LOHRec: Leveraging Order and Hierarchy in Generative Sequential Recommendation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | generative recommenders focus on maximizing the prediction probability of the next item in the temporal sequence, ignoring diverse potential items. |
| Approach: | They propose a learning framework that leverages order and hierarchy in generative recommendation using quantized identifiers to further explore performance ceiling of lightweight generative recommenders. |
| Outcome: | The proposed learning framework outperforms strong prior baselines across multiple datasets. |
Similar Papers
UCGRec: User-Centric Graph Learning for LLM-based Sequential Recommendation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for sequential recommendation rely primarily on item descriptions or utilize user preferences independently. |
| Approach: | They propose a method that integrates diverse user-relevant preference signals into a unified user-centric graph and injects the graph-based knowledge into the LLM through end-to-end training with graph neural networks. |
| Outcome: | The proposed method outperforms conventional and state-of-the-art methods on four widely used sequential real-world recommendation datasets. |
HSUGA: LLM-Enhanced Recommendation with Hierarchical Semantic Understanding and Group-Aware Alignment (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for enhancing sequential recommendation use long interaction sequences, but they lack the ability to extract user preferences from long sequences. |
| Approach: | They propose a plugin that integrates LLMs to infer user preferences from interaction sequences. |
| Outcome: | The proposed algorithms improve user semantic embedding extraction and utilization on three benchmark datasets. |
Sequential LLM Framework for Fashion Recommendation (2024.emnlp-industry)
Copied to clipboard
Han Liu, Xianfeng Tang, Tianlang Chen, Jiapeng Liu, Indu Indu, Henry Zou, Peng Dai, Roberto Galan, Michael Porter, Dongmei Jia, Ning Zhang, Lian Xiong
| Challenge: | Existing fashion recommendation systems struggle with the unique challenges of the fashion domain. |
| Approach: | They propose a sequential fashion recommendation framework that leverages a pre-trained large language model enhanced with recommendation-specific prompts. |
| Outcome: | The proposed framework significantly improves fashion recommendation performance on Amazon fashion. |
FedCSR: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning (2025.coling-main)
Copied to clipboard
Dongyi Zheng, Hongyu Zhang, Jianyang Zhai, Lin Zhong, Lingzhi Wang, Jiyuan Feng, Xiangke Liao, Yonghong Tian, Nong Xiao, Qing Liao
| Challenge: | Existing federated frameworks for cross-domain sequential recommendation rely on user alignment, which increases communication costs and privacy risks. |
| Approach: | They propose a federated cross-domain sequential recommendation framework that eliminates the need for user alignment between platforms. |
| Outcome: | The proposed framework eliminates the need for user alignment between platforms. |
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. |
Learning Transition Patterns by Large Language Models for Sequential Recommendation (2025.coling-main)
Copied to clipboard
Jianyang Zhai, Zi-Feng Mai, Dongyi Zheng, Chang-Dong Wang, Xiawu Zheng, Hui Li, Feidiao Yang, Yonghong Tian
| Challenge: | Extensive experiments on six real-world datasets show our approach outperforms the best baselines by 7.33% in NDCG@10, 4.65% in Recall@10 and 8.42% in MRR. |
| Approach: | They propose a framework for mapping sequential item texts to sequential item IDs that incorporates multi-query input and item linear projection to model conditional probability distribution of items. |
| Outcome: | The proposed framework outperforms baseline models on six real-world datasets by 7.33% and 4.65% respectively. |
Leveraging Order-Free Tag Relations for Context-Aware Recommendation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to tag recommendation neglect orderlessness and inter-dependency . Empirical results on Instagram and Stack Overflow show that our method is significantly superior to the previous approaches. |
| Approach: | They propose a sequence-oblivious generation method for tag recommendation . the next tag to be generated is independent of the order of the generated tags . they also propose regressive generation methods that take orderlessness into account . |
| Outcome: | Empirical results show that the proposed method is superior to previous approaches . the proposed system is based on two domains, Instagram and Stack Overflow . |
An Empirical Exploration of Local Ordering Pre-training for Structured Prediction (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have shown that pre-training contextualized encoders with language model objectives is effective for structured prediction. |
| Approach: | They propose a semi-supervised method for pre-training contextualized encoders with language model objectives. |
| Outcome: | The proposed method is effective on three typical structured prediction tasks in four languages. |
The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential Recommendation (2024.findings-naacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown excellent performance on various NLP tasks. |
| Approach: | They propose a method that integrates multiple demonstration users into one aggregated demonstration to improve sequential recommendation. |
| Outcome: | The proposed method outperforms state-of-the-art LLM-based sequential recommendation methods on three recommendation datasets. |
Re3val: Reinforced and Reranked Generative Retrieval (2024.findings-eacl)
Copied to clipboard
| Challenge: | generative retrieval models encode pointers to information in a corpus as an index within the model’s parameters. |
| Approach: | They propose a generative retrieval model that leverages contextual information to rerank retrieved page titles and utilizes REINFORCE to maximize rewards generated by constrained decoding. |
| Outcome: | The proposed model can't be tuned for the downstream readers as decoding the page title is a non-differentiable operation. |