RECOMMENDATION

CTM - A Model for Large-Scale Multi-View Tweet Topic Classification (2022.naacl-industry)

Challenge:	Existing approaches to integrate the recommendation function and dialog generation function smoothly are lacking.
Approach:	They propose to integrate dialog context for recommendation and dialog generation better using a pre-trained language model and an item metadata encoder to integrate the recommendation and dialogue generation.
Outcome:	The proposed architecture improves the integration of recommendation and dialog generation functions.

Copied to clipboard

Vivek Kulkarni, Kenny Leung, Aria Haghighi

Information Extraction & Text Mining

Challenge:	Existing methods to classify social media posts into topics have been used to class up documents into topics.
Approach:	They propose a neural model that automatically associates social media posts with topics to solve these challenges.
Outcome:	The proposed model outperforms existing methods in the context of Twitter where the topic space is 10 times larger with potentially multiple topic associations per Tweet.

IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation (2021.acl-demo)

Copied to clipboard

Jiefu Gong, Xiao Hu, Wei Song, Ruiji Fu, Zhichao Sheng, Bo Zhu, Shijin Wang, Ting Liu

SVD-GCL: A Noise-Augmented Hybrid Graph Contrastive Learning Framework for Recommendation (2025.coling-main)

Challenge:	Automated Essay Assessment (AEA) aims to judge students’ writing proficiency in an automatic way.
Approach:	They propose to use Chinese AEA system IFlyEssayAssess to evaluate essays written by native Chinese students from primary and junior schools.
Outcome:	The proposed system provides application services for essay scoring, review generation, recommendation, and explainable analytical visualization.

Copied to clipboard

Liping Wang, Shichao Li, Hui Wang, Yuyan Gao, Mingyao Wei

RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models (2022.aacl-main)

Challenge:	Recent advances in graph neural networks have made it difficult to capture user preferences.
Approach:	They propose a graph contrastive learning recommendation model based on noise augmentation that integrates truncated singular value decomposition in the feature engineering stage.
Outcome:	The proposed model reduces dimensionality and denoises the original data.

Copied to clipboard

Lingzhi Wang, Huang Hu, Lei Sha, Can Xu, Daxin Jiang, Kam-Fai Wong

Multimodal Review Generation with Privacy and Fairness Awareness (2020.coling-main)

Challenge:	Existing generative methods to recommend items are shallowly integrated into the model training and have poor chit-chat ability.
Approach:	They propose a framework that integrates recommendation into the dialog generation by introducing a vocabulary pointer.
Outcome:	The proposed framework outperforms the state-of-the-art models on a benchmark dataset.

Copied to clipboard

Xuan-Son Vu, Thanh-Son Nguyen, Duc-Trong Le, Lili Jiang

Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation (2025.coling-main)

Challenge:	Existing frameworks for generating personalized reviews take privacy and fairness into account . users generate digital footprints when "traveling" on the internet .
Approach:	They propose a neural-based framework that generates personalized reviews with privacy and fairness in mind.
Outcome:	The proposed framework generates plausibly long reviews while controlling the amount of exploited user data and using the least sentiment biased embeddings.

Copied to clipboard

Caihong Mu, Keyang Zhang, Jialiang Zhou, Yi Liu

Enhancing Reranking for Recommendation with LLMs through User Preference Retrieval (2025.coling-main)

Challenge:	Existing contrastive learning-based methods struggle with data sparsity in real-world recommendations . Graph collaborative filtering incorporates contrastive training as an auxiliary task to improve performance .
Approach:	They propose a perturbation-driven dual auxiliary contrastive learning task for collaborative filtering . structure perturbation and weight perturbation are used to construct two graphs .
Outcome:	The proposed model outperforms benchmark models on multiple public datasets.

Copied to clipboard

Haobo Zhang, Qiannan Zhu, Zhicheng Dou

A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation (2020.lrec-1)

Challenge:	Existing large language models (LLMs) generate redundant output, which generates irrelevant information about the user’s preferences on candidate items from user behavior sequences.
Approach:	They propose a framework that enhances reranking for recommendation with large language models through user preference retrieval.
Outcome:	The proposed framework improves reranking for recommendation with large language models through user preference retrieval on three real-world public datasets.

Copied to clipboard

Matthias Kraus, Fabian Fischbach, Pascal Jansen, Wolfgang Minker

InstructPTS: Instruction-Tuning LLMs for Product Title Summarization (2023.emnlp-industry)

Challenge:	Existing literature on traditional and conversational recommendation systems, but how to provide suggestions is still an open question.
Approach:	They propose to use explicit and implicit strategies to compare user preferences and a proactive strategy to provide information from a gathered system to determine user acceptance.
Outcome:	The proposed strategies significantly influence the perception of human-computer interaction.

Copied to clipboard

Besnik Fetahu, Zhiyu Chen, Oleg Rokhlenko, Shervin Malmasi

Summarization

Challenge:	E-commerce product catalogs contain billions of items with lengthy titles . this leads to a gap between how customers refer to these unnatural titles - and how they are used .
Approach:	They propose a novel approach to product title summarization that uses a fine-tuned instruction strategy to train a highly accurate model.
Outcome:	The proposed approach can generate more accurate product title summaries with an improvement of over 14 and 8 BLEU and ROUGE points.

