Extracting User Preferences and Personality from Text for Restaurant Recommendation

Restaurant recommender systems are designed to support restaurant selection by assisting consumers with the information overload problem. However, despite their promises, they have been criticized of insufficient performance. Recent research in recommender systems has acknowledged the importance of personality in improving recommendation; however, limited work exploited this aspect in the restaurant domain. Similarly, the importance of user preferences in food has been known to improve recommendation but most systems explicitly ask the users for this information. In this paper, we explore the influence of personality and user preference by utilizing text in consumers’ electronic word of mouth (eWOM) to predict the probability of a user enjoying a restaurant he/she had not visited before. Food preferences are extracted though a trained named-entity recognizer learned from a labelled dataset of foods, generated using a rule-based approach. The prediction of user personality is achieved through a bi-directional transformer approach with a feed-forward classification layer, due to its improved performance in similar problems over other machine learning models. The personality classification model utilizes the textual information of reviews and predicts the personality of the author. Topic modelling is used to identify additional features that characterize users’ preferences and restaurants properties. All aforementioned features are used collectively to train an extreme gradient boosting tree model, which outputs the predicted user rating of restaurants. The trained model is compared against popular recommendation techniques such as nonnegative matrix factorization and single value decomposition.