An Approach to Utilize E-commerce Product Reviews to Remove Irrelevant Recommendations

Information explosion in the digital era has affected e-commerce users' purchase experience as the users encounter a large number of choices for the item of interest. A recommender system enriches the customers' shopping experience by downsizing the number of alternatives suggested to the user. It suggests the right items to the right customers at the right time, thus, benefiting both the customer and the stakeholders involved. User reviews are a first-hand account of a user's encounter with an item and consist of positive, negative, and neutral feedback. Users' opinion for item(s) of interest can be gauged through this textual feedback. Through this paper, an approach to improve the recommendations has been provided by leveraging the product review functionality. Extracting the product features from user-provided feedback and not recommending products with uninteresting product features to improve the recommendation list is the main idea behind this approach.