OpenRaiser/Intern-Atlas huggingface dataset:
Improving Graph Autoencoders by Hard Sample Refinement with Global Similarity
Authors: Ge Chen, Yulan Hu, Sheng Ouyang, Cuicui Luo
Venue: CIKM
Year: 2025

Masked graph autoencoders (GAEs) have attracted significant attention in recent years. GAEs typically leverage graph neural networks to reconstruct topological properties and node features. However, existing feature-based GAEs face performance bottlenecks, particularly on hard-to-reconstruct nodes, due to their excessive reliance on local aggregation. To address this limitation, we propose a novel framework, Global-Similarity-Enhanced Graph Autoencoder (GSE-GAE). GSE-GAE adopts a knowledge distillation strategy within a self-supervised teacher-student architecture. Specifically, a teacher module integrates raw features and topology with long-range structural augmentations for hard nodes, while a representation alignment loss ensures effective transfer of global knowledge to the student model. Extensive experiments demonstrate the superiority of GSE-GAE, providing new insights into improving performance.
Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs
Authors: Yuhao Wang, Junwei Pan, Xinhang Li, Maolin Wang, Yuan Wang, Yue Liu, Dapeng Liu, Jie Jiang, Xiangyu Zhao
Venue: CIKM
Year: 2025

Sequential recommendation (SR) aims to capture users' dynamic interests and sequential patterns based on their historical interactions. Recently, the powerful capabilities of large language models (LLMs) have driven their adoption in SR. However, we identify two critical challenges in existing LLM-based SR methods: 1) embedding collapse when incorporating pre-trained collaborative embeddings and 2) catastrophic forgetting of quantized embeddings when utilizing semantic IDs. These issues dampen the model scalability and lead to suboptimal recommendation performance. Therefore, based on LLMs like Llama3-8B-instruct, we introduce a novel SR framework named MME-SID, which integrates multimodal embeddings and quantized embeddings to mitigate embedding collapse. Additionally, we propose a Multimodal Residual Quantized Variational Autoencoder (MM-RQ-VAE) with maximum mean discrepancy as the reconstruction loss and contrastive learning for alignment, which effectively preserve intra-modal distance information and capture inter-modal correlations, respectively. To further alleviate catastrophic forgetting, we initialize the model with the trained multimodal code embeddings. Finally, we fine-tune the LLM efficiently using LoRA in a multimodal frequency-aware fusion manner. Extensive experiments on three public datasets validate the superior performance of MME-SID thanks to its capability to mitigate embedding collapse and catastrophic forgetting. The implementation code and datasets are publicly available for reproduction: https://github.com/Applied-Machine-Learning-Lab/MME-SID.
DinoCompanion: An Attachment-Theory Informed Multimodal Robot for Emotionally Responsive Child-AI Interaction
Authors: Boyang Wang, Yuhao Song, Jinyuan Cao, Peng Yu, Hongcheng Guo, Zhoujun Li
Venue: CIKM
Year: 2025

Emotional development of children fundamentally relies on secure attachment relationships, yet current AI companions lack the theoretical foundation to provide developmentally appropriate emotional support. We introduce DinoCompanion, the first attachment-theory-grounded multimodal robot for emotionally responsive child-AI interaction. We address three critical challenges in child-AI systems: the absence of developmentally-informed AI architectures, the need to balance engagement with safety, and the lack of standardized evaluation frameworks for attachment-based capabilities. Our contributions include: (i) a multimodal dataset of 128 caregiver-child dyads containing 125,382 annotated clips with paired preference-risk labels, (ii) CARPO (Child-Aware Risk-calibrated Preference Optimization), a novel training objective that maximizes engagement while applying epistemic-uncertainty-weighted risk penalties, and (iii) AttachSecure-Bench, a comprehensive evaluation benchmark covering ten attachment-centric competencies with strong expert consensus. AttachSecure-Bench achieves state-of-the-art performance (57.15%), outperforming GPT-4o and Gemini-2.5-Pro, with exceptional secure base behaviors and superior attachment risk detection. Ablations validate the critical importance of multimodal fusion, uncertainty-aware risk modeling, and hierarchical memory for coherent, emotionally attuned interactions.
Decoder-only Pre-training Enhancement for Spatio-temporal Traffic Forecasting
Authors: Tao Yu, Junhong Wan, Yao Fu, Weihao Jiang, Jiang Zhu
Venue: CIKM
Year: 2025

