Fiducia: A Personalized Food Recommender System for Zomato
This addresses personalized food discovery for Zomato users, but it is incremental as it applies existing methods to a specific domain.
The paper tackles personalized food recommendation by developing Fiducia, a system that processes Zomato reviews to suggest restaurants for specific cafe items based on sentiment analysis and similarity metrics. It achieves 85% accuracy in sentiment analysis and an RMSE of 1.01, outperforming baselines.
This paper presents Fiducia, a food review system involving a pipeline which processes restaurant-related reviews obtained from Zomato (India's largest restaurant search and discovery service). Fiducia is specific to popular cafe food items and manages to identify relevant information pertaining to each item separately in the reviews. It uses a sentiment check on these pieces of text and accordingly suggests an appropriate restaurant for the particular item depending on user-item and item-item similarity. Experimental results show that the sentiment analyzer module of Fiducia achieves an accuracy of over 85% and our final recommender system achieves an RMSE of about 1.01 beating other baselines.