BookStore: An Innovative Book Recommendation System Driven by Facial Expressions

Abstract

In the age of digital content consumption, personalization plays a crucial role in enhancing the user experience. However, many existing recommendation systems focus primarily on past interactions or ratings, often overlooking the user's current emotions and cultural background. This is particularly relevant in literature, where readers' mood and background can significantly influence their reception of various genres or topics. The proposed study addresses this gap by proposing an emotion-based book recommendation system that utilizes Facial Emotion Recognition (FER) to suggest culturally appropriate books aligned with the user’s emotional state. A deep learning model based on Convolutional Neural Networks (CNN) was trained to classify facial expressions and identify a person’s real-time emotions. The system features a culturally diverse dataset of books, categorized by emotion-relevant themes using Natural Language Processing (NLP) techniques. The user’s emotional state is determined through the FER model, and recommendations are made based on this identified mood and the user's cultural background. The model is trained to recognize facial emotions using labeled facial emotion datasets and has been tested across multiple scenarios to ensure it provides relevant book suggestions. The prototype system accurately identified users’ emotions with an 84% success rate and successfully recommended books of interest in 90% of the attempts. Users confirmed that the system could enhance their interest in reading by aligning book suggestions with their emotional and cultural inclinations. While these preliminary results are promising, they highlight the model’s potential to create a friendly reading environment by suggesting books that resonate with users' moods and cultural contexts.