OpenRaiser/Intern-Atlas huggingface dataset:
SarRec: Statistically-guaranteed Augmented Retrieval for Recommendation
Authors: Tong Zhang, Nitin Bisht, Zihao Li, Guandong Xu, Xianzhi Wang
Venue: CIKM
Year: 2025

Recently, Large Language Models with Retrieval-Augmented Generation (RAG) have recently emerged as a powerful paradigm for sequential recommendation. However, existing methods typically retrieve items for each user without any principled mechanism for guaranteeing the reliability of generated recommendations, limiting their trustworthiness. To address this, we introduce SarRec : Statistically-guaranteed Augmented Retrieval for Recommendations, a framework that uses a simple retrieval step to provide relevant context and delivers calibrated, uncertainty-aware predictions with formal statistical guarantees. Specifically, SarRec first constructs the user's context set, utilizing a lightweight differentiable retrieval mechanism for identifying relevant context, and then calibrates the LLM's outputs by adapting the conformal prediction mechanism. We further provide a theoretical analysis that establishes an upper bound on the expected risk of recommendation performance metrics. Extensive experiments on multiple datasets from different domains validate the effectiveness of our framework.
ECLIPSE: Efficient Cross-Lingual Log Intelligence Parser with Semantic Entropy-Enhanced LCS Algorithm
Authors: Wei Zhang, Xianfu Cheng, Xiang Li, Jian Yang, Liying Zhang, Xiangyuan Guan, Zhoujun Li
Venue: CIKM
Year: 2025

Log parsing is essential in software engineering but is challenged by the immense complexity of log templates and diverse cross-platform and cross-lingual log semantics and structures in industrial logs. We propose ECLIPSE, an Efficient Cross-platform and Cross-lingual Log Intelligent Parsing framework with Semantic Entropy-Enhanced Longest Common Subsequence algorithm in industrial Environments. ECLIPSE leverages large language models to extract log keywords and maintains a dynamic dictionary mapping these keywords to log templates. When parsing, it retrieves candidate templates based on the keywords and log length. We design an algorithm named Semantic Entropy-Enhanced Longest Common Subsequence (Entropy-ELCS) for identifying the best template, improving token-level accuracy by incorporating information entropy and semantic elements into the longest common subsequence algorithm. The dictionary is updated with new keywords and templates for continuous improvement. Experiments on public benchmarks and our industrial log parsing benchmark ECLIPSE-BENCH demonstrate that ECLIPSE achieves strong performance and superior efficiency, especially when handling large template sets.
Billion-Scale Graph Deep Learning Framework for Ads Recommendation
Authors: Si Zhang, Weilin Cong, Dongqi Fu, Andrey Malevich, Hao Wu, Baichuan Yuan, Xin Zhou, Kaveh Hassani, Zhigang Hua, Austin Derrow-Pinion, Yan Xie, Xuewei Wang, Yinglong Xia, Ning Yao, Vena Li, Sem Park, Bo Long
Venue: CIKM
Year: 2025

In this paper, we systemically disentangle BHG, a graph deep learning framework for daily users' ads recommendations. BHG mainly relies on two pillars: (1) graph tokenization to convert the input temporal heterogeneous graph into sequences of tokens, and (2) graph MLP-Mixer neural architecture to learn node representations on sequences of tokens via a mini-batch manner. In general, BHG embraces three advantages: (1) flexibility, i.e., BHG can be seamlessly integrated with any existing industrial recommendation model by treating the learned node embeddings as additional features that encode interactions, (2) efficiency, i.e., the graph tokenization allows sampling the neighborhood both locally and globally, and reduces the number of nodes considered for aggregations, and (3) model simplicity, i.e., the graph MLP-Mixer does not require self-attention for aggregating nodes and hence enjoys the simplicity. We demonstrate the superior performance of the proposed BHG on two internal datasets and one public dataset. We hope this paper can share insights and explain large-scale graph deep learning deployments for researchers, engineers, and practitioners.
Edge-Variational Graph Neural Networks: Harnessing Weak Ties for Enhanced Default Risk Prediction
Authors: Feng Zhang, Jianfeng Chi, Rui Ma, Gang Chen, Rongqi Chen
Venue: CIKM
Year: 2025

