OpenRaiser/Intern-Atlas huggingface dataset:
Segmentation Similarity Enhanced Semantic Related Entity Fusion for Multi-modal Knowledge Graph Completion
Authors: Yunpeng Wang, Bo Ning, Xin Wang, Chengfei Liu, Guanyu Li
Venue: SIGIR
Year: 2025

Multi-modal Knowledge Graph Completion (MKGC) aims at leveraging multi-modal information to infer missing objective facts in incomplete multi-modal knowledge graphs, thereby significantly enhancing their expressive capabilities. The segmentation of semantic data, including image segmentation and word-level descriptions, often contain implicit relationships between entities that are frequently overlooked by existing methodologies, thus limiting the effectiveness of reasoning tasks. Therefore, we propose a novel completion inference method based on fine-grained semantic segmentation, which enhances reasoning capability by utilizing implicit relationships between entities. Primarily, we introduce the concept of Semantic Related Entity (SRE) and a novel SRE selection algorithm, which captures the semantic neighboring relationships of entities based on segmentation semantic similarity to fully exploit the semantic association information. Subsequently, we propose a Multi-modal Related Entity Fusion Transformer (M-REFT) model to effectively utilize SREs from semantic modalities and neighbors from structural modality for completion inference. The M-REFT employs a hierarchical Transformer architecture to encode the fusion modality representation between each entity and its SREs, and then decode the triplet representation with the neighbor information to identify missing entities in incomplete triplets. We conducted extensive comparative experiments with several state-of-the-art models on three datasets, demonstrating the significant performance advantages of M-REFT. A series of ablation experiments and case studies further validate the rationality and necessity of the SRE concept and the SRE selection algorithm.
Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models
Authors: Yuhao Wang, Junwei Pan, Pengyue Jia, Wanyu Wang, Maolin Wang, Zhixiang Feng, Xiaotian Li, Jie Jiang, Xiangyu Zhao
Venue: SIGIR
Year: 2025

Sequential Recommendation (SR) aims to leverage the sequential patterns in users' historical interactions to accurately track their preferences. However, the primary reliance of existing SR methods on collaborative data results in challenges such as the cold-start problem and sub-optimal performance. Concurrently, despite the proven effectiveness of large language models (LLMs), their integration into commercial recommender systems is impeded by issues such as high inference latency, incomplete capture of all distribution statistics, and catastrophic forgetting. To address these issues, we introduce a novel Pre-train, Align, and Disentangle (PAD) framework to enhance SR models with LLMs. In particular, we initially pre-train both the SR and LLM models to obtain collaborative and textual embeddings. Subsequently, we propose a characteristic recommendation-anchored alignment loss using multi-kernel maximum mean discrepancy with Gaussian kernels. Lastly, a triple-experts architecture, comprising aligned and modality-specific experts with disentangled embeddings, is fine-tuned in a frequency-aware manner. Experimental results on three public datasets validate the efficacy of PAD, indicating substantial enhancements and compatibility with various SR backbone models, particularly for cold items. The code and datasets are accessible for reproduction: https://github.com/Applied-Machine-Learning-Lab/PAD.
Enhancing Homophily in Heterogeneous Graph Contrastive Learning via Connection Strength and Multi-view Self-Expression
Authors: Haosen Wang, Chenglong Shi, Can Xu, Surong Yan, Rong Xie, Pan Tang
Venue: SIGIR
Year: 2025

Heterogeneous graph pre-training (HGP) has demonstrated remarkable performance across various domains. However, the issue of heterophily in real-world heterogeneous graphs (HGs) has been largely neglected. To bridge this research gap, we proposed a novel heterogeneous graph contrastive learning framework, termed HGMS, which leverages connection strength and multi-view self-expression to learn homophilous node representations. Specifically, we design a heterogeneous edge dropping augmentation strategy, which preferentially preserves metapath-based edges with strong connection strength, thereby improving the homophily of augmented views. Moreover, we propose a multi-view self-expressive learning method to infer the homophily between nodes from the subspace. In practice, we develop two approaches to solve the self-expressive matrix. The solved self-expressive matrix serves as an additional augmented view to provide homophilous information and is used to mitigate false negatives in contrastive loss. Extensive experimental results demonstrate the superiority of HGMS across different downstream tasks. The code is available at https://github.com/senllh/HGMS/tree/main.
InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning
Authors: Zheng Wang, Shu Xian Teo, Jun Jie Chew, Wei Shi
Venue: SIGIR
Year: 2025

Recent advancements in large language models (LLMs) have enabled their use as agents for planning complex tasks. Existing methods typically rely on a thought-action-observation (TAO) process to enhance LLM performance, but these approaches are often constrained by the LLMs' limited knowledge of complex tasks. Retrieval-augmented generation (RAG) offers new opportunities by leveraging external databases to ground generation in retrieved information. In this paper, we identify two key challenges (enlargability and transferability) in applying RAG to task planning. We propose InstructRAG, a novel solution within a multi-agent meta-reinforcement learning framework, to address these challenges. InstructRAG includes a graph to organize past instruction paths (sequences of correct actions), an RL-Agent with Reinforcement Learning to expand graph coverage for enlargability, and an ML-Agent with Meta-Learning to improve task generalization for transferability. The two agents are trained end-to-end to optimize overall planning performance. Our experiments on four widely used task planning datasets demonstrate that InstructRAG significantly enhances performance and adapts efficiently to new tasks, achieving up to a 19.2% improvement over the best existing approach.
GlFoMR: A Glance-then-Focus Multimodal Reasoning Framework for Diagram Question Answering
Authors: Yaxian Wang, Bifan Wei, Jun Liu, Lingling Zhang, Shuting He, Jun Li, Qika Lin
Venue: SIGIR
Year: 2025

