| Challenge: | Existing models focus on sequential order of items and neglect to handle temporal dynamics . existing models neglect to capture hidden user preferences via various temporal signals . |
| Approach: | They propose a model that generates recommendations into a text-to-text generation task . they introduce Time-aware Prompting and Trend-awful Inference . |
| Outcome: | The proposed model outperforms state-of-the-art models with gains of 15.4% and 14.3% . it is based on time-aware Prompting and Trend-awful Inference . |
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| Challenge: | generative recommenders focus on maximizing the prediction probability of the next item in the temporal sequence, ignoring diverse potential items. |
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Time-aware Prompting for Text Generation (2022.findings-emnlp)
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Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering (2024.findings-emnlp)
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| Challenge: | Existing language models have limited sensitivity to temporal information and inadequate temporal reasoning capabilities. |
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