Improving Performance of Product Recommendations Using User Reviews

YEAR : 2019


With the vast amount of data that the world has nowadays, Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new items rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item. The motivation for this project comes from the eagerness to get a deep understanding of recommender systems. A website has been developed that uses different techniques for recommendations namely Frequent Item set, Association Ruling and Apriori algorithm.


System Requirements

Operating System : Windows 7,8,10 (64 bit)
Software : Python
Tools : Anaconda (Jupyter notebook IDE)


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