Adaptive Hyper-parameter Learning for Deep Semantic Retrieval (2023.emnlp-industry)

Copied to clipboard

Mingming Li, Chunyuan Yuan, Huimu Wang, Peng Wang, Jingwei Zhuo, Binbin Wang, Lin Liu, Sulong Xu

Information Retrieval

Challenge:	Existing methods for deep semantic retrieval are highly sensitive to hyper-parameters . a novel adaptive metric learning method is proposed to overcome this limitation .
Approach:	They propose a method that adaptively obtains hyper-parameters without fixed or extra-trainable hyper-parmeters . they adopt a symmetric metric learning method to mitigate model collapse issues .
Outcome:	The proposed method outperforms existing methods on a real-world dataset and brings economic benefits.

Fine-grained Interest Matching for Neural News Recommendation (2020.acl-main)

Copied to clipboard

Heyuan Wang, Fangzhao Wu, Zheng Liu, Xing Xie

Doctor Recommendation in Online Health Forums via Expertise Learning (2022.acl-long)

Challenge:	Existing studies represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation.
Approach:	They propose a Fine-grained interest matching method for neural news recommendation based on multi-level representations and fine-grain matching between segment pairs of each browsed news and the candidate news at each semantic level.
Outcome:	The proposed model can capture more fine-grained interest matching signals by performing interactions between each pair of news at multi-level semantic granularities.

Copied to clipboard

Xiaoxin Lu, Yubo Zhang, Jing Li, Shi Zong

VN-MTEB: Vietnamese Massive Text Embedding Benchmark (2026.findings-eacl)

Challenge:	Currently, manual doctor allocations are used to handle large volumes of queries, limiting the efficiency to help patients in sheer quantities.
Approach:	They propose to use patient queries to model doctor recommendation using their profiles and past dialogues to estimate their capabilities.
Outcome:	The proposed model outperforms baseline models on a Chinese online health forum, outperforming baseline models.

Copied to clipboard

Loc Pham, Tung Luu, Thu Vo, Minh Nguyen, Viet Hoang

KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)

Challenge:	a lack of large-scale test datasets makes it difficult to evaluate AI models before deploying them in real-world projects.
Approach:	They propose a Vietnamese benchmark for embedding models that leverages large language models and embeddable models to translate and filter samples from the Massive Multilingual Text Embedding Benchmark.
Outcome:	The proposed benchmark outperforms existing models in Vietnamese and English tasks with 41 datasets.

Copied to clipboard

Jun Zhang, Yan Yang, Chencai Chen, Liang He, Zhou Yu

Sequential LLM Framework for Fashion Recommendation (2024.emnlp-industry)

Challenge:	Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity.
Approach:	They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process.
Outcome:	The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation.

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

Towards Conversational Recommendation over Multi-Type Dialogs (2020.acl-main)

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.

Copied to clipboard

Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

MIRA: Empowering One-Touch AI Services on Smartphones with MLLM-based Instruction Recommendation (2025.acl-industry)

Challenge:	In recent years, there has been a significant increase in the work of conversational recommendation due to the rise of voice-based bots.
Approach:	They use a Chinese dialog dataset DuRecDial to study conversational recommendation in the context of multi-type dialogs where bots can proactively lead a conversation from a non-recommendation dialog to a recommendation dialog.
Outcome:	The proposed dataset allows to investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how interact with users for recommendation.

Copied to clipboard

Zhipeng Bian, Jieming Zhu, Xuyang Xie, Quanyu Dai, Zhou Zhao, Zhenhua Dong

Reasoning

Challenge:	generative AI is revolutionizing how users interact with smartphones, transforming how they interact with them.
Approach:	They propose a framework for task instruction recommendation that enables intuitive one-touch AI tasking on smartphones.
Outcome:	The proposed framework shows significant improvements in recommendation accuracy and coherence and intent alignment with predefined instruction candidates.

A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs (2025.acl-industry)

Copied to clipboard

Chiwan Park, Wonjun Jang, Daeryong Kim, Aelim Ahn, Kichang Yang, Woosung Hwang, Jihyeon Roh, Hyerin Park, Hyosun Wang, Min Seok Kim, Jihoon Kang

UP5: Unbiased Foundation Model for Fairness-aware Recommendation (2024.eacl-long)

Challenge:	Large Language Models (LLMs) have led to significant improvements in various service domains, including search, recommendation, and chatbot applications.
Approach:	They propose a framework for developing scalable, controllable, and reliable AI-driven agents that can be applied to real-world applications.
Outcome:	The proposed framework bridges the gap between academic research and real-world application, and enables scalable, controllable, and reliable AI-driven agents.

