Papers with recommendation systems
GraphNarrator: Generating Textual Explanations for Graph Neural Networks (2025.acl-long)
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| Challenge: | Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. |
| Approach: | They propose to use a generative language model to map input-output pairs to explanations reflecting the model’s decision-making process to generate a model that generates pseudo-labels that capture the model's decisions from saliency-based explanations. |
| Outcome: | Extensive experiments show that GraphNarrator produces human-preferred explanations that are faithful, concise, and human-like. |
KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction (2023.starsem-1)
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| Challenge: | Knowledge graphs are incomplete in the information they represent, necessitating knowledge graph completion tasks. |
| Approach: | They propose a new entity/relation embedding layer that learns to differentiate distinctive entity and relation types, thus allowing the model to learn the structure of the knowledge graph. |
| Outcome: | The proposed language model learns to differentiate distinct entity and relation types, thus learning the structure of the knowledge graph. |
Harmless Transfer Learning for Item Embeddings (2022.findings-naacl)
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| Challenge: | Existing approaches to learn item embeddings for categorical features are limited by the frequency of items in real-world. |
| Approach: | They propose a method that transfers knowledge from frequent items to rare items by introducing an auxiliary transfer loss. |
| Outcome: | The proposed framework significantly boosts the performance on a variety of NLP and recommendation system tasks. |
MIReAD: Simple Method for Learning High-quality Representations from Scientific Documents (2023.acl-short)
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| Challenge: | Pretrained language models can learn rich textual representations, but they cannot provide powerful document-level representations for scientific articles. |
| Approach: | They propose a transformer-based method that learns semantically meaningful representations from scientific papers by fine-tuning transformer models to predict the target journal class based on the abstract. |
| Outcome: | The proposed method outperforms six existing models for representation learning on scientific documents across four evaluation standards. |
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. |
Point-of-Interest Type Inference from Social Media Text (2020.aacl-main)
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| Challenge: | Using a dataset of 200,000 English tweets, we can predict the type of the place from which a tweet was sent from. |
| Approach: | They propose to analyze a dataset of 200,000 tweets from 2,761 points-of-interest in the U.S. and train classifiers to predict the type of the location a tweet was sent from. |
| Outcome: | The proposed method can predict the type of the location a tweet was sent from and reach a macro F1 of 43.67 across eight classes. |
MICE: Mixture of Image Captioning Experts Augmented e-Commerce Product Attribute Value Extraction (2025.acl-industry)
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| Challenge: | Existing visual attribute value extraction methods rely on product images and textual information, which can be ambiguous, inaccurate, or unavailable. |
| Approach: | They propose a framework that leverages a curated pool of image captioning models to generate accurate captions from product images. |
| Outcome: | The proposed framework significantly improves state-of-the-art large multimodal models in zero-shot and fine-tuning settings. |
HierDiffuse: Progressive Diffusion for Robust Interest Fusion in CTR Prediction (2025.emnlp-industry)
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Ziheng Ni, Congcong Liu, Yuying Chen, Zhiwei Fang, Changping Peng, Zhangang Lin, Ching Law, Jingping Shao
| Challenge: | Existing approaches fuse long-term behavioral profiles and short-term interactions, suffering from representational misalignment and noise in transient signals. |
| Approach: | They propose a framework that redefines interest fusion as a hierarchical denoising process through diffusion models. |
| Outcome: | The proposed framework redefines interest fusion as a hierarchical denoising process through diffusion models. |
Sequential LLM Framework for Fashion Recommendation (2024.emnlp-industry)
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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. |
On Using Arabic Language Dialects in Recommendation Systems (2025.findings-naacl)
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| Challenge: | Using natural language processing (NLP) to analyze user reviews in recommendation systems is unexplored. |
| Approach: | They propose to integrate Arabic dialects as a signal in recommendation systems by using explicit and implicit approaches. |
| Outcome: | The proposed approach improves recommendation performance and encourages further research in the Arab multicultural world. |
Exploring Unified Training Framework for Multimodal User Profiling (2025.coling-main)
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| Challenge: | Recent studies on user profiling focus on extracting multiple aspects of user attributes from textual reviews, but these studies do not fully exploit the potential of the rich multimodal data at hand. |
| Approach: | They propose a task that utilizes both review texts and their accompanying images to generate comprehensive user profiles. |
| Outcome: | The proposed training framework incorporates historical review texts and images for user profile generation. |
OpenGraph: Towards Open Graph Foundation Models (2024.findings-emnlp)
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| Challenge: | Graph Neural Networks (GNNs) have emerged as promising techniques for encoding structural information and improving performance in tasks like link prediction and node classification. |
| Approach: | They propose a graph foundation model that generalizes to unseen graph data with different properties. |
| Outcome: | The proposed model achieves remarkable zero-shot graph learning performance across various settings. |
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue (D19-1)
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| Challenge: | Traditional recommendation systems produce static rather than interactive recommendations invariant to a user’s specific requests, clarifications, or current mood. |