Although spatio-temporal graph neural networks (STGNNs) become widely used methods in traffic forecasting, they still encounter an issue named short-sightedness. Specifically, due to high model complexity and GPU memory usage, STGNNs are restricted to processing only very short input time series. This limited context often causes STGNNs to focus on local variations and overlook long-term patterns, leading to misinterpretation of time series trends. To tackle this issue, recent studies propose to perform mask reconstruction pre-training on traffic series to enhance STGNNs. However, we argue that mask reconstruction is a suboptimal pre-training paradigm for traffic forecasting, because there exists a great gap between pre-training and downstream forecasting, caused by their inconsistent training targets. To eliminate this gap, we propose a new pre-training paradigm named next patch prediction and prove its advantages from both empirical and theoretical perspectives. Based on this paradigm, we introduce a new framework called Decoder-only Pre-training Enhancement (DoP) to unleash the potential of traffic pre-training model. Specifically, DoP uses Transformer decoders as infrastructure, and leverages next patch prediction as target to conduct pre-training. In addition, we propose a new dual-view temporal embedding to fully capture temporal information and spatial spectral enhancement to model spatial information. After pre-training, DoP enhances existing STGNNs seamlessly with periodic enhancement mechanism. On four real-world traffic benchmarks, we demonstrate its start-of-the-art performance.
Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
Authors: Shuo Yu, Mingyue Cheng, Qi Liu, Daoyu Wang, Jiqian Yang, Jie Ouyang, Yucong Luo, Chenyi Lei, Enhong Chen
Venue: CIKM
Year: 2025

Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach to mitigating the hallucination of large language models (LLMs) through the integration of external knowledge. While numerous efforts, most studies focus on a single type of external knowledge source. However, in real-world applications, most situations involve diverse knowledge from various sources, yet this area has been less explored. The main dilemma is the lack of a suitable dataset containing multiple knowledge sources and pre-exploration of the associated issues. To address these challenges, we standardize a benchmark dataset that combines structured and unstructured knowledge across diverse and complementary domains. Based on this dataset, we further develop a plug-and-play RAG framework, PruningRAG, whose main characteristic is the use of multi-granularity pruning strategies to optimize the integration of relevant information while minimizing misleading context. It consistently improves performance across various existing RAG variants, demonstrating its robustness and broad applicability. Building upon the standardized dataset and PruningRAG, we also report a series of experimental results, as well as insightful findings. Our dataset and code are publicly available. https://github.com/USTCAGI/PruningRAG, with the aim of advancing future research in the RAG community.
GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning
Authors: Yu Song, Zhigang Hua, Yan Xie, Jingzhe Liu, Bo Long, Hui Liu
Venue: CIKM
Year: 2025

Self-supervised learning (SSL) has shown great promise in graph representation learning. However, most existing graph SSL methods are developed and evaluated under a single-dataset setting, leaving their cross-dataset transferability largely unexplored and limiting their ability to leverage knowledge transfer and large-scale pretraining, factors that are critical for developing generalized intelligence beyond fitting training data. To address this gap and advance foundation model research for graphs, we present GSTBench, the first systematic benchmark for evaluating the transferability of graph SSL methods. We conduct large-scale pretraining on ogbn-papers100M and evaluate five representative SSL methods across a diverse set of target graphs. Our standardized experimental setup decouples confounding factors such as model architecture, dataset characteristics, and adaptation protocols, enabling rigorous comparisons focused solely on pretraining objectives. Surprisingly, we observe that most graph SSL methods struggle to generalize, with some performing worse than random initialization. In contrast, GraphMAE, a masked autoencoder approach, consistently improves transfer performance. We analyze the underlying factors that drive these differences and offer insights to guide future research on transferable graph SSL, laying a solid foundation for the ''pretrain-then-transfer'' paradigm in graph learning. Our code is available at https://github.com/SongYYYY/GSTBench.
TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation
Authors: Xue Xia, Saurabh Vishwas Joshi, Kousik Rajesh, Kangnan Li, Yangyi Lu, Nikil Pancha, Dhruvil Deven Badani, Jiajing Xu, Pong Eksombatchai
Venue: CIKM
Year: 2025