Default risk prediction (DRP) leveraging financial relational networks (FRNs) has seen extensive application in recent years. Connection confidence within an FRN is pivotal in ensuring the efficacy of FRN-based DRP, with strong connections offering a more substantial predictive impact and weak connections providing supplementary predictive insights. However, in practical DRP implementations, the confidence in both strong and weak connections may be compromised due to fraudulent activities and misinformation caused by data collection biases, necessitating a novel method to effectively assess and calibrate low-confidence connections within the FRN to enhance DRP. To address this challenge, we propose a novel method named Edge-Variational Graph Neural Networks (EVGNN). During the graph encoding phase, EVGNN employs variational inference to assess connection confidence within the FRN, eliminating low-confidence connections and reinforcing weak connections with significant predictive value. In the decoding phase, EVGNN recodes network nodes based on the calibrated FRN structure, yielding refined node representations that bolster DRP. Empirical evaluation based on public datesets and a large-scale FRN dataset from a real-world DRP scenario validates the effectiveness of the proposed method in identifying connection confidence and affirms the validity of utilizing calibrated FRN to enhance DRP.
Meta-Adaptive Network for Effective Cold-Start Recommendation via Warm-Aware Representation Learning
Authors: Ao Zhang, Boya Du, Yulin Xu, Jialin Zhu, Yuning Jiang
Venue: CIKM
Year: 2025

Click-Through Rate (CTR) prediction models enable users to discover matched items in recommender systems. Industrial-scale models typically adopt a unified embedding approach for both hot and cold items. However, existing embedding-based models exhibit limitations in representation learning for cold items due to sparse historical user interactions. In this paper, we propose a Meta-Adaptive Network for Effective Cold-Start Recommendation (MANE). Inspired by meta-learning, we develop a lightweight plug-and-play meta-learner that generates enhanced representations to model full-lifecycle representations for cold items. Our meta-network dynamically adjusts the contribution of generalized features in final representations as item exposure increases, enabling adaptive balancing between generalization and specificity for cold items. In addition, certain high-potential items in cold-start scenarios face challenges in effective exposure due to limited interaction signals. Therefore, we further propose a novel representation learning method that incorporates a warm-aware contrastive loss, which aligns the representations of cold items with those of hot items exhibiting high multimodal similarity. Experimental results on the Taobao production dataset and online A/B testing validate the effectiveness of our method, achieving 4.34% item page views (IPV) and 2.84% CTR improvement.
LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosis
Authors: Weiming Zhang, Lingyue Fu, Qingyao Li, Kounianhua Du, Jianghao Lin, Jingwei Yu, Wei Xia, Weinan Zhang, Ruiming Tang, Yong Yu
Venue: CIKM
Year: 2025

Cognitive diagnosis (CD) plays a crucial role in intelligent education, evaluating students' comprehension of knowledge concepts based on their test histories. However, current CD methods often model students, exercises, and knowledge concepts solely on their ID relationships, neglecting the abundant semantic relationships present within the educational data space. Furthermore, contemporary intelligent tutoring systems (ITS) frequently involve the addition of new students and exercises, creating cold-start scenarios that ID-based methods find challenging to manage effectively. The advent of large language models (LLMs) offers the potential for overcoming this challenge with open-world knowledge. In this paper, we propose LLM4CD, which Leverages Large Language Models for open-world knowledge Augmented Cognitive Diagnosis. Our method utilizes the open-world knowledge of LLMs to construct cognitively expressive textual representations, which are then encoded to introduce rich semantic information into the CD task. Additionally, we propose an innovative bi-level encoder framework that models students' test histories through two levels of encoders: a macro-level cognitive text encoder and a micro-level knowledge state encoder. This approach substitutes traditional ID embeddings with semantic representations, enabling the model to accommodate new students and exercises with open-world knowledge and address the cold-start problem. Extensive experimental results demonstrate that LLM4CD consistently outperforms previous CD models on multiple real-world datasets, validating the effectiveness of leveraging LLMs to introduce rich semantic information into the CD task.
TopKNet: Learning to Perceive the Top-K Pivotal Nodes in Spatio-Temporal Data for Traffic Forecasting
Authors: Weilai Zhang, Dongbin Hu
Venue: CIKM
Year: 2025

