I. INTRODUCTION
In the past decade the world has observed a steep rise in the use of e-commerce [12] in various domains of life.
generates a numerical measure of that
This has been possible due to the advancements in the field of online
data security.
People feel safe to make online transactions and hence, the market of online services has evolved to a great extent.
In today’s era, the consumer relies a lot on peer reviews and ratings to make a choice.
This makes online ratings and reviews a significant asset in the process of online marketing and form the backbone of the recommender system [14].
Though there are a lot of online service providers that make use of a number of rating parameters to target their marketing campaigns, but still there is room for improvements [6].
In this paper, we propose a basic recommender system [15] that gives personalized ratings for a user and recommends products or services based on the ratings, generated by the system [13].
A rating is basically a measure of user’s taste and preferences. The system predicts how much a user would like or dislike a particular item and generates a numerical measure of that [3].