Papers by Qianlong Wen
What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context (2026.acl-long)
Copied to clipboard
| Challenge: | Existing preference-alignment approaches rely on binary pairwise comparisons, overlooking preference intensity and temporal context. |
| Approach: | They propose a unified preference optimization framework that maps both explicit and implicit feedback into a common preference signal and constructs adaptive reward margins that jointly account for preference intensity and interaction recency. |
| Outcome: | The proposed framework outperforms state-of-the-art recommendations while maintaining behavioral patterns aligned with human decision-making. |
Progressive Self-Training with Discriminator for Aspect Term Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to extract aspect terms from review sentences are limited due to lack of annotated data. |
| Approach: | They propose to refine conventional self-training to progressive self-teaching to reduce noise . they use a discriminator to filter the noisy pseudo-labels. |
| Outcome: | The proposed model outperforms baseline models and achieves state-of-the-art performance on four SemEval datasets. |