Copied to clipboard

Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, Zelong Li, Yongfeng Zhang

On Using Arabic Language Dialects in Recommendation Systems (2025.findings-naacl)

Challenge:	Large Language Models (LLMs) are gaining a foothold in Recommender Systems (RS) but there is growing concern that LLMs perpetuate stereotypes and may result in unfair recommendations.
Approach:	They propose a counterfactually-fair-prompt method for LLM-based recommendation that is based on unbiased foundation mOdels.
Outcome:	The proposed method achieves better recommendation performance with a high level of fairness on two real-world datasets.

Copied to clipboard

Abdulla Alshabanah, Murali Annavaram

Low-Resource NLP

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.

OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)

Copied to clipboard

Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou

Reasoning

Challenge:	Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs.
Approach:	They propose a framework that integrates dialogue, reasoning, and personalized recommendation.
Outcome:	Experiments across public benchmarks show state-of-the-art performance.

CLICK: Contrastive Learning for Injecting Contextual Knowledge to Conversational Recommender System (2023.eacl-main)

Copied to clipboard

Hyeongjun Yang, Heesoo Won, Youbin Ahn, Kyong-Ho Lee

CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation (2021.emnlp-main)

Challenge:	Existing CRSs lack capturing comprehensive user preferences . existing systems lack contextual knowledge to capture user preferences from a dialogue context .
Approach:	They propose a Contrastive Learning approach for Injecting Contextual Knowledge from Reddit data to a CRS task.
Outcome:	The proposed approach captures a user preference from a dialogue context without items . it improves on the existing methods, and the results are published in the journal of cognitive science.

Copied to clipboard

Wenchang Ma, Ryuichi Takanobu, Minlie Huang

LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation (2025.emnlp-industry)

Challenge:	Existing systems that explore user preference through conversational interactions do not exploit the context and knowledge to make accurate recommendations.
Approach:	They propose a model that performs tree-structured reasoning on a knowledge graph and generates informative dialog acts to guide language generation.
Outcome:	The proposed model can arrive at more accurate recommendation and generate more informative and engaging responses.

Copied to clipboard

Weizhi Zhang, Liangwei Yang, Wooseong Yang, Henry Peng Zou, Yuqing Liu, Ke Xu, Sourav Medya, Philip S. Yu

Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders (2026.acl-long)

Challenge:	Existing algorithms for collaborative filtering are limited by their computational demands and latency.
Approach:	They propose a framework to integrate pre-trained LLM embeddings into CF models through selective initialization strategies.
Outcome:	The proposed framework improves recommendation performance while maintaining low computational costs.

Copied to clipboard

Yupeng Hou, Jiacheng Li, Xiangjun Fu, Zhankui He, An Yan, Xiusi Chen, Julian McAuley

Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification (2025.emnlp-industry)

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.

Copied to clipboard

Huike Zou, Haiyang Yang, Yindu Su, Chen Li Yu, Qinye Xie, Chengbao Lian, Qingheng Zhang, Shuguang Han, Fei Huang, Jufeng Chen

Information Retrieval

Challenge:	Existing methods for product attribute value identification suffer from cascading errors and lack of generalization capability.
Approach:	They propose a multi-level retrieval scheme that uses products and attribute values as distinct hierarchical levels in PAVI domain.
Outcome:	The proposed method performs better than the state-of-the-art methods on a real-world industrial dataset.

Integrating Group-based Preferences from Coarse to Fine for Cold-start Users Recommendation (2025.coling-main)

Copied to clipboard

Siyu Wang, Jianhui Jiang, Jiangtao Qiu, Shengran Dai

Challenge:	Existing approaches to cross-domain recommendation (CDR) draw on historical purchase records or reviews to generate user representations.
Approach:	They propose a model that integrates preferences from coarse to fine levels to improve recommendations for cold-start users.
Outcome:	The proposed model outperforms state-of-the-art approaches on three CDR tasks.

Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback (2022.findings-naacl)

Copied to clipboard

Yunfan Hu, Zhaopeng Qiu, Xian Wu

Challenge:	Existing work on news recommendation only used positive and negative implicit feedback and suffered from the noise impact.
Approach:	They propose a denoising neural network for news recommendation with positive and negative implicit feedback, named DRPN.
Outcome:	The proposed method improves on the real-world large-scale dataset.

From ID to LLM: Rethinking Representation Learning for Recommendation (2026.acl-long)

Copied to clipboard

Song-Li Wu, Zhaocheng Du, Weinan Gan, Jingyi Wang, Xianquan Wang

Hierarchical User and Item Representation with Three-Tier Attention for Recommendation (N19-1)

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.

Copied to clipboard

Chuhan Wu, Fangzhao Wu, Junxin Liu, Yongfeng Huang

Towards Knowledge-Based Recommender Dialog System (D19-1)

Challenge:	Existing methods to learn user and item representations from reviews are limited . existing methods learn user representations based on ratings given by users .
Approach:	They propose a hierarchical user and item representation model with three-tier attention to learn user and items from reviews for recommendation.
Outcome:	The proposed model can learn user and item representations from reviews on four benchmark datasets.