Traffic prediction is a crucial research area in spatio-temporal forecasting. The key to traffic forecasting lies in the effective modelling of complex dependencies in spatio-temporal graph as well as capturing spatio-temporal heterogeneity. The majority of existing methodologies harness fully connected graphs derived from spatio-temporal graph neural networks or transformer-based models to model intricate spatio-temporal dependencies. However, it is paramount to recognize that not every node within this spatio-temporal graph contributes equally to the modeling of such dependencies. Consequently, the ability to discern and effectively leverage the significant nodes within the graph holds the key to enhancing the accuracy of traffic prediction models. In this paper, we center our attention on the pivotal nodes within the spatio-temporal graph and propose a novel method called TopKNet for effective traffic forecasting. Specifically, we introduce Time-Aware TopK Attention and TopK GCN for pivotal nodes within the temporal and spatial dimensions respectively. Moreover, Time-Aware Spatial Identity Embedding and Heterogeneity-Aware Loss are designed to characterise the spatio-temporal heterogeneity of nodes. Experiments on six real-world traffic datasets verify our proposed method's effectiveness compared to state-of-the-art baselines. These results offer fresh perspectives and insights that can enrich the endeavors of subsequent researchers working on the design and optimization of traffic models. The code will be made public at the following website https://github.com/randomforest1111/TopKNet.
Maps Ranking Optimization in Airbnb
Authors: Hongwei Zhang, Malay Haldar, Kedar Bellare, Sherry Chen, Soumyadip Banerjee, Xiaotang Wang, Mustafa Abdool, Huiji Gao, Pavan Tapadia, Liwei He, Sanjeev Katariya, Stephanie Moyerman
Venue: CIKM
Year: 2025

Search results on Airbnb are presented in two user interfaces: a list of rectangle cards, referred to as the feed result, that include photos, prices, ratings, and other details of the listings, and listing pins on the map, referred to as the map result, which can either display as price pins or appear without prices as mini-pins. The map plays a key role in Airbnb. Not only does it display the location of the listings in the search results, but it also serves as an interactive user interface that allows users to view the details of the pins and perform new searches by moving or zooming on the map. Majority of searches on Airbnb are conducted using the map, and majority of bookings are related to listings shown in map search results. Limited research has been conducted within the industry to address the unique challenges of maps ranking. For years, it was assumed that showing the top K results based on the model designed for feed ranking was also optimal for the map. However, this assumption simply breaks down when we take a closer look at the NDCG (Normalized Discounted Cumulative Gain) metric. Attention is key to NDCG, and the attention flow on the feed does not apply to the map. In this paper, we will begin with the NDCG theory and redesign map-specific NDCG by introducing three types of attention factors. We conducted a series of experiments to test whether optimizing map NDCG could drive the booking rates on Airbnb, and the results strongly supported this hypothesis.
SAKG: Structure-Aware Large Language Model Framework for Knowledge Graph Reasoning
Authors: Qingyu Zhang, Min Hu, Wenlong Fei, Jiaoyun Yang, Hongbo Li
Venue: CIKM
Year: 2025

Existing approaches to leveraging knowledge graphs in large language models often lack explicit structural modeling, which can lead to hallucinations and unstable reasoning over graph data. To address this, we propose SAKG, a structure-aware prompting framework designed to enhance the alignment between knowledge graph representations and language model inference. SAKG employs a hierarchical prompting strategy that integrates explicit task instructions, structural embeddings, and selectively filtered neighbor context. In particular, we introduce a progressive neighbor selection mechanism that combines relation co-occurrence statistics with embedding-based semantic similarity, ensuring that only informative and relevant neighbors are included in the prompt. This design enables the model to better capture relational semantics, structural dependencies, and contextual cues within the graph. Experimental results on multiple benchmarks demonstrate that SAKG consistently improves the effectiveness and factual consistency of knowledge graph reasoning with large language models.
Relation-Sensitive Visual Aggregation Enhances Multimodal Knowledge Graph Completion
Authors: Qingyu Zhang, Min Hu, Wenlong Fei, Jiaoyun Yang, Hongbo Li
Venue: CIKM
Year: 2025

