Topic-enhanced Graph Neural Networks for Extraction-based Explainable Recommendation
Jie Shuai, Le Wu, Kun Zhang, Peijie Sun, Richang Hong, Meng Wang · 2023
Review information has been demonstrated beneficial for the explainable recommendation. It can be treated as training corpora for generation-based methods or knowledge bases for extraction-based models. However, for generation-based methods, the sparsity of user-generated reviews and the high complexity of generative language models lead to a lack of personalization and adaptability. For extraction-based methods, focusing only on relevant attributes makes them invalid in situations where explicit attribute words are absent, limiting the potential of extraction-based models.