OpenRaiser/Intern-Atlas huggingface dataset:
BiListing: Modality Alignment for Listings
Authors: Guillaume Guy, Mihajlo Grbovic, Chun How Tan, Han Zhao
Venue: CIKM
Year: 2025

Airbnb is a leader in offering travel accommodations. Airbnb has historically relied on structured data to understand, rank, and recommend listings to guests due to the limited capabilities and associated complexity arising from extracting meaningful information from text and images. With the rise of representation learning, leveraging rich information from text and photos has become easier. A popular approach has been to create embeddings for text documents and images to enable use cases of computing similarities between listings or using embeddings as features in an ML model. However, an Airbnb listing has diverse unstructured data: multiple images, various unstructured text documents such as title, description, and reviews, making this approach challenging. Specifically, it is a non-trivial task to combine multiple embeddings of different pieces of information, i.e. each image, each review, etc., to reach a single meaningful listing representation, especially if some of the embeddings lie in different spaces. Faced with such a problem, practitioners often resort to unprincipled approaches of averaging embeddings to produce a single one. However, this often results in an inaccurate representation due to loss of information in the averaging process. This paper proposes BiListing, for Bimodal Listing, an approach to align text and photos of a listing by leveraging large-language models and pretrained language-image models. The BiListing approach has several favorable characteristics: capturing unstructured data into a single embedding vector per listing and modality, enabling zero-shot capability to search inventory efficiently in user-friendly semantics, overcoming the cold start problem, and enabling listing-to-listing search along a single modality, or both. We conducted offline and online tests to leverage the BiListing embeddings in the Airbnb search ranking model, and successfully deployed it in production, achieved 0.425% of NDCB gain, and drove tens of millions in incremental revenue.
Study on LLMs for Promptagator-Style Dense Retriever Training
Authors: Daniel Gwon, Nour Jedidi, Jimmy Lin
Venue: CIKM
Year: 2025

Promptagator demonstrated that Large Language Models (LLMs) with few-shot prompts can be used as task-specific query generators for fine-tuning domain-specialized dense retrieval models. However, the original Promptagator approach relied on proprietary and large-scale LLMs which users may not have access to or may be prohibited from using with sensitive data. In this work, we study the impact of open-source LLMs at accessible scales (≤14B parameters) as an alternative. Our results demonstrate that open-source LLMs as small as 3B parameters can serve as effective Promptagator-style query generators. We hope our work will inform practitioners with reliable alternatives for synthetic data generation and give insights to maximize fine-tuning results for domain-specific applications. Our code is available at https://www.github.com/mitll/promptodile
Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Welding
Authors: Yannik Hahn, Jan Voets, Antonin Königsfeld, Hasan Tercan, Tobias Meisen
Venue: CIKM
Year: 2025

Modern manufacturing relies heavily on fusion welding processes, including gas metal arc welding (GMAW). Despite significant advances in machine learning-based quality prediction, current models exhibit critical limitations when confronted with the inherent distribution shifts that occur in dynamic manufacturing environments. In this work, we extend the VQ-VAE Transformer architecture-previously demonstrating state-of-the-art performance in weld quality prediction-by leveraging its autoregressive loss as a reliable out-of-distribution (OOD) detection mechanism. Our approach exhibits superior performance compared to conventional reconstruction methods, embedding error-based techniques, and other established baselines. By integrating OOD detection with continual learning strategies, we optimize model adaptation, triggering updates only when necessary and thereby minimizing costly labeling requirements. We introduce a novel quantitative metric that simultaneously evaluates OOD detection capability while interpreting in-distribution performance. Experimental validation in real-world welding scenarios demonstrates that our framework effectively maintains robust quality prediction capabilities across significant distribution shifts, addressing critical challenges in dynamic manufacturing environments where process parameters frequently change. This research makes a substantial contribution to applied artificial intelligence by providing an explainable and at the same time adaptive solution for quality assurance in dynamic manufacturing processes-a crucial step towards robust, practical AI systems in the industrial environment.
Beyond Pairwise Learning-To-Rank At Airbnb
Authors: Malay Haldar, Daochen Zha, Huiji Gao, Liwei He, Sanjeev Katariya
Venue: CIKM
Year: 2025