Copied to clipboard

Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang

SPECTER: Document-level Representation Learning using Citation-informed Transformers (2020.acl-main)

Challenge:	Existing frameworks that only provide information about user preferences can be inaccurate in e-commerce recommender systems.
Approach:	They propose a framework which integrates the recommender system and dialog generation system by introducing information about users’ preferences.
Outcome:	The proposed framework can achieve better performance in both dialog generation and recommendation compared with baselines.

Copied to clipboard

Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, Daniel Weld

Folksonomication: Predicting Tags for Movies from Plot Synopses using Emotion Flow Encoded Neural Network (C18-1)

Challenge:	Recent Transformer language models do not leverage information on inter-document relatedness, which limits their document-level representation power.
Approach:	They propose a method to generate document-level embeddings using citation graphs.
Outcome:	The proposed method outperforms baselines on document-level tasks.

Copied to clipboard

Sudipta Kar, Suraj Maharjan, Thamar Solorio

Challenge:	Existing systems that generate tags for movies can help users better retrieve movies based on their personal preferences and user profiles.
Approach:	They propose a neural network model that merges synopses and emotion flows to predict a set of movies' tags.
Outcome:	The proposed model outperforms a machine learning system by learning 18% more tags than the previous one.

An Annotated Corpus of Textual Explanations for Clinical Decision Support (2022.lrec-1)

Copied to clipboard

Roland Roller, Aljoscha Burchardt, Nils Feldhus, Laura Seiffe, Klemens Budde, Simon Ronicke, Bilgin Osmanodja

RecMind: Large Language Model Powered Agent For Recommendation (2024.findings-naacl)

Challenge:	In recent years, machine learning for clinical decision support has gained more and more attention.
Approach:	They propose to use XAI to provide an explanation of a model's decision making process by constructing a corpus of sentences that are annotated with different semantic layers.
Outcome:	The proposed models outperform physicians on very specific, narrow tasks or can help physicians to work more efficiently.

Copied to clipboard

Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Yanbin Lu, Xiaojiang Huang, Yingzhen Yang

MPST: A Corpus of Movie Plot Synopses with Tags (L18-1)

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.

Copied to clipboard

Sudipta Kar, Suraj Maharjan, A. Pastor López-Monroy, Thamar Solorio

Tagging

Challenge:	a corpus of movie plot synopses and tags can be used to build automatic tagging systems . a method to collect these tags allows us to learn to predict tags from plot synoopsis .
Approach:	They propose to collect a corpus of movie plot synopses and 70 tags to analyze their properties.
Outcome:	The proposed method can be used to predict movie tags from plot synopses.

Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action (2025.findings-emnlp)

Copied to clipboard

Tongyoung Kim, Jeongeun Lee, SooJin Yoon, SungHwan Kim, Dongha Lee

Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents (2022.findings-acl)

Challenge:	Conversational Recommender Systems (CRSs) aim to engage users in dialogue to provide tailored recommendations.
Approach:	They propose a task that integrates preference elicitation, recommendation and persuasion . they propose CSALES, a conversational sales agent that infers contextual user profiles .
Outcome:	The proposed task integrates preference elicitation, recommendation, and persuasion within a conversational framework.

Copied to clipboard

Yicheng Zou, Hongwei Liu, Tao Gui, Junzhe Wang, Qi Zhang, Meng Tang, Haixiang Li, Daniell Wang

PersonaX: A Recommendation Agent-Oriented User Modeling Framework for Long Behavior Sequence (2025.findings-acl)

Challenge:	Existing text semantic matching models do not provide granularity for text comparison.
Approach:	They propose a simple yet effective training strategy for text semantic matching by disentangling keywords from intents.
Outcome:	The proposed approach achieves stable performance improvements against a wide range of models on three benchmarks.

Copied to clipboard

Yunxiao Shi, Wujiang Xu, Zhang Zeqi, Xing Zi, Qiang Wu, Min Xu

Challenge:	Existing methods for user profile modeling extract only partial segments from full historical behavior sequence, resulting in incomplete modeling and suboptimal profiling.
Approach:	They propose an agent-agnostic LLM-UM framework to augment downstream recommendation agents . it segments complete historical behaviors into clustered groups and performs offline multi-persona profiling .
Outcome:	The proposed framework improves agent performance and inference efficiency by 31% and 10% using 30–50% of behavioral data.

Stealthy Attack on Large Language Model based Recommendation (2024.acl-long)

Copied to clipboard

Jinghao Zhang, Yuting Liu, Qiang Liu, Shu Wu, Guibing Guo, Liang Wang

Robustness in NLP

Challenge:	Recent advances in recommender systems have been overlooked due to their emphasis on textual content.
Approach:	They propose to introduce large language models into recommendation models to exploit the semantic understanding and strong transferability of LLMs.
Outcome:	The proposed approach significantly boosts an item’s exposure by altering its textual content during the testing phase, without requiring direct interference with the model’s training process.

CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs (2021.emnlp-main)

Copied to clipboard

Jinfeng Zhou, Bo Wang, Ruifang He, Yuexian Hou

AGRec: Adapting Autoregressive Decoders with Graph Reasoning for LLM-based Sequential Recommendation (2025.findings-acl)

Challenge:	Existing conversational recommender systems (CRS) do not track the deep shift of user interest in conversations due to the complex of high-order and incomplete paths.
Approach:	They propose a conversational context-based reinforcement learning model which does explicit multi-hop reasoning on KGs with a contextual context-driven reinforcement learning framework.
Outcome:	Extensive experiments show that CRFR improves on paths of interest shift in knowledge graphs (KGs) .

Copied to clipboard

Xinfeng Wang, Jin Cui, Fumiyo Fukumoto, Yoshimi Suzuki

Structured Data in NLP

Challenge:	Autoregressive decoders in large language models excel at capturing sequential behaviors for generative recommendations, but they lack graph-structured user-item interactions, which are widely recognized as beneficial.
Approach:	They propose a novel algorithm that adapts LLMs’ decoders with graph reasoning for recommendation by augmenting the decoding logits with an auxiliary GNN model to optimize token generation.
Outcome:	The proposed model outperforms state-of-the-art models in sequential recommendations.

ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation (2026.findings-acl)

Copied to clipboard

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

Graph Neural News Recommendation with Unsupervised Preference Disentanglement (2020.acl-main)

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.

Copied to clipboard

Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, Ming Zhou

Decoding in Latent Spaces for Efficient Inference in LLM-based Recommendation (2025.findings-emnlp)

Challenge:	Existing methods to learn informative user and news representations fail to consider high-order connectivity underlying the user-news interactions.
Approach:	They propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement which can encode high-order relationships into user and news representations by information propagation along the graph.
Outcome:	The proposed model can encode high-order relationships into user and news representations by information propagation along the graph and disentangle latent preference factors by a neighborhood routing algorithm.

Copied to clipboard

Chengbing Wang, Yang Zhang, Zhicheng Wang, Tianhao Shi, Keqin Bao, Fuli Feng, Tat-Seng Chua

Green & Sustainable NLP

Challenge:	Light Latent-space Decoding (L2D) is an efficient and efficient latent- space decoding method.
Approach:	They propose to bypass language-space decoding by matching candidate items with LLM's internal thought representations in the latent space.
Outcome:	The proposed method is 10x faster than language-space decoding while maintaining or enhancing performance.

PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation (2020.findings-emnlp)

Copied to clipboard

Guangneng Hu, Qiang Yang

GuardEmb: Dynamic Watermark for Safeguarding Large Language Model Embedding Service Against Model Stealing Attack (2024.findings-emnlp)

Challenge:	Existing research focuses on the recommendation performance of the target domain while ignores the privacy leakage of the source domain.
Approach:	They propose to learn a privacy-aware neural representation by improving target performance while protecting source privacy.
Outcome:	The proposed model can disentangle the knowledge benefitting the transfer from leaking the privacy.

Copied to clipboard

Liaoyaqi Wang, Minhao Cheng

MidMed: Towards Mixed-Type Dialogues for Medical Consultation (2023.acl-long)

Challenge:	Recent studies reveal the risk of the model stealing attack, posing a financial threat to EaaS providers.
Approach:	They propose a dynamic embedding watermarking method that detects watermarks in embedded text . this method is a cross-platform approach that trains a verifier to detect watermark .
Outcome:	The proposed method enables an attacker to replicate the proposed method for profit without compromising embedding functionality.

Copied to clipboard

Xiaoming Shi, Zeming Liu, Chuan Wang, Haitao Leng, Kui Xue, Xiaofan Zhang, Shaoting Zhang

User Memory Reasoning for Conversational Recommendation (2020.coling-main)

Challenge:	Current medical dialogue systems assume that patients have explicit goals but are often unavailable in real-world situations due to the lack of medical knowledge.
Approach:	They propose a human-to-human mixed-type medical consultation dialogue corpus . they build benchmarking baselines on MidMed and propose an instruction-guiding framework . Experimental results show the effectiveness of InsMed .
Outcome:	The proposed system can help patients clarify their goals in real-world situations . it covers four departments with 8,309 dialogues and provides benchmarking baselines .

Copied to clipboard

Hu Xu, Seungwhan Moon, Honglei Liu, Bing Liu, Pararth Shah, Bing Liu, Philip Yu

Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network (D19-1)

Challenge:	Existing systems that update user preferences via asking relevant questions are unable to dynamically maintain and reason over their knowledge for current (and possibly future) recommendations.
Approach:	They propose a new memory graph (MG) -> Conversational Recommendation parallel corpus with 7K+ human-to-human role-playing dialogs and a graph-based reasoning model that updates MG from unstructured utterances and predicts optimal dialog policies based on updated MG.
Outcome:	The proposed model is based on a large-scale user memory bootstrapped from real-world user scenarios and can be easily updated from unstructured utterances.

Copied to clipboard

Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, Xing Xie

Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation (2020.coling-main)

Challenge:	Existing methods to learn user and item representations from review texts do not take into account the user-user and item-item relatedness of the user.
Approach:	They propose to use review content and user-item graphs to integrate them as different views.
Outcome:	The proposed approach can learn user and item representations from review content and user-item graphs.

