Papers with recommendation tasks
Query Distillation: BERT-based Distillation for Ensemble Ranking (2020.coling-industry)
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| Challenge: | Recent years have witnessed substantial progress in the development of neural ranking networks, but an increasingly heavy computational burden due to growing numbers of parameters and the adoption of model ensembles. |
| Approach: | They propose a two-stage distillation method that allows a smaller student model to be trained while benefiting from the better performance of the teacher model. |
| Outcome: | The proposed method shows higher-quality rankings compared to the teacher model. |
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data (2022.naacl-main)
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| Challenge: | Despite advances in self-supervised learning, there is a lack of models that can effectively capture both intra- and intra-item semantics for semi-structured session data. |
| Approach: | They propose a graph-based transformer model for semi-structured session data that captures both intra- and intra-item semantics. |
| Outcome: | The proposed model outperforms baselines in three session search and entity linking tasks by up to 9%. |
LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations (2025.coling-main)
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Wenlin Zhang, Chuhan Wu, Xiangyang Li, Yuhao Wang, Kuicai Dong, Yichao Wang, Xinyi Dai, Xiangyu Zhao, Huifeng Guo, Ruiming Tang
| Challenge: | Lack of training data leads to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. |
| Approach: | They propose a tree-based LLM recommendation framework which structures all items into an item tree to improve the efficiency of LLM’s item retrieval. |
| Outcome: | The proposed framework outperforms the baseline model in the A/B test on Huawei industrial system. |
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning (2023.acl-long)
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Kyuyong Shin, Hanock Kwak, Wonjae Kim, Jisu Jeong, Seungjae Jung, Kyungmin Kim, Jung-Woo Ha, Sang-Woo Lee
| Challenge: | Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. |
| Approach: | They propose to use user behavior sequences as plain text to represent rich information in any domain or system without losing generality. |
| Outcome: | The proposed frameworks achieve excellent results on diverse recommendation tasks and can be used on unseen domains and services. |
Aligning Large Language Models with Recommendation Knowledge (2024.findings-naacl)
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Yuwei Cao, Nikhil Mehta, Xinyang Yi, Raghunandan Hulikal Keshavan, Lukasz Heldt, Lichan Hong, Ed Chi, Maheswaran Sathiamoorthy
| Challenge: | Large language models (LLMs) excel at natural language reasoning, but cannot model complex user-item interactions inherent in recommendation tasks. |
| Approach: | They propose to equip large language models with recommendation-specific knowledge to address this gap by combining Masked Item Modeling and Bayesian Personalized Ranking (BPR) auxiliary task data samples are generated that encode item correlations and user preferences. |
| Outcome: | Experiments on Amazon Toys & Games, Beauty, and Sports & Outdoors show that the proposed method outperforms conventional and LLM-based baselines by significant margins in retrieval. |
Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models (2022.findings-emnlp)
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| Challenge: | Recent work on how to encode compositional task structure has been limited by semantic parsing and multihop reasoning for the purpose of Q&A. |
| Approach: | They propose an approach to decomposing a target task into component tasks and fine-tuning smaller LMs on a curriculum of such component tasks. |
| Outcome: | The proposed approach outperforms end-to-end learning even with equal data, and gets better as more component tasks are modeled. |
Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2026.acl-long)
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| Challenge: | Recent advances in large language models have enabled their use as semantic encoders for recommendation, but their roles and behaviors in this setting are still not well understood. |
| Approach: | They propose a benchmark to evaluate large language models as semantic encoders in recommendation scenarios. |
| Outcome: | The proposed benchmark shows that ranking of 11 leading LLMs is low compared to MTEB, highlighting the unique challenges of semantic encoding in recommendation. |
From ID to LLM: Rethinking Representation Learning for Recommendation (2026.acl-long)
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| Challenge: | Recent studies indicate a fundamental incompatibility between ID representations and language model (LM) representations as they capture behavioral and semantic spaces respectively. |
| Approach: | They propose a Profile-then-Embedding framework for recommendation that integrates semantic user and item profiles and a Personalized Embedded stage to encode these profiles into task-aligned recommendation embeddings. |
| Outcome: | The proposed framework achieves significant gains across three benchmark datasets, including cold-start and long-tail scenarios. |
RecMind: Large Language Model Powered Agent For Recommendation (2024.findings-naacl)
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Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Yanbin Lu, Xiaojiang Huang, Yingzhen Yang
| Challenge: | Existing recommendations systems are limited in generalizing to new tasks due to model scale and data size constraints. |
