A hybrid of XGBoost and aspect-based review mining with attention neural network for user preference prediction

Chin Hui Lai*, Duen-Ren Liu, Kun Sin Lien

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

With the rapid development of the internet, users tend to refer to the rating scores or review opinions on social platforms. Most recommendation systems use collaborative filtering (CF) methods to recommend items based on users’ ratings. The rating-based CF methods do not consider users’ review opinions on different aspects of items. The accuracy of the rating predictions can be effectively improved by considering the latent semantics and various aspects of user reviews. In this paper, a novel rating prediction method is proposed according to an attention-based gated recurrent unit (GRU) deep learning model with semantic aspects. A two-phase method is proposed herein; it combines the word attention mechanism and review semantics to extract aspect features from user preferences. In the first phase, a bidirectional GRU neural network is adopted according to word attention in order to extract important words from users’ reviews. In the second phase, we split users’ reviews into words, and generate the aspect-based attention semantic vectors from these reviews based on Latent Dirichlet Allocation and the attention weights of the chosen words. The XGBoost method is then adopted to predict user preference ratings based on the aspect-based attention semantic vectors. The experimental results show that the proposed method outperforms traditional prediction methods and effectively improves the accuracy of predictions.

Original languageEnglish
Pages (from-to)1203–1217
Number of pages15
JournalInternational Journal of Machine Learning and Cybernetics
DOIs
StatePublished - Mar 2021

Keywords

  • Aspect-based review mining
  • Attention mechanism
  • Deep learning
  • Latent Dirichlet Allocation
  • Recommender system
  • Text mining

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