There are three fundamental asks from a ranking algorithm: it should scale to handle a large number of items, sort items accurately by their utility, and impose a total order on the items for logical consistency. But here's the catch---no algorithm can achieve all three at the same time. We call this limitation the SAT theorem for ranking algorithms. Given the dilemma, how can we design a practical system that meets user needs? Our current work at Airbnb provides an answer, with a working solution deployed at scale. We start with pairwise learning-to-rank (LTR) models---the bedrock of search ranking tech stacks today. They scale linearly with the number of items ranked and perform strongly on metrics like NDCG by learning from pairwise comparisons. They are at a sweet spot of performance vs. cost, making them an ideal choice for several industrial applications. However, they have a drawback---by ignoring interactions between items, they compromise on accuracy. To improve accuracy, we create a ''true'' pairwise LTR model---one that captures interactions between items during pairwise comparisons. But accuracy comes at the expense of scalability and total order, and we discuss strategies to counter these challenges. Traveling further along the road to greater accuracy, we take each item in the search result, and compare it against the rest of the items along two dimensions: (1) Superiority: How strongly do searchers prefer the given item over the remaining ones? (2) Similarity : How similar is the given item to all the other items? This forms the basis of our ''all-pairwise'' LTR framework, which factors in interactions across all items at once. Looking at items on the search result page all together---superiority and similarity combined---gives us a deeper understanding of what searchers truly want. We quantify the resulting improvements in searcher experience through offline and online experiments at Airbnb.
Augmenting Limited and Biased RCTs through Pseudo-Sample Matching-Based Observational Data Fusion Method
Authors: Kairong Han, Weidong Huang, Taiyang Zhou, Peng Zhen, Kun Kuang
Venue: CIKM
Year: 2025

In the online ride-hailing pricing context, companies often conduct randomized controlled trials (RCTs) and utilize uplift models to assess the effect of discounts on customer orders, which substantially influences competitive market outcomes. However, due to the high cost of RCTs, the proportion of trial data relative to observational data is small, which only accounts for 0.65% of total traffic in our context, resulting in significant bias when generalizing to the broader user base. Additionally, the complexity of industrial processes reduces the quality of RCT data, which is often subject to heterogeneity from potential interference and selection bias, making it difficult to correct. Moreover, existing data fusion methods are challenging to implement effectively in complex industrial settings due to the high dimensionality of features and the strict assumptions that are hard to verify with real-world data. To address these issues, we propose an empirical data fusion method called pseudo-sample matching. By generating pseudo-samples from biased, low-quality RCT data and matching them with the most similar samples from large-scale observational data, the method expands the RCT dataset while mitigating its heterogeneity. We validated the method through simulation experiments, conducted offline and online tests using real-world data. In a week-long online experiment, we achieved a 0.41% improvement in profit, which is a considerable gain when scaled to industrial scenarios with hundreds of millions in revenue. In addition, we discuss the harm to model training, offline evaluation, and online economic benefits when the RCT data quality is not high, and emphasize the importance of improving RCT data quality in industrial scenarios. Further details of the simulation experiments can be found in the GitHub repository https://github.com/Kairong-Han/Pseudo-Matching.
From Bicliques to BiFlexi Cliques: A New Era of Bipartite Subgraph Discovery
Authors: Taejoon Han, Song Kim, Woungjae Choo, Junghoon Kim
Venue: CIKM
Year: 2025