Modeling user action sequences has become a popular focus in industrial recommendation system research, particularly for Click-Through Rate (CTR) prediction tasks. However, industry-scale CTR models often rely on short user sequences, limiting their ability to capture long-term behavior. They also rarely address the infrastructure challenges involved in efficiently serving large-scale sequential models. Additionally, these models typically lack an integrated action-prediction task within a point-wise ranking framework, reducing their predictive power. We introduce TransAct V2, a production model for Pinterest's Homefeed ranking system, featuring three key innovations: (1) leveraging very long user sequences to improve CTR predictions, (2) employing scalable, low-latency deployment solutions tailored to handle the computational demands of extended user action sequences, and (3) integrating a Next Action Loss function for enhanced user action forecasting. To overcome latency and storage constraints, we leverage efficient data-processing strategies and model-serving optimizations, enabling seamless industrial-scale deployment. Our approach's effectiveness is further demonstrated through ablation studies. Furthermore, extensive offline and online A/B experiments confirm major gains in key metrics, including engagement volume and recommendation diversity, showcasing TransAct V2's real-world impact.
Fine-Grained Graph Rationalization
Authors: Zhe Xu, Menghai Pan, Yuzhong Chen, Huiyuan Chen, Yuchen Yan, Mahashweta Das, Hanghang Tong
Venue: CIKM
Year: 2025

Rationale discovery is defined as finding a subset of the input data that maximally supports the prediction of downstream tasks. In the context of graph machine learning, graph rationale is defined as identifying the critical subgraph in the given graph topology. In contrast to the rationale subgraph, the remaining subgraph is named the environment subgraph. Graph rationalization can enhance the model performance because the mapping between the graph rationale and the prediction label is viewed as invariant, by definition. To ensure the discriminative power of the extracted rationale subgraphs, a key technique named intervention is applied, whose core idea is that given changing environment subgraphs, the semantics from the rationale subgraph is invariant, which guarantees the correct prediction result. However, most, if not all, of the existing graph rationalization methods develop their intervention strategies on the graph level, which is coarse-grained. In this paper, we propose FIne-grained Graph rationalization (FIG). Our idea is driven by the self-attention mechanism, which provides rich interactions between input nodes. Based on that, FIG can achieve node-level and virtual node-level intervention. Our experiments involve 7 real-world datasets, and the proposed FIG shows significant performance advantages compared to 13 baseline methods.
Learning to Comparison-Shop
Authors: Jie Tang, Daochen Zha, Xin Liu, Huiji Gao, Liwei He, Stephanie Moyerman, Sanjeev Katariya
Venue: CIKM
Year: 2025

In online marketplaces like Airbnb, users frequently engage in comparison shopping before making purchase decisions. Despite the prevalence of this behavior, a significant disconnect persists between mainstream e-commerce search engines and users' comparison needs. Traditional ranking models often evaluate items in isolation, disregarding the context in which users compare multiple items on a search results page. While recent advances in deep learning have sought to improve ranking accuracy, diversity, and fairness by encoding listwise context, the challenge of aligning search rankings with user comparison shopping behavior remains inadequately addressed. In this paper, we propose a novel ranking architecture - Learning-to-Comparison-Shop (LTCS) System - that explicitly models and learns users' comparison shopping behaviors. Through extensive offline and online experiments, we demonstrate that our approach yields statistically significant gains in key business metrics - improving NDCG by 1.7% and boosting booking conversion rate by 0.6% in A/B testing - while also enhancing user experience. We also compare our model against state-of-the-art approaches and demonstrate that LTCS significantly outperforms them.
Dual Context-Aware Negative Sampling Strategy for Graph-based Collaborative Filtering
Authors: Xi Wu, Wenzhe Zhang, Liangwei Yang, Xiaohan Fang, Jiquan Peng, Jibing Gong
Venue: CIKM
Year: 2025

