Personalized Recommendation Using Aspect-Aware Knowledge Graph Learning

Abstract

This study aims to apply user reviews and numerical ratings toward items to create an aspect-aware high-order representation for a recommendation system. We propose a novel aspect-aware knowledge graph recommendation model (AKGR) with the deep learning method to predict users' ratings on non-interacted items, from which more personalized recommendations can be made. First, we create a sequence-to-sequence encoder and decoder model by exploiting contextual and syntactic information in user reviews to extract aspects critical to items. Then we utilize the principal component analysis (PCA) and the K-means clustering to analyze the extracted aspects for category classification. Based on the aspects, we construct a graph structure to connect users and items which share the same aspect-based opinions for mining user preferences and item attributes. Finally, we combine the user and item latent features from the reviews and the user-item rating matrix to complete the rating prediction task by applying the factorization machine model. We conducted experiments on three aspect extraction datasets and five rating prediction datasets. To verify the effectiveness of the proposed aspect extraction model and rating prediction model, comparison experiments were made with some state-of-the-art baseline models, such as double embeddings convolutional neural network (DE-CNN) and dual graph convolutional network (DualGCN). The experiment results revealed that our proposed aspect extraction model had the best performance for the three datasets with an F1 score of 82.41%, 88.57%, and 73.39%. In the experiments of rating prediction, the proposed AKGR model achieved the best MAE and MSE scores on the five datasets, and there was an average improvement of 4.48% against the best baseline.