Incorporating Review-missing Interactions for Generative Explainable Recommendation (2025.coling-main)
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| Challenge: | Existing models of explainable recommendation use user reviews as ground truths, but in practice, a large amount of users may not leave reviews after purchasing items. |
| Approach: | They propose to incorporate user preferences into explainable recommender models by leveraging generative models to predict the missing reviews and then training the model based on all the predicted and original reviews. |
| Outcome: | The proposed model improves the explanation quality on three publicly available datasets. |
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| Challenge: | Recent years have witnessed a growing interest in the development of explainable recommendation models. |
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| Challenge: | Existing models learn user and item embeddings and generate reasons based on these embedds. |
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Review-based Question Generation with Adaptive Instance Transfer and Augmentation (2020.acl-main)
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| Challenge: | Existing methods to generate questions for verbose reviews are inefficient for potential consumers . lack of training data hinders efficient review digestion, authors say . |
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| Challenge: | Existing systems that use user and item identity as inputs for review generation are lacking in the field of natural language processing. |
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| Challenge: | Existing explanations for user reviews often fail to meet user-centric aspects, reducing their usefulness to users. |
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MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation (2025.acl-long)
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Ching-Wen Yang, Zhi-Quan Feng, Ying-Jia Lin, Che Wei Chen, Kun-da Wu, Hao Xu, Yao Jui-Feng, Hung-Yu Kao
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| Challenge: | Existing approaches for explainable machine learning systems focus on interpreting outputs or connections between inputs and outputs. |
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| Challenge: | Literature review systems generate literature review tables by inferring schemas and values from documents. |
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| Challenge: | Existing explainable recommendation models generate repetitive sentences for different items or empty sentences with insufficient details. |
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