Knowledge-aware attentional neural network for review-based movie recommendation with explanations

In this paper, we propose a knowledge-aware attentional neural network (KANN) for dealing with movie recommendation tasks by extracting knowledge entities from movie reviews and capturing understandable interactions between users and movies at the knowledge level. In most recommendation systems, review information is already widely utilized to uncover the explicit preferences of users for items, especially for domains including movie recommendations, music recommendations, and book recommendations, as reviews are full of knowledge entities relevant to the domain. When processing review information, current methods usually use word embeddings to represent reviews for modeling users and items. As a result, they may split the meaning of a phrase, and thereby induce erroneous predictions. Moreover, most methods capture high-order interactions between users and items after obtaining latent low-dimensional representations, which means they cannot discover understandable interactions or provide knowledge-level explanations. By incorporating knowledge graph representation into movie recommendation tasks, the proposed KANN can not only capture the inner attention among user (movie) reviews but also compute the outer attention values between users and movies before generating corresponding latent vector representations. These characteristics enable the explicit preferences of users for movies to be learned and understood. We test our model on two datasets (IMDb and Amazon) for the movie rating prediction task and the clickthrough rate prediction task and show that it outperforms some of the existing state-of-the-art models and gains outstanding prediction performances in cases with a very small amount of reviews. Furthermore, we demonstrate the high explainability of the proposed KANN by visualizing the interaction between users and movies through a case study. Our results and analyses highlight the relatively high effectiveness and reliability of KANN for movie recommendation tasks.