filtering techniques we propose a framework which includes a feedback analysis to improve the recommendation system. The enhanced model AIDS the customers in decision making. We have proposed the feedback system at two levels. One is external feedback where the comments are gathered from public platforms like social media and automobile websites. The other is internal feedback i.e. the feedback is taken from users who have been provided with recommended items. The opinions extracted from such varied comments broadens the system and results. Our proposed hybrid model with feedback analysis has improvised the current system by providing better suggestions to customers. © 2015 IEEE.
Cite this Research Publication : V. Kavinkumar, Reddy, R. R., Balasubramanian, R., Sridhar, M., Sridharan, K., and Dr. Venkataraman D., “A hybrid approach for recommendation system with added feedback component”, in 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, 2015, pp. 745-752.