| Approach: | They use a goal-driven recommendation dialogue dataset to develop an end-to-end dialogue system that can simultaneously converse and recommend. |
| Outcome: | The proposed system can converse and recommend movies to humans without considering the task goal itself. |
Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements (2025.findings-acl)
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Shu Yang, Shenzhe Zhu, Zeyu Wu, Keyu Wang, Junchi Yao, Junchao Wu, Lijie Hu, Mengdi Li, Derek F. Wong, Di Wang
| Challenge: | Existing fraud detection benchmarks focus on single-turn classification tasks, failing to capture dynamic nature of real-world fraud attempts. |
| Approach: | They propose a bilingual benchmark to assess LLMs' ability to resist fraud and phishing attacks across five key fraud categories: Fraudulent Services, Impersonation, Phishing Scams, Fake Job Postings, and Online Relationships. |
| Outcome: | The proposed model improves in role-play settings and in e-commerce and recommendation systems. |
Structural Contrastive Representation Learning for Zero-shot Multi-label Text Classification (2022.findings-emnlp)
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| Challenge: | Existing approaches for zero-shot multi-label text classification struggle with accuracy and poor training efficiency. |
| Approach: | They propose a structural contrastive representation learning approach that uses randomized text segmentation to generate high-quality contrastive pairs. |
| Outcome: | The proposed approach improves accuracy and speed up training time on publicly available datasets. |
Aligning Black-box Language Models with Human Judgments (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) are increasingly used as automated judges to evaluate recommendation systems, search engines, and other subjective tasks. |
| Approach: | They propose a framework to align LLM judgments with individual human evaluators or their aggregated judgments without retraining or fine-tuning the LLM. |
| Outcome: | The proposed framework achieves 142% improvement in agreement across 29 tasks and exceeds inter-human agreement on four out of six tasks. |
ComPO: Community Preferences for Language Model Personalization (2025.naacl-long)
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| Challenge: | Current methods for training language models with human feedback rely on subjective preferences that are assumed to account for an "average" user . however, annotating preferences is inherently subjective and results in generic models that generate outputs not preferred by many user groups. |
| Approach: | They propose a method to personalize preference optimization in LMs by contextualizing the probability distribution of model outputs with the preference provider. |
| Outcome: | The proposed method improves performance by focusing on group-level preferences rather than individual feedback. |
Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration (2025.findings-emnlp)
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| Challenge: | Query-to-Recommendation framework integrates large langucage models into recommendation systems . but it faces training-induced bias and bottlenecks from serialized architecture . |
| Approach: | They propose a parallel recommendation framework that decouples LLMs from candidate pre-selection and direct retrieval over the entire item pool. |
| Outcome: | The proposed framework decouples LLMs from candidate pre-selection and enables direct retrieval over the entire item pool. |
ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning (2025.findings-naacl)
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| Challenge: | Empirical evaluations demonstrate that ReasoningRec surpasses state-of-the-art methods by up to 12.5% in recommendation prediction while simultaneously providing human-intelligible explanations. |
| Approach: | They propose a reasoning-based recommendation framework that leverages Large Language Models to model users and items, focusing on preferences, aversions, and explanatory reasoning. |
| Outcome: | The proposed framework surpasses state-of-the-art methods by up to 12.5% in recommendation prediction while providing human-intelligible explanations. |
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. |
Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction (2022.lrec-1)
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Mehdi Mirzapour, Waleed Ragheb, Mohammad Javad Saeedizade, Kevin Cousot, Helene Jacquenet, Lawrence Carbon, Mathieu Lafourcade
| Challenge: | Knowledge graphs are used for information extraction, search engines, question answering, and recommendation systems. |
| Approach: | They propose a French knowledge graph dataset based on RezoJDM. |
| Outcome: | The proposed dataset can be used in many downstream tasks for the French language . it shows that it embeds knowledge graph baselines for link prediction tasks . |
Rank, Chunk and Expand: Lineage-Oriented Reasoning for Taxonomy Expansion (2025.findings-acl)
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| Challenge: | Existing taxonomy expansion methods struggle with representation limits and generalization, while generative methods process all candidates at once, introducing noise and exceeding context limits. |
| Approach: | They propose a plug-and-play framework that combines discriminative ranking and generative reasoning for efficient taxonomy expansion. |
| Outcome: | Experiments show that LORex improves accuracy by 12% and similarity by 5% over state-of-the-art methods. |
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users (2024.lrec-main)
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Yejin Kim, Scott Rome, Kevin Foley, Mayur Nankani, Rimon Melamed, Javier Morales, Abhay K. Yadav, Maria Peifer, Sardar Hamidian, H. Howie Huang
| Challenge: | Current approaches focus on improving ranking performance at the cost of escalating complexity and complicating the task. |
| Approach: | They propose a hybrid multi-task learning approach that trains on user-item and item-i item interactions. |
| Outcome: | The proposed approach improves accuracy, relevance, and diversity of user recommendations even for cold-start users. |
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning (2023.findings-acl)
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| Challenge: | Despite recent advances in transformer-based sentence encoders, the encoding of long documents (Ks of words) is still challenging with respect to both efficiency and quality considerations. |