Copied to clipboard

Zhongfen Deng, Hao Peng, Congying Xia, Jianxin Li, Lifang He, Philip Yu

Target-Guided Open-Domain Conversation (P19-1)

Challenge:	Existing methods for review rating prediction ignore hierarchies among data . paper review rating predictions are important for improving paper review process .
Approach:	They propose a Hierarchical bi-directional self-attention Network framework for paper review rating prediction and recommendation . they leverage hierarchical structure of paper reviews with three levels of encoders .
Outcome:	The proposed approach can be used to make an effective decision-making tool for the academic paper review process.

Copied to clipboard

Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric Xing, Zhiting Hu

Decoding Matters: Addressing Amplification Bias and Homogeneity Issue in Recommendations for Large Language Models (2024.emnlp-main)

Challenge:	a new study aims to improve opendomain chat systems by integrating goals and strategy into the system.
Approach:	They propose a structured approach that introduces coarse-grained keywords to control intended content of system responses and attains smooth conversation transition through turn-level supervised learning.
Outcome:	The proposed system produces meaningful and effective conversations significantly better than other approaches.

Copied to clipboard

Keqin Bao, Jizhi Zhang, Yang Zhang, Xinyue Huo, Chong Chen, Fuli Feng

Generating Domain-Specific Knowledge Graphs from Large Language Models (2025.findings-acl)

Challenge:	Existing approaches to adapt Large Language Models (LLMs) for recommendation encounter significant challenges such as amplification bias and homogeneity.
Approach:	They propose a new decoding approach called Debiasing-Diversifying Decoding (D3) that disables length normalization for ghost tokens to alleviate amplification bias and incorporates a text-free assistant model to encourage tokens less frequently generated by LLMs for counteracting recommendation homogeneity.
Outcome:	Extensive experiments on real-world datasets demonstrate the proposed approach’s effectiveness in enhancing accuracy and diversity.

Copied to clipboard

Marinela Parović, Ze Li, Jinhua Du

Enhancing Explainable Rating Prediction through Annotated Macro Concepts (2024.acl-long)

Challenge:	Large language models (LLMs) have shown impressive world knowledge across different benchmarks and domains but their knowledge is inconveniently scattered across their billions of parameters.
Approach:	They propose a prompt-based method to extract knowledge solely from LLMs’ parameters to construct domain-specific KGs by a schema-based process.
Outcome:	The proposed method generates large domain-specific KGs containing tens of thousands of entities and relations, and then evaluates against Wikidata, an open-source human-created KG.

Copied to clipboard

Huachi Zhou, Shuang Zhou, Hao Chen, Ninghao Liu, Fan Yang, Xiao Huang

Few-Shot Learning for Cold-Start Recommendation (2024.lrec-main)

Challenge:	Existing models learn user and item embeddings and generate reasons based on these embedds.
Approach:	They propose a concept-based explanation framework that leverages macro concepts to bridge the gap between the user/item embeddings and the recommendation reasons.
Outcome:	Extensive experiments on three datasets prove the proposed model is superior to existing models.

Copied to clipboard

Mingming Li, Songlin Hu, Fuqing Zhu, Qiannan Zhu

MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction (2023.findings-acl)

Challenge:	Existing methods for cold-start learning and recommendation are brittle to scenarios with few interactions.
Approach:	They propose a Few-shot learning method for Cold-Start recommendation that consists of three hierarchical structures that are local and global .
Outcome:	The proposed method improves on two public real-world datasets and is stable compared with the state-of-the-art.

Copied to clipboard

Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu, Dongfang Liu

VIP5: Towards Multimodal Foundation Models for Recommendation (2023.findings-emnlp)

Challenge:	Existing methods for product attribute value extraction focus on extracting values for a set of known attributes with sufficient training data.
Approach:	They propose a prompt tuning approach to extract attributes from product information using mixed prompts.
Outcome:	The proposed approach improves on two product benchmarks and shows parameter-efficient training and avoids model overfitting.

Copied to clipboard

Shijie Geng, Juntao Tan, Shuchang Liu, Zuohui Fu, Yongfeng Zhang

GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation (2026.findings-acl)

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.

Copied to clipboard

Lin Mu, Guoji Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context (2026.acl-long)

Challenge:	Existing methods for translating collaborative information into textual prompts or injecting pre-trained embeddings into the LLM treat structural information as static input and fail to capture high-order relational dependencies.
Approach:	They propose a framework that generalizes low-rank adaptation from independent to structure-aware propagation by embedding a trainable graph message-passing network within the low-ranked adaptation pathway.
Outcome:	Experiments on multiple benchmarks show that GraphLoRA outperforms state-of-the-art recommendation methods and achieves superior generalization.

Copied to clipboard

Zhongyu Ouyang, Qianlong Wen, Chunhui Zhang, Yanfang Ye, Soroush Vosoughi

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.

