Insights Procured From the Recommendation System Based On User Reviews
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Abstract
With the dawn of the 21st century we saw an unprecedented influx of data. It is only in the past decade that our
processing capabilities have caught up to being able to utilize the huge amount of data with several algorithms to be
useful. Product reviews and ratings are popular tools to support buying decisions of consumers. These tools are also
valuable for online retailers, who use rating systems in order to build trust and reputation in e- commerce. Many
online shops offer quantitative ratings, textual reviews or a combination of both. The number of reviews on Amazon
has grown significantly over the years. Customers who made purchases on Amazon provide reviews by rating the
product from 1 to 5 stars and sharing a text summary of their experience and opinions of the product. This research
aims to provide statistical insights into Amazon product reviews, examine their helpfulness in recommending
products, and suggest a new way to predict the helpfulness score of the user reviews
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