Existing multimodal knowledge graph completion methods often overlook triple correlations between relations and images, limiting the expressiveness of multimodal embeddings. In this paper, we categorize triple correlations into Intra-triple Correlations (IaC) and Inter-triple Correlations (IeC), and propose a method called Relation-Sensitive Visual Aggregation (RSVA) to explicitly model them. Specifically, RSVA consists of two modules. The first is the Visual Semantic Aggregation Module, aggregating visual features for the central entity considering IaC. The second is the Contextual Neighbor Aggregation Module, capturing IeC by aggregating visual semantics from neighboring entities. In link prediction experiments, RSVA demonstrates the effect of IaC and IeC on the embeddings of central entities and achieves improved performance compared with previous approaches. These results demonstrate the effectiveness of RSVA, indicating that explicitly modeling the latent correlations between relations and images can enhance the representational capability of multimodal knowledge graphs.
Advancing Graph Isomorphism Tests with Metric Space Indicators: A Tool for Improving Graph Learning Tasks
Authors: Shenghui Zhang, Pak Lon Ip, Rongqin Chen, Shunran Zhang, Leong Hou U
Venue: CIKM
Year: 2025

To enhance the capability of Graph Neural Networks (GNNs) in judging graph isomorphism and graph classification tasks, this paper introduces a metric space-based graph isomorphism judgment method called the k-MSI test, which offers more topological information than the k-WL test and demonstrates superior graph isomorphism judgment capabilities compared to the k-WL test at the same complexity level. On the open test isomorphic dataset BREC, our k-MSI test accuracy rate is more than 11% ahead of the other methods. Furthermore, based on the k-MSI test, we propose a feature enhancement method Node Metric Indicator (NMI) that supplies additional topological information of graphs for GNNs and presents a novel GNN named Metric Space Indicators Graph Neural Network (MSIGNN). Experimental results on a publicly available benchmark graph classification task indicate that the NMI feature-based MSIGNN outperforms state-of-the-art methods on the BREC graph isomorphism test dataset and achieves satisfactory performance on real-world datasets.
Traffic Safety Evaluation Based on Macroscopic Traffic Features in Road Tunnels
Authors: Yupu Zhang, Lei Jia, Hao Miao, Weizhu Qian, Yan Zhao, Kai Zheng
Venue: CIKM
Year: 2025

Traffic accidents are one of the leading causes of death in the world. As an important part of the design of traffic roads, tunnels bring convenience but also have huge safety risks. To monitor road safety in real time and give timely warnings for drivers in tunnels, where the light is dark, the space is limited, and the signal is unstable, we study the problem of traffic safety evaluation based on macroscopic traffic features in road tunnels. In particular, we transform the problem into a four-classification problem. To overcome the long collection cycle of traffic crash data, we use the time-to-collision index as the standard for dividing safety levels of road sections in tunnels. To achieve the goal of collecting data in real time under the environment constraints of tunnels, we use macroscopic traffic features as input in our model. Specifically, we design a deep learning model, where the lane block can extract the interaction information of sequential road segments in the same lane, and the prediction block can integrate the results of the individual prediction of each lane and the overall prediction. An extensive emprical study with real data offers insight into the effectiveness and efficiency of the proposed model.
Global-Distribution Aware Scenario-Specific Variational Representation Learning Framework
Authors: Moyu Zhang, Yujun Jin, Jinxin Hu, Yu Zhang
Venue: CIKM
Year: 2025

Current recommendation methods typically use a unified framework to offer personalized recommendations for different scenarios provided by commercial platforms. However, they often employ shared bottom representations, which partially hinders the model's capacity to capture scenario uniqueness. Ideally, users and items should exhibit specific characteristics in different scenarios, prompting the need to learn scenario-specific representations to differentiate scenarios. Yet, variations in user and item interactions across scenarios lead to data sparsity issues, impeding the acquisition of scenario-specific representations. To learn robust scenario-specific representations, we introduce a Global-Distribution Aware Scenario-Specific Variational Representation Learning Framework (GSVR) that can be directly applied to existing multi-scenario methods. Specifically, considering the uncertainty stemming from limited samples, our approach employs a probabilistic model to generate scenario-specific distributions for each user and item in each scenario, estimated through variational inference (VI). Additionally, we introduce the global knowledge-aware multinomial distributions as prior knowledge to regulate the learning of the posterior user and item distributions, ensuring similarities among distributions for users with akin interests and items with similar side information. This mitigates the risk of users or items with fewer records being overwhelmed in sparse scenarios. Extensive experimental results affirm the efficacy of GSVR in learning more robust representations.
Hyperbolic Prompt Learning for Incremental Event Detection with LLMs
Authors: Xiujin Zhang, Wenxin Jin, Haotian Hong, Pengfei Zhang, Jiting Li, Kongjing Gu, Hao Peng, Li Sun
Venue: CIKM
Year: 2025