MAKED: Multi-lingual Automatic Keyword Extraction Dataset (2022.lrec-1)

Copied to clipboard

Yash Verma, Anubhav Jangra, Sriparna Saha, Adam Jatowt, Dwaipayan Roy

Information Extraction & Text Mining

Challenge:	a large dataset of news articles spanning 20 languages is lacking for keyword extraction.
Approach:	They propose a large-scale multi-lingual keyword extraction dataset for 11 of 20 languages . authors believe it will help advance the field of automatic keyword extraction .
Outcome:	The proposed dataset is the first for 11 of 20 languages and is based on 540K+ news articles from the BBC News network.

Resolving Indirect Referring Expressions for Entity Selection (2023.acl-long)

Copied to clipboard

Mohammad Javad Hosseini, Filip Radlinski, Silvia Pareti, Annie Louis

Graph-Structured Trajectory Extraction from Travelogues (2025.acl-long)

Challenge:	Recent advances in language modeling have enabled new conversational systems.
Approach:	They propose to use a dataset of indirect referring expressions to solve the problem of reference resolution when people use natural expressions . they propose to model the problem using 42K indirect referred expressions across three domains and a public dataset of entity pairs and utterances.
Outcome:	The proposed models achieve 82%-87% accuracy in realistic settings, while reasonable invites further advances.

Copied to clipboard

Aitaro Yamamoto, Hiroyuki Otomo, Hiroki Ouchi, Shohei Higashiyama, Hiroki Teranishi, Hiroyuki Shindo, Taro Watanabe

Information Extraction & Text Mining

Challenge:	Existing studies treat travelogues as sequences of visited locations, but they lack a benchmark dataset.
Approach:	They propose to represent the trajectory as a graph that can capture the hierarchy as well as the visiting order and construct a benchmark dataset for the extraction.
Outcome:	The proposed dataset shows that even naive baseline systems can predict visited locations and the visiting order between them, while it is more challenging to predict the hierarchical relations.

ClauseRec: A Clause Recommendation Framework for AI-aided Contract Authoring (2021.emnlp-main)

Copied to clipboard

Vinay Aggarwal, Aparna Garimella, Balaji Vasan Srinivasan, Anandhavelu N, Rajiv Jain

Information Retrieval

Challenge:	Contracts are a common type of legal document that frequent in business workflows, but there has been limited NLP research in understanding and generating them.
Approach:	They propose a task of clause recommendation to help automate contract authoring . they first predict if a specific clause type is relevant to be added in a contract . then they propose two-staged pipeline to recommend top clauses based on the contract context .
Outcome:	The proposed pipeline predicts if a clause type is relevant to be added in a contract and recommends the top clauses for the given type based on the contract context.

Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal Recommendation (2024.findings-emnlp)

Copied to clipboard

Yueqi Wang, Zhenrui Yue, Huimin Zeng, Dong Wang, Julian McAuley

TST: A Schema-Based Top-Down and Dynamic-Aware Agent of Text-to-Table Tasks (2025.acl-long)

Challenge:	integrating rich multimodal knowledge into recommender systems remains a challenge . despite performance improvements, different recommendation scenarios often require varying granularities.
Approach:	They propose a framework that captures item features at different granularities and learns informative representations for efficient recommendation across multiple dimensions.
Outcome:	The proposed framework achieves superior performance over state-of-the-art models on multiple benchmark datasets.

Copied to clipboard

Peiwen Jiang, Haitong Jiang, Ruhui Ma, Yvonne Jie Chen, Jinhua Cheng

Pay Attention to Implicit Attribute Values: A Multi-modal Generative Framework for AVE Task (2023.findings-acl)

Challenge:	Existing methods to extract text content based on static table structures neglect to deal with precise inner-document evidence extraction and dynamic information such as multiple entities and events.
Approach:	They propose a dynamic content extraction agent framework that uses type recognition to extract context evidences with the conduction of domain schema sequentially.
Outcome:	The proposed framework exhibits state-of-the-art (SOTA) performance on a large dataset.

Copied to clipboard

Yupeng Zhang, Shensi Wang, Peiguang Li, Guanting Dong, Sirui Wang, Yunsen Xian, Zhoujun Li, Hongzhi Zhang

Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation (2023.emnlp-main)

Challenge:	Existing approaches to extract attribute values from product descriptions are incomplete and noisy due to the tedious nature of this task.
Approach:	They propose a framework to extract attributes from product descriptions to acquire implicit attributes in addition to the explicit ones.
Outcome:	The proposed framework outperforms existing methods on the extraction of implicit attribute values while achieving comparable performance for the explicit ones.

Copied to clipboard

Yeongseo Jung, Eunseo Jung, Lei Chen

CLEAR: A Clinically Grounded Tabular Framework for Radiology Report Evaluation (2025.findings-emnlp)

Challenge:	Existing models that use separate recommendation and dialogue modules produce inconsistent results . a multi-task learning model is proposed to bridge the gap between recommendation and generated responses .
Approach:	They propose a multi-task learning model that integrates knowledge from two teachers and selectively gates between them via Contextualized Knowledge Distillation.
Outcome:	The proposed model significantly improves recommendation performance while enhancing fluency and achieves comparable results in terms of diversity.