Real-world bipartite communities tend to exhibit relaxed internal connectivity as their size increases, making traditional biclique models too restrictive for cohesive subgraph discovery. In this paper, we propose the Biflexi, a novel bipartite subgraph model that employs flexible, size-adaptive degree thresholds based on sublinear constraints. Our approach dynamically adjusts connectivity requirements according to subgraph size, enabling the discovery of larger and more realistic cohesive structures. We prove that the Maximum Biflexi problem is NP-hard and develop an efficient heuristic algorithm. Experimental results on real-world datasets demonstrate the effectiveness and scalability of our algorithm and the applicability of our model.
DocPolicyKG: A Lightweight LLM-Based Framework for Knowledge Graph Construction from Chinese Policy Documents
Authors: Chen Han, Yuanyuan Li, Xijin Tang
Venue: CIKM
Year: 2025

Chinese policy documents are typically written in a concise yet contextually rich style, containing implicit hierarchical logic and strategic intent. These linguistic and structural features present challenges for traditional information extraction methods, which often struggle with cross-sentence dependencies and semantic complexity. To address these, we propose DocPolicyKG, a novel framework for constructing knowledge graphs from Chinese policy documents using lightweight large language models (LLMs), integrating domain ontology, fine-tuning, and prompt engineering. Focusing on the investment promotion policies in China, experimental results demonstrate that DocPolicyKG significantly outperforms the base model Deepseek-R1-7B and achieves performance competitive with GPT-4o on both named entity recognition (NER) and relation triplet extraction (RTE) tasks. Building on DocPolicyKG, we construct the first large-scale knowledge graph of Chinese investment promotion policies, and further integrate Graph Retrieval-Augmented Generation (Graph RAG) to support policy question answering through entity-relation reasoning and semantic retrieval. The graph data is publicly available at: github.com/hanshenmesen/DocPolicyKG.
RAG-based Unanswerable Question Detection in Clinical Text-to-SQL
Authors: Donghee Han, Seungjae Lim, Mun Yong Yi
Venue: CIKM
Year: 2025

Large-scale language models (LLMs) have shown exceptional performance in various tasks, particularly in zero-shot and few-shot settings. However, in sensitive domains like healthcare, detecting unanswerable questions remains a critical challenge. This task is challenging due to data imbalance, and existing methods are computationally expensive and inflexible to data distribution changes. To address these issues, we propose Retrieval-augmented Question Answerability Detection (RaQAD), a training-free method that uses LLMs to identify unanswerable questions by retrieving semantically similar examples as few-shot prompts. RaQAD ensures semantically similar sampling, adapts to schema changes, and eliminates the need for additional training. Extensive experiments on clinical datasets demonstrate its effectiveness in outperforming existing approaches while addressing data imbalance challenges.
Development of Autonomous Failure Maintenance System for Semiconductor Manufacturing
Authors: Nuri Han, Jiwon Seo, Jonghee Ha, Jihyung Oh, Jinwoo Lee, Boram Jeong, Jongbin Park, Gilhwan Kim, Yohwan Joo
Venue: CIKM
Year: 2025

Semiconductor equipment failure analysis is critical in the fast-paced semiconductor industry, where complex and sensitive components are susceptible to failures that can lower productivity, raise costs, and shorten equipment lifespan. But conventional failure analysis methods rely on time series-based fault detection classification to identify the cause of failure, and have limitations in relying on experts for appropriate actions. We present a fully deployed autonomous maintenance system that closes the failure recovery loop, from detecting anomalies to executing corrective actions, by integrating graph-based log analysis, LLM-based semantic reasoning and hybrid retrieval mechanisms. Deployed across 762 photolithography equipment at Samsung Electronics fab from 2021 to 2025, the system handled over 85,000 real-world breakdowns, reduced Mean Time to Recovery by 7 minutes on average. This autonomous system enhances productivity, reduces costs, and maintains high product quality, supporting the transition to fully automated environments in the semiconductor industry.
MTGR: Industrial-Scale Generative Recommendation Framework in Meituan
Authors: Ruidong Han, Bin Yin, Shangyu Chen, He Jiang, Fei Jiang, Xiang Li, Chi Ma, Mincong Huang, Xiaoguang Li, Chunzhen Jing, Yueming Han, MengLei Zhou, Lei Yu, Chuan Liu, Wei Lin
Venue: CIKM
Year: 2025

