Cross-Domain Emotion-Based Recommender System for Books and Movies

In this contribution, we propose a method for providing User-Based Collaborative Filtering Recommendations using the emotions present in social media reviews. We use a deep learning model which identifies the review dominant emotion from the 6 primary emotions proposed by W.G. Parrott. For experiments, we use a dataset containing book reviews and movie reviews that we collected from Goodreads and IMDB websites using our customized web scrapers. Moreover, we represent the book and associated movie as an unique item in the dataset. We validated our recommender system using a set of system-unseen reviews that simulate a set of users seeking for recommendations. The top k recommendations received for each simulated user are then analyzed by our proposed performance measures.