| Approach: | They propose an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge to provide zero-shot personalized recommendations. |
| Outcome: | The proposed model outperforms existing zero/few-shot LLM-based recommendation baseline methods in various tasks and achieves comparable performance to a fully trained recommendation model P5. |
Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View (2026.findings-acl)
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Hao Liao, Jiwei Zhang, Jianxun Lian, Wensheng Lu, Mingqi Wu, null Shuowangg, Yong Zhang, Yitian Huang, Mingyang Zhou, Rui Mao
| Challenge: | Existing approaches to reduce OOD recommendations fall into three grounding paradigms: retrieval, constrained generation and discrete item tokenizer generation. |
| Approach: | They propose a framework that instantiates three grounding paradigms under a single architecture . embedding-based retrieval, constrained generation and discrete item-tokenizer methods are implemented . |
| Outcome: | The proposed framework eradicates OOD recommendations across all variants and achieves state-of-the-art accuracy compared to strong ID-based and LLM-based baselines. |
ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation (2026.findings-acl)
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Yihua Zhang, Mingfu Liang, Jiyan Yang, Rong Jin, Wen-Yen Chen, Yiping Han, Huayu Li, Buyun Zhang, Liang Luo, Luke Simon, Sijia Liu, Tianlong Chen, Xi Liu
| Challenge: | Recent advances in multimodal recommenders lack explicit reasoning and self-awareness of uncertainty. |
| Approach: | They propose a reasoning-augmented multimodal agent structured around a three-stage explicit reasoning pipeline. |
| Outcome: | The proposed agent improves ranking metrics and performance on four standard recommendation tasks across five real-world datasets. |
Aligning Large Language Models for Controllable Recommendations (2024.acl-long)
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| Challenge: | Existing literature focuses on integrating domain-specific knowledge into LLMs to enhance accuracy using a fixed task template. |
| Approach: | They propose a collection of supervised learning tasks augmented with labels derived from a conventional recommender model to improve LLMs’ proficiency in adhering to recommendation-specific instructions. |
| Outcome: | The proposed approach significantly improves the capability of LLMs to respond to instructions within recommender systems, reducing formatting errors while maintaining a high level of accuracy. |
SOLAR: Serendipity Optimized Language Model Aligned for Recommendation (2025.findings-emnlp)
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Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, Yongxin Ni
| Challenge: | Large Language Models have shown strong potential in recommendation tasks . however, their application to serendipity-oriented recommendations remains challenging . |
| Approach: | They propose a domain-adaptive instruction tuning method that aligns Large Language Models with recommendation tasks. |
| Outcome: | The proposed framework bridges the domain gap between LLMs and recommendation tasks. |
VIP5: Towards Multimodal Foundation Models for Recommendation (2023.findings-emnlp)
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| Challenge: | Recent advances in foundation models have impeded the ability for these fields to benefit from each other’s advancements. |
| Approach: | They propose to use a multimodal foundation model to unify various modalities and recommendation tasks under the P5 recommendation paradigm to implement personalized prompts. |
| Outcome: | The proposed model will unify visual, textual, and personalization modalities under the P5 recommendation paradigm and will improve recommendation performance and efficiency. |
CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems (2025.findings-acl)
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| Challenge: | Existing approaches to align LLMs with recommendation tasks do not fully leverage their sequential information processing capabilities. |
| Approach: | They propose a system that allows users to expand their vocabulary by assigning a unique ID to each item within the expanded vocabulary. |
| Outcome: | The proposed system maximizes the sequence understanding abilities of large language models, significantly enhancing their performance on recommendation tasks. |
Enhancing High-order Interaction Awareness in LLM-based Recommender Model (2024.emnlp-main)
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| Challenge: | Existing approaches to model user-item interactions do not account for high-order interactions. |
| Approach: | They propose to enhance whole-word embeddings to enhance LLMs’ interpretation of graph-constructed interactions for recommendations without requiring graph pre-training. |
| Outcome: | The proposed model outperforms state-of-the-art methods in direct recommendations. |
ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning (2026.acl-long)
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| Challenge: | Existing reasoning-augmented systems that handle complex queries are lacking . we present a framework that enhances LLM-based recommendation assistants . |
| Approach: | They propose a reinforcement fine-tuning framework that enhances LLM-based recommendation . they use a dual-graph Enhanced Reward Shaping framework to integrate recommendation metrics . |
| Outcome: | The proposed framework outperforms state-of-the-art recommendations and preserves core abilities. |