Scaling law has recently been validated in the recommendation system, adopting generative recommendation strategies to achieve scalability. However, these generative approaches require abandoning the meticulously constructed cross features of traditional recommendation models,leading to a significant decline in model performance. To address this challenge, we propose Meituan Generative Recommendation, which is based on the HSTU architecture and is capable of retaining the original deep learning recommendation model (DLRM) features, including cross features. Additionally, MTGR achieves training and inference acceleration through user-level compression to ensure efficient scaling. We also propose Group-Layer Normalization (GLN) to enhance the performance of encoding within different semantic spaces and the dynamic masking strategy to avoid information leakage. We further optimize the training frameworks, enabling support for our models with 10 to 100 times computational complexity compared to the DLRM, without significant cost increases. MTGR achieved 65x FLOPs for single-sample forward inference compared to the DLRM model, resulting in the largest gain in nearly two years both offline and online. This breakthrough was successfully deployed on Meituan, the world's largest food delivery platform, where it has been handling the main traffic.
Mixture-of-KAN for Multivariate Time Series Forecasting
Authors: Xiao Han, Zhenduo Zhang, Xinfeng Zhang, Yiling Wu, Zhe Wu
Venue: CIKM
Year: 2025

Multivariate time series forecasting is a crucial task that predicts the future states based on historical inputs. Although current deep learning-based methods have made significant advancements, they still face the criticism of lacking interpretability. The rise of the Kolmogorov-Arnold Network (KAN) provides a new perspective to implement an efficient and interpretable deep learning-based method for forecasting time series. However, we find there are two main challenges in the application of KAN in time series forecasting: how to select the appropriate one from various KAN variants and how to train the deep KAN-based network. To this end, we propose the multi-layer mixture-of-KAN network, which achieves excellent performance while retaining KAN's ability to be transformed into a combination of symbolic functions. The core module is the mixture-of-KAN layer, which uses a mixture-of-experts structure to assign variables to best-matched KAN experts. Then, we analyze the shortcomings of parameter initialization in the original KAN and provide an effective initialization method to alleviate training instability. Extensive experimental results demonstrate that our proposed method is effective in multivariate time series forecasting. Codes are released in https://github.com/2448845600/EasyTSF.
KRAFT: A Knowledge Graph-Based Framework for Automated Map Conflation
Authors: Farnoosh Hashemi, Laks V. S. Lakshmanan
Venue: CIKM
Year: 2025

Digital maps play a crucial role in various applications such as navigation, fleet management, and ride-sharing, necessitating their accuracy and currency, which require timely updates. While the majority of geospatial databases (GDBs) provide high-quality information, their data is (i) limited to specific regions and/or (ii) missing some entities, even in their covered areas. Map conflation is the process of augmentation of a GDB using another GDB to conflate missing spatial features. Existing map conflation methods suffer from two main limitations: (1) They are designed for the conflation of linear objects (e.g., road networks) and cannot simply be extended to non-linear objects, thus missing information about most entities in the map. (2) They are heuristic algorithmic approaches that are based on pre-defined rules, unable to learn entities matching in a data-driven manner. To address these limitations, we design KRAFT, a learning based approach consisting of three parts: (1) Knowledge Graph Construction - where each GDB is represented by a knowledge graph, (2) Map Matching - where we use a knowledge graph alignment method as well as a geospatial feature encoder to match entities in obtained knowledge graphs, and (3) Map Merging - where we merge matched entities in the previous modules in a consistent manner, using a mixed integer linear programming formulation that fully merges the GDBs without adding any inconsistencies. Our experimental evaluation shows that not only does KRAFT achieve outstanding performance compared to state-of-the-art and baseline methods in map conflation tasks, but each of its modules (e.g., Map Matching and Map Merging) also separately outperforms traditional matching and merging methods.
LinkGPT: Leveraging Large Language Models for Enhanced Link Prediction in Text-Attributed Graphs
Authors: Zhongmou He, Jing Zhu, Shengyi Qian, Joyce Chai, Danai Koutra
Venue: CIKM
Year: 2025