Class-incremental event detection (CIED) is essential for real-world information extraction systems, which must continually recognize new event types without forgetting past knowledge. The main challenge lies in balancing stability and adaptability under data imbalance. Existing methods often underuse the hierarchical and syntactic structures of language, and thus limit the generalization capacity. We propose HPLLM, a hyperbolic prompt-enhanced large language model framework, motivated by the observation that both embedding distributions and dependency graphs in event datasets exhibit hyperbolic properties. HPLLM integrates two key components: (1) Hyperbolic LoRA fine-tuning, enabling geometry-aware parameter adaptation for hierarchical semantics; and (2) Hyperbolic Adaptive Graph Diffusion Convolution (HADC), which encodes syntactic dependencies into structure-aware prompts for LLMs. Together, these techniques strengthen semantic discrimination, reduce forgetting, and improve adaptation across incremental stages. Extensive experiments on ACE2005 and MAVEN demonstrate that HPLLM consistently surpasses state-of-the-art baselines in macro-F1, achieving stronger retention of old knowledge and better generalization to new event types. In particular, the model shows clear gains on rare categories with few training mentions, demonstrating its robustness in imbalanced and few-shot regimes.
Augmenting Guest Search Results with Recommendations at Airbnb
Authors: Haowei Zhang, Philbert Lin, Dishant Ailawadi, Soumyadip Banerjee, Shashank Dabriwal, Hao Li, Kedar Bellare, Liwei He, Sanjeev Katariya
Venue: CIKM
Year: 2025

Users on Airbnb often perform exhaustive searches with varying conditions to find suitable accommodations. However, overly narrow search criteria can lead to insufficient results, causing frustration and abandonment of search journeys. To address these challenges, we developed flexible pivot recommendations that dynamically augment search results by suggesting alternative dates, relaxing amenity requirements, or adjusting price constraints. These recommendations align with users' broader travel intent, resulting in a measurable improvement in booking rates on the platform. Our solution introduces two key innovations: (1) a modular and extensible architecture to generate the flexible pivot recommendations that integrates seamlessly with Airbnb's existing search ranking system, enabling rapid iteration and minimizing maintenance overhead; and (2) an efficient approach leveraging transfer learning and a Mixture of Experts (MoE) architecture to rank recommendations alongside organic search results. This approach handles diverse scenarios, from single to multiple recommendations, while addressing cold-start challenges and supporting ongoing enhancements. Our solution's scalability and generalizability make it applicable to industries such as online travel agencies and e-commerce platforms, where users benefit from more diverse, intent-aligned recommendations.
C-FAITH: A Chinese Fine-Grained Benchmark for Automated Hallucination Evaluation
Authors: Xu Zhang, Zhifei Liu, Jiahao Wang, Huixuan Zhang, Fan Xu, Junzhe Zhang, Xiaojun Wan
Venue: CIKM
Year: 2025

Despite the rapid advancement of large language models, they remain highly susceptible to generating hallucinations, which significantly hinders their widespread application. Hallucination research requires dynamic and fine-grained evaluation. However, most existing hallucination benchmarks (especially in Chinese language) rely on human annotations, making automatical and cost-effective hallucination evaluation challenging. To address this, we introduce HaluAgent, an agentic framework that automatically constructs fine-grained question-answering (QA) dataset based on some knowledge documents. Our experiments demonstrate that the manually designed rules and prompt optimization can improve the quality of generated data. Using HaluAgent, we construct C-FAITH, a Chinese QA hallucination benchmark created from 1,399 knowledge documents obtained from web scraping, totaling 60,702 entries. We comprehensively evaluate 16 mainstream LLMs with our proposed C-FAITH, providing detailed experimental results and analysis.