Negative sampling plays a critical role in collaborative filtering (CF), as it accelerates convergence and improves recommendation accuracy. Among recent studies, mixup-based negative sampling has shown promising performance. However, existing methods primarily focus on increasing the similarity between the synthesized negative and the positive item, without considering the false positive issue commonly found in implicit feedback scenarios. Blindly training all positive samples with overly hard negatives can magnify the impact of false positives and hurt recommendation performance. To address this challenge, we first provide a theoretical analysis revealing that mixup-synthesized hard negatives implicitly reweight the similarity difference between the user's interactions and both the positive and negative boundaries, thereby shaping the training signal. Motivated by this, we propose a novel strategy named Dual Context-Aware Negative Sampling (DCANS), which enhances each positive item by assessing its alignment with the user's interest context, and simultaneously adjusts the hardness of synthesized negatives based on their relevance to the same interest context. This strategy optimizes the training direction toward the user's genuine preferences, mitigating the negative impact of false positives while preserving the benefits of hard negative sampling. Extensive experiments on three benchmark datasets demonstrate that our method achieves consistent improvements over state-of-the-art baselines. Our PyTorch implementation is available https://github.com/Wu-Xi/DCANS.
Calibrated and Diverse News Coverage
Authors: Tianyi Zhou, Stefan Neumann, Kiran Garimella, Aristides Gionis
Venue: CIKM
Year: 2025

In recent years, there has been a debate about whether automated news aggregators, like Google News, lead readers to content that reinforces their existing beliefs and restricts their exposure to a biased subset of perspectives. To avoid bias, it has become common practice that news aggregators provide articles based on source diversity: for each story, they pick articles from news sources with different political leanings. In this paper, we ask whether this practice is sufficient. In particular, we study how well the diversity of viewpoints, in particular with respect to entities, is covered by articles picked using plain source diversity. We analyze a dataset fetched from Google News and find that, even though the top articles exhibit some diversity with respect to the leanings of the news outlets, many possible viewpoints towards the entities are missing. Based on this observation we design novel methods for selecting a small set of articles that cover all possible viewpoints; to ensure that our selections are useful we show how to incorporate the user preferences into our model. Our experiments on four real-world datasets show that our algorithms cover significantly more different viewpoints than previous baselines.
To Know What User Concerns: Conceptual Knowledge Reasoning for User Satisfaction Estimation in E-Commerce Dialogue Systems
Authors: Li Lin, Yaochang Liu, Kaiwen Xia, Shuai Wang
Venue: CIKM
Year: 2025

With the development of generative models, dialogue systems play an important role in many web applications, such as E-commerce and Question-Answering websites. The accurate user satisfaction estimation (USE) is a critical problem in measuring the quality of dialogue systems. In e-commerce, users usually seek consultation through dialogue systems to know detailed information about the products they intend to purchase. Existing studies mainly focus on analyzing user sentiment in a dialogue for USE, neglecting to understand what the user is concerned about when requesting a consultation. It may cause fatal errors when the response is emotionally friendly but non-informative. Thus, to evaluate how a dialogue satisfies the user's requirements, it is essential to have a conceptual understanding of the products to determine if the response has addressed the user's question. In this paper, we propose a knowledge-enhanced USE model named CoRe-USE, which introduces the Conceptual Knowledge Reasoning for USE in E-Commerce Dialogue Systems. We first design a simple yet efficient entity linking and relation selection module enabling conceptual reasoning in each dialogue. Then, we propose a hierarchical encoder to capture the contextual information in multi-turn dialogues. Finally, we introduce a knowledge enhancement module to fuse conceptual reasoning into contextual embeddings to produce USE. For evaluation, we conduct experiments on three real-world datasets in various scenarios, the results demonstrate the effectiveness and robustness of CoRe-USE compared with SOTA baselines.
TalkDep: Clinically Grounded LLM Personas for Conversation-Centric Depression Screening
Authors: Xi Wang, Anxo Pérez, Javier Parapar, Fabio Crestani
Venue: CIKM
Year: 2025

