Papers with Generative recommendation
Reasoning Over Space: Enabling Geographic Reasoning for LLM-Based Generative Next POI Recommendation (2026.acl-long)
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| Challenge: | Existing LLM-based recommenders lack explicit modeling of geographic signals . without explicit modeling geographic signals, recommenders struggle to capture core mobility patterns . |
| Approach: | They propose a framework that utilizes geography as a decision variable within the reasoning process. |
| Outcome: | The proposed framework achieves over 10% relative gains in hit rate over strongest LLM-based baselines and improves cross-city transfer. |
From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation (2026.acl-long)
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Kaiwen Wei, Kejun he, Xiaomian Kang, Jie Zhang, null Ymyang, Li Jin, Zhenyang Li, Jiang Zhong, Richard He Bai, Junnan Zhu
| Challenge: | Generative recommendation models inherently bias towards local contexts, failing to capture deeper historical dependencies necessary for understanding complex user intents. |
| Approach: | They propose a training framework that shifts the objective from simple next-step prediction to deep comprehension of history by entropy-guided masking policy and a curriculum learning scheduler to enhance the framework. |
| Outcome: | The proposed framework outperforms state-of-the-art generative models on three public datasets and shows that it is more accurate than current models. |
Enhancing Time Awareness in Generative Recommendation (2025.findings-emnlp)
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| 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 . |
GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion (2025.acl-long)
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| Challenge: | Existing studies rely on item metadata to construct abbreviated item IDs, leading to a loss of valuable details. |
| Approach: | They propose a Generative Recommender via semantic-aware multi-granular late fusion to integrate rich semantics efficiently with minimal information loss. |
| Outcome: | The proposed model outperforms eight state-of-the-art recommendation models on four benchmark datasets and achieves significant improvements of 11.5-16.0% in Recall@5 and 5.3-13.6% in NDCG@5. |