Inspired by the success of Large Language Models (LLMs) in language and vision tasks, there has been growing interest in applying LLMs to graph tasks, particularly on Text-Attributed Graphs (TAGs). However, most prior work tackles the node classification task. In this work, we evaluate an LLM's ability to reason over structured data and infer new facts based on learned patterns by focusing on link prediction (LP)-the task of predicting missing links between nodes-that is understudied in the literature. This task poses two key challenges: (1) How to effectively integrate pairwise structural information, which is crucial for LP performance, into LLMs, and (2) how to address the computational bottleneck during inference. To tackle these challenges, we propose LinkGPT, the first LLM-based training and inference framework specifically designed for LP on homogeneous TAGs. To enhance the LLM's ability to understand the underlying structure, we carefully design a node encoder and pairwise encoder, and leverage a two-stage instruction tuning to effectively incorporate the nodewise and pairwise information into LLMs. For inference efficiency, we introduce a retrieval-reranking scheme. Extensive experiments show that LinkGPT achieves state-of-the-art performance on real-world graphs and demonstrates superior zero-shot and few-shot generalization. At inference time, it achieves a 10× speedup while maintaining high LP accuracy.
Cross-Domain Graph Neural Networks for Notification at LinkedIn
Authors: Shihai He, Julie Choi, Tianqi Li, Zhiwei Ding, Peng Du, Priya Bannur, Franco Liang, Fedor Borisyuk, Padmini Jaikumar, Xiaobing Xue, Viral Gupta
Venue: CIKM
Year: 2025

Notification recommendation systems are critical to driving user engagement on professional platforms like LinkedIn. Designing such systems involves integrating heterogeneous signals across domains, capturing temporal dynamics, and optimizing for multiple, often competing, objectives. Graph Neural Networks (GNNs) provide a powerful framework for modeling complex interactions in such environments. In this paper, we present a cross-domain GNN-based system deployed at LinkedIn that unifies user, content, and activity signals into a single, large-scale graph. By training on this cross-domain structure, our model significantly outperforms single-domain baselines on key tasks, including click-through rate (CTR) prediction and professional engagement. We introduce architectural innovations including temporal modeling and multi-task learning, which further enhance performance. Deployed in LinkedIn's notification system, our approach led to a 0.10% lift in weekly active users and a 0.62% improvement in CTR. We detail our graph construction process, model design, training pipeline, and both offline and online evaluations. Our work demonstrates the scalability and effectiveness of cross-domain GNNs in real-world, high-impact applications.
Information Diffusion Prediction Based on User Multi-Dimensional Feature Interaction
Authors: Jiaxing He, Yang Fang, Tianyang Shao, Xiang Zhao
Venue: CIKM
Year: 2025

Information diffusion prediction, the forecasting of propagation paths, provides critical insights into information spread mechanisms, directly enabling applications like misinformation spread forecasting and detection for malicious account. Prior research primarily focused on combining user social graphs and information cascades for prediction, often overlooking the distinct role characteristics users exhibit during interactions. Classifying users into different roles enables the construction of a multi-layered social graph, facilitating the extraction of deeper user features. This paper introduces a model that leverages multi-dimensional interactions between user features. Specifically, to account for users' dynamic preferences, we construct sequential hypergraphs from information cascades using timestamps and utilize a hypergraph neural network to extract users' dynamic features. Furthermore, to capture users' static features, we build multi-layer social networks from the social graph based on users' roles. We employ graph convolutional networks to separately extract static features from each layer and subsequently fuse them using an attention mechanism. Superior performance of our framework is evidenced by experimental validation on real-world datasets against cutting-edge benchmarks.