Copied to clipboard

Yuyang Jiang, Chacha Chen, Shengyuan Wang, Feng Li, Zecong Tang, Benjamin M. Mervak, Lydia Chelala, Christopher M Straus, Reve Chahine, Samuel G. Armato Iii, Chenhao Tan

C2KD: Cross-layer and Cross-head Knowledge Distillation for Small Language Model-based Recommendation (2025.findings-acl)

Challenge:	Existing metrics lack the granularity and interpretability to capture nuanced clinical differences between candidate and ground-truth radiology reports.
Approach:	They propose a tabular framework with Expert-curated labels and an attribute-level comparison for radiology report evaluation (CLEAR)
Outcome:	The proposed framework can extract clinical attributes and provide automated metrics that are strongly aligned with clinical judgment.

Copied to clipboard

Xiao Chen, Changyi Ma, Wenqi Fan, Zhaoxiang Zhang, Li Qing

PepRec: Progressive Enhancement of Prompting for Recommendation (2024.emnlp-main)

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.

Copied to clipboard

Yakun Yu, Shi-ang Qi, Baochun Li, Di Niu

Stop Playing the Guessing Game! Evaluating Conversational Recommender Systems via Target-free User Simulation (2025.findings-emnlp)

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.

Copied to clipboard

SungHwan Kim, Kwangwook Seo, Tongyoung Kim, Jinyoung Yeo, Dongha Lee

DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)

Challenge:	despite advances in CRSs, reliably assessing their ability to elicit preferences remains a challenge.
Approach:	They propose a user-CRS evaluation protocol with target-free user simulators . they show that current evaluation metrics emphasize single-turn recall of target items .
Outcome:	The proposed evaluation protocol is based on a simulation-based evaluation environment.

Copied to clipboard

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.

One Planner To Guide Them All ! Learning Adaptive Conversational Planners for Goal-oriented Dialogues (2025.emnlp-main)

Copied to clipboard

Huy Quang Dao, Lizi Liao

Distilling LLM Reasoning into Dense Encoders: Bridging the Accuracy-Efficiency Gap in Recommendation (2026.findings-acl)

Challenge:	Existing methods for goal-oriented dialogues involve training separate models for specific combinations of objectives, leading to computational and scalability issues.
Approach:	They propose a new dialogue policy method that can adapt to varying objective preferences at inference time without retraining.
Outcome:	The proposed method can adapt to varying objective preferences at inference time without retraining.

Copied to clipboard

Donghee Han, Daeyoung Roh, A Young Kim, Hwanjun Song, Mun Yong Yi

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)

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.

Copied to clipboard

Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen

Green & Sustainable NLP

Challenge:	Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy.
Approach:	They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges.
Outcome:	The proposed framework reduces retrieval time while maintaining high model performance.

Leveraging Unpaired Feedback for Long-Term LLM-based Recommendation Tuning (2025.findings-emnlp)

Copied to clipboard

Jizhi Zhang, Chongming Gao, Wentao Shi, Xin Chen, Jingang Wang, Xunliang Cai, Fuli Feng

Challenge:	a recent study highlights unpaired feedback as a key challenge for long-term LLM-based recommenders . unpaired user feedback is crucial for improving LLMs in dynamic user environments, authors say .
Approach:	They propose a framework that incorporates unpaired feedback into LLMs to improve long-term recommendation performance.
Outcome:	The proposed framework improves long-term recommendation performance by incorporating unpaired feedback without requiring paired supervision.

Logit Space Constrained Fine-Tuning for Mitigating Hallucinations in LLM-Based Recommender Systems (2025.emnlp-main)

Copied to clipboard

Jianfeng Deng, Qingfeng Chen, Debo Cheng, Jiuyong Li, Lin Liu

Reasoning

Challenge:	Existing LLM-based recommender systems rely on standard fine-tuning methodologies, often ignoring hallucination issues during the fine-uning process.
Approach:	They propose a logit space constraint-based fine-tuning framework to mitigate hallucination in LLM-based recommenders by incorporating Kullback–Leibler divergence into the training objective.
Outcome:	Experiments on two recommendation models with distinct LLM backbones and four real-world datasets show that LCFT reduces hallucination and enhances recommendation performance.

TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification (2025.acl-long)

Copied to clipboard

Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen

Green & Sustainable NLP

Challenge:	Existing methods for product attribute value identification face critical challenges . seller-provided attribute values are often incomplete or inaccurate .
Approach:	They propose a retrieval-based method that uses taxonomy-aware contrastive learning . they use product profiles and candidate values to encode and retrieve attributes based on similarity .
Outcome:	The proposed method is based on a taxonomy-aware, hard negative sampling and adaptive inference with dynamic thresholds.

MemRec: Collaborative Memory-Augmented Agentic Recommender System (2026.acl-long)

Copied to clipboard

Weixin Chen, Yuhan Zhao, Jingyuan Huang, Zihe Ye, Mingxuan Ju, Tong Zhao, Neil Shah, Li Chen, Yongfeng Zhang