Abstract
The business of E-commerce is increasingly becoming popular due to pervasive Internet technologies. It is a human tendency to rely on the data or information, which they receive from their friends and neighbours prior to taking any decision, especially before purchasing any item. Presently, people are getting vast information and worldwide data though Web. Due to information overload, customers often face difficulties to locate their item of interest. Recommender system plays a significant role, and it helps to deal with information overload and further provides personalized recommendations to customers or users. In this paper, recommendation of smartphone was given based on feedback given by customer using weighted mean approach. The prediction was calculated for untried items, based on ratings given by new user using collaborative filtering. The results of recommendation and prediction show the approach is interesting.
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Acknowledgements
The authors are thankful to Director, MCKVIE and Principal, MCKVIE, for providing the computer laboratories and other infrastructure to do the proposed work. The authors are also thankful to the students of CSE department of MCKVIE for collecting the required data for the proposed work.
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Bandyopadhyay, S., Thakur, S.S., Mandal, J.K. (2020). Mining User’s Data Based on Customer’s Rating for Prediction and Recommendation—A Comparative Analysis. In: Mandal, J., Bhattacharya, K., Majumdar, I., Mandal, S. (eds) Information, Photonics and Communication. Lecture Notes in Networks and Systems, vol 79. Springer, Singapore. https://doi.org/10.1007/978-981-32-9453-0_12
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