Papers by Shengran Dai
Integrating Group-based Preferences from Coarse to Fine for Cold-start Users Recommendation (2025.coling-main)
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| Challenge: | Existing approaches to cross-domain recommendation (CDR) draw on historical purchase records or reviews to generate user representations. |
| Approach: | They propose a model that integrates preferences from coarse to fine levels to improve recommendations for cold-start users. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on three CDR tasks. |
A Hierarchical Sequence-to-Set Model with Coverage Mechanism for Aspect Category Sentiment Analysis (2024.lrec-main)
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| Challenge: | Aspect category sentiment analysis (ACSA) aims to detect aspect categories and their corresponding sentiment polarities (category-sentiment pairs) generative models face three challenges, including addressing the missing predictions and focusing on relevant sentiment words. |
| Approach: | They propose to use sequence-to-set learning to tackle all three challenges simultaneously. |
| Outcome: | The proposed model is able to detect aspect categories and their corresponding sentiment polarities (category-sentiment pairs) but it is unable to predict all aspect categories within a sentence due to the disordered set. |