The increasing demand for mental health services has outpaced the availability of real training data to develop clinical professionals, leading to limited support for the diagnosis of depression. This shortage has motivated the development of simulated or virtual patients to assist in training and evaluation, but existing approaches often fail to generate clinically valid, natural, and diverse symptom presentations. In this work, we embrace the recent advanced language models as the backbone and propose a novel clinician-in-the-loop patient simulation pipeline, TalkDep, with access to diversified patient profiles to develop simulated patients. By conditioning the model on psychiatric diagnostic criteria, symptom severity scales, and contextual factors, our goal is to create authentic patient responses that can better support diagnostic model training and evaluation. We verify the reliability of these simulated patients with thorough assessments conducted by clinical professionals. The availability of validated simulated patients offers a scalable and adaptable resource for improving the robustness and generalisability of automatic depression diagnosis systems.
AutoRuleSQL: Hybrid Text-to-SQL via Rule-Driven Fast Paths and LLM Bootstrapping
Authors: Han Xu, Yang Li, Yanhai Xiong, Robert Mintern, Amir Louka, Haipeng Chen
Venue: CIKM
Year: 2025

Natural Language to SQL (NL2SQL) enables natural language access to structured data, but LLM-based methods can be inefficient for real-time use and repetitive query patterns. We present AutoRuleSQL, a hybrid system that combines template-based fast paths with LLM fallback and offline bootstrapping. Empirical results show that it reduces latency by over 12.6% and improves execution accuracy by up to 4.0%, when combined with existing NL2SQL methods.
Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark
Authors: Xiaopeng Li, Jingtong Gao, Pengyue Jia, Xiangyu Zhao, Yichao Wang, Wanyu Wang, Yejing Wang, Yuhao Wang, Huifeng Guo, Ruiming Tang
Venue: CIKM
Year: 2025

Multi-Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained considerable attention. However, current research in MSR faces two significant challenges that hinder the field's development: the absence of uniform procedures for multi-scenario dataset processing, thus hindering fair comparisons, and most models being closed-source, which complicates comparisons with current SOTA models. Consequently, we introduce our benchmark, Scenario-Wise Rec, which comprises six public datasets and twelve baseline models, along with a training and evaluation pipeline. We further validate Scenario-Wise Rec on an industrial advertising dataset, underscoring its robustness. We hope the benchmark will give researchers clear insights into prior work, enabling them to develop novel models and thereby fostering a collaborative research ecosystem in MSR. Our source code is publicly available (https://github.com/Applied-Machine-Learning-Lab/Scenario-Wise-Rec).
Reliable and Efficient Container Orchestration of LLMs via MCP
Authors: Han Xu, Xingyuan Wang, Shihao Ji
Venue: CIKM
Year: 2025

This paper presents a structured decoding approach to support reliable and efficient container orchestration using large language models (LLMs) in conjunction with the Model Context Protocol (MCP), a standard interface for LLMs to interact with Docker and Kubernetes. We address key challenges in using LLMs for container orchestration: high token overhead from outputs and the risk of generating invalid or unsafe commands. Empirical results demonstrate up to a 76.2% latency reduction.
MHSNet: An MoE-based Hierarchical Semantic Representation Network for Accurate Duplicate Resume Detection with Large Language Model
Authors: Yu Li, Zulong Chen, Wenjian Xu, Hong Wen, Yipeng Yu, Man Lung Yiu, Yuyu Yin
Venue: CIKM
Year: 2025

To maintain the company's talent pool, recruiters need to continuously search for resumes from third-party websites (e.g., LinkedIn, Indeed). However, fetched resumes are often incomplete and inaccurate. To improve the quality of third-party resumes and enrich the company's talent pool, it is essential to conduct duplication detection between the fetched resumes and those already in the company's talent pool. Such duplication detection is challenging due to the semantic complexity, structural heterogeneity, and information incompleteness of resume texts. To this end, we propose MHSNet, an multi-level identity verification framework that fine-tunes BGE-M3 using contrastive learning. With the fine-tuned BGE-M3, MHSNet generates multi-level sparse and dense representations for resumes, enabling the computation of corresponding multi-level semantic similarities. Moreover, the state-aware Mixture-of-Experts (MoE) is employed in MHSNet to handle diverse incomplete resumes. Experimental results verify the effectiveness of MHSNet.