Deep Learning Based Product Recommendation System
Narayana Darapaneni, Anwesh Reddy, Vibin Venugopal, Meghana Jannavada, Ajith Vasisht, Suvodeep Das, Surangana Ghosh, Saurav Kumar Sinha · 2023
The increasing volume of information available online calls for more effective and individualized methods of information management.Recommendation systems address this challenge by providing personalized recommendations to users based on various factors, such as their interests and interaction history.This paper focuses on the development of a conversational chatbot that offers product recommendations and related images based on multiple input features.The chatbot utilizes Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and a linear kernel matrix to generate product recommendations.Unlike traditional recommendation systems, the chatbot employs a conversational interface, resulting in a more human-like and intuitive interaction.The use of natural language processing techniques and a conversational interface provide a novel and improved way to make recommendations, enhancing the overall user experience.Our approach to product recommendations is unique and has the potential to bring a new level of personal-ization and interaction to the recommendation system field.