IJMTES – A Research Paper on Data Analysis (rating distribution, time series analysis) & using The Recommendation Algorithm with Cosine similarity, Correlation matrix and SVD on Amazon dataset

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : A Research Paper on Data Analysis (rating distribution, time series analysis) & using The Recommendation Algorithm with Cosine similarity, Correlation matrix and SVD on Amazon dataset

Author’s Name : Pranjal Chowdhury , ParthoProtim Sarkar

Volume 07 Issue 01  2020unnamed

ISSN no:  2348-3121

Page no: 10-14

Abstract –  Nowadays . Data analysis has become a very important area for both companies and researchers as a consequence of the technological developments in recent years. Recommender system has a long history as a successful application in artificial intelligence. Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. This paper describes some algorithms designed for this task including cosine-based similarity algorithm and correlation-based similarity algorithm. we conclude that correlation based similarity algorithm acts better than Cosine based similarity algorithm . We adopted free-formatted rating-based  into traditional 2-Dimensional SVD approach. We analyzed the effect of different rating similarity techniques to the 3-Dimensional SVD recommendation performance.

Keywords –  Recommendation Systems, Singular Value Decomposition, Collaborative filtering, correlation-based similarity algorithms, cosine-based similarity algorithm