| Approach: | They propose to combine a self-contrastive siamese network and a convex neural Bregman divergence network to train longfomer-based document encoders using an unsupervised contrastive learning method. |
| Outcome: | The proposed model outperforms baseline models on three long document topic classification tasks from the legal and biomedical domains. |
Leveraging LLM Reasoning Enhances Personalized Recommender Systems (2024.findings-acl)
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Alicia Tsai, Adam Kraft, Long Jin, Chenwei Cai, Anahita Hosseini, Taibai Xu, Zemin Zhang, Lichan Hong, Ed H. Chi, Xinyang Yi
| Challenge: | Recent advances have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting. |
| Approach: | They propose to use Large Language Models to perform tasks with subjectivity and personalized preferences as inputs to RecSys. |
| Outcome: | The proposed framework aligns with real human judgment on the coherence and faithfulness of LLM reasoning responses. |
C2KD: Cross-layer and Cross-head Knowledge Distillation for Small Language Model-based Recommendation (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) show promise but their size and high inference costs limit deployment on resource-constrained devices. |
| Approach: | They propose a framework to transfer task-relevant knowledge from two complementary dimensions to Large Language Models (LLMs) Large Language models (LLMS) have demonstrated great potential in sequential recommendation tasks . |
| Outcome: | Extensive experiments across diverse model families show that the proposed framework achieves competitive performance compared to LLMs. |
KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models (2025.acl-long)
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| Challenge: | Recent advances in large language models and the abundance of food data have led to studies to improve food understanding using LLMs. |
| Approach: | They propose a unified system that leverages food KGs and LLMs to provide personalized food recommendations and generate recipes with associated micro-nutritional information. |
| Outcome: | The proposed system outperforms existing methods in food recommendation, recipe generation and nutritional analysis. |
PepRec: Progressive Enhancement of Prompting for Recommendation (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have been gaining in-depth performance in natural language processing domains. |
| Approach: | They propose a training-free prompting framework that captures knowledge from content-based filtering and collaborative filtering to boost recommendation performance with LLMs. |
| Outcome: | The proposed framework outperforms traditional deep learning recommendation models and prompt-based recommendation systems on two real-world datasets. |
Multi-Granularity Fusion Text Semantic Matching Based on WoBERT (2024.lrec-main)
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| Challenge: | Existing text-matching methods struggle with semantic nuances in short texts . a novel approach to improve text semantic matching is being developed . |
| Approach: | They propose a multi-granularity fusion model that harnesses a pre-trained language model to capture text semantic nuances. |
| Outcome: | The proposed model improves on Chinese short text matching datasets compared to traditional methods . the proposed model captures individual text semantic nuances and improves accuracy . |
DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)
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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability. |
| Approach: | They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. |
| Outcome: | The proposed model outperforms baselines on three real-world datasets. |
Jump Starting Bandits with LLM-Generated Prior Knowledge (2024.emnlp-main)
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| Challenge: | Contextual multi-armed bandits generate personalized recommendations based on user-specific contexts. |
| Approach: | They propose an initialization algorithm for contextual bandits by prompting LLMs to produce a pre-training dataset of approximate human preferences for the bandit. |
| Outcome: | The proposed approach significantly reduces online learning regret and data-gathering costs for training such models. |
Distilling LLM Reasoning into Dense Encoders: Bridging the Accuracy-Efficiency Gap in Recommendation (2026.findings-acl)
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| Challenge: | Existing distillation approaches target Small Language Models (SLMs) or Conventional Recommendation Models, but face a critical trade-off between computational cost and semantic reasoning capacity. |
| Approach: | They propose a framework that establishes a text encoder as the optimal student architecture for scalable recommendation. |
| Outcome: | Experiments on four datasets show that the proposed framework outperforms state-of-the-art models and achieves significantly reduced latency. |
DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling (2025.acl-long)
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Aili Chen, Chengyu Du, Jiangjie Chen, Jinghan Xu, Yikai Zhang, Siyu Yuan, Zulong Chen, Liangyue Li, Yanghua Xiao
| Challenge: | Existing methods for generating personas from static historical data fail to capture dynamic behaviors and evolving preferences in real-world interactive scenarios. |
| Approach: | They propose a novel approach that iteratively updates personas using streaming user behavior data to continually enhance their quality. |
| Outcome: | The proposed approach delivers 32.2% reduction in user behavior prediction error over four update rounds, outperforming the best baseline by 22.92%. |
Token-level Proximal Policy Optimization for Query Generation (2025.emnlp-main)
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Yichen Ouyang, Lu Wang, Fangkai Yang, Pu Zhao, Chenghua Huang, Jianfeng Liu, Bochen Pang, Yaming Yang, Yuefeng Zhan, Hao Sun, Qingwei Lin, Saravan Rajmohan, Weiwei Deng, Dongmei Zhang, Feng Sun
| Challenge: | Large Language Models (LLMs) have improved search engines and recommendation systems through their text understanding capabilities. |
| Approach: | They propose a token-level proximal policy optimization approach to empower LLMs to perform better in query generation through fine-tuning. |
| Outcome: | The proposed approach outperforms existing LLMs on an open-source and industrial dataset. |