Diagram question answering (DQA) is a challenging task that requires models to combine with domain-specific knowledge and reason over the diagrams to answer questions. Multimodal Large Language Models (MLLMs) have recently made notable strides in combining textual and visual information, emerging as a promising solution for addressing the DQA task. However, they still encounter challenges in deliberate multimodal reasoning over the fine-grained visual details of content-rich and knowledge-grounded diagrams. The tight interweaving of visual and textual reasoning for MLLMs is also susceptible to hallucinations. To overcome these limitations, we propose a Glance-then-Focus Multimodal Reasoning framework named GlFoMR for DQA, which features a flexible architecture for comprehensive visual and text interaction. Firstly, the diagram is parsed into a hierarchical structure spanning different granularities including isolated single-object, object-group, and whole-diagram. Subsequently, the Glance-Plan and Focus-Reason stages collaborate to decouple the complex reasoning process. Glance-Plan first generates a preliminary plan by glancing at the multimodal context, specifying sub-goals related to knowledge extraction, visual perception, and visual reasoning. Based on these sub-goals, Focus-Reason further integrates domain-specific knowledge and visual details to enable more deliberate reasoning. The parsed multi-granularity diagram information is seamlessly incorporated into the corresponding sub-goal achievement process, enhancing the perception and reasoning capabilities of MLLMs for better DQA performance. Extensive experimental results on four DQA datasets demonstrate that GlFoMR achieves substantial improvements, showcasing its potential to advance the development of multimodal reasoning.
SAFT: Structure-aware Transformers for Textual Interaction Classification
Authors: Hongtao Wang, Renchi Yang, Hewen Wang, Haoran Zheng, Jianliang Xu
Venue: SIGIR
Year: 2025

Textual interaction networks (TINs) are an omnipresent data structure used to model the interplay between users and items on e-commerce websites, social networks, etc., where each interaction is associated with a text description. Classifying such textual interactions (TIC) finds extensive use in detecting spam reviews in e-commerce, fraudulent transactions in finance, and so on. Existing TIC solutions either (i) fail to capture the rich text semantics due to the use of context-free text embeddings, and/or (ii) disregard the bipartite structure and node heterogeneity of TINs, leading to compromised TIC performance. In this work, we propose SAFT, a new architecture that integrates language- and graph-based modules for the effective fusion of textual and structural semantics in the representation learning of interactions. In particular, line graph attention (LGA)/gated attention units (GAUs) and pretrained language models (PLMs) are capitalized on to model the interaction-level and token-level signals, which are further coupled via the proxy token in an iterative and contextualized fashion. Additionally, an efficient and theoretically-grounded approach is developed to encode the local and global topology information pertaining to interactions into structural embeddings. The resulting embeddings not only inject the structural features underlying TINs into the textual interaction encoding but also facilitate the design of graph sampling strategies. Extensive empirical evaluations on multiple real TIN datasets demonstrate the superiority of SAFT over the state-of-the-art baselines in TIC accuracy.
Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator
Authors: Bing Wang, Bingrui Zhao, Ximing Li, Changchun Li, Wanfu Gao, Shengsheng Wang
Venue: SIGIR
Year: 2025

Over the past decade, social media platforms have been key in spreading rumors, leading to significant negative impacts. To counter this, the community has developed various Rumor Detection (RD) algorithms to automatically identify them using user comments as evidence. However, these RD methods often fail in the early stages of rumor propagation when only limited user comments are available, leading the community to focus on a more challenging topic named Rumor Early Detection (RED). Typically, existing RED methods learn from limited semantics in early comments. However, our preliminary experiment reveals that the RED models always perform best when the number of training and test comments is consistent and extensive. This inspires us to address the RED issue by generating more human-like comments to support this hypothesis. To implement this idea, we tune a comment generator by simulating expert collaboration and controversy and propose a new RED framework named CAMERED. Specifically, we integrate a mixture-of-expert structure into a generative language model and present a novel routing network for expert collaboration. Additionally, we synthesize a knowledgeable dataset and design an adversarial learning strategy to align the style of generated comments with real-world comments. We further integrate generated and original comments with a mutual controversy fusion module. Experimental results show that CAMERED outperforms state-of-the-art RED baseline models and generation methods, demonstrating its effectiveness.
Enhancing Cross-Domain Recommendation with Plug-In Contrastive Representations from Large Language Models
Authors: Ke Wang, Ji Zhang, Kuan Liu
Venue: SIGIR
Year: 2025

Cross-Domain Recommendation (CDR) leverages auxiliary information from extra domains to enhance performance by learning domain-invariant and domain-specific representations. However, existing methods primarily rely on ID information of users and items, resulting in the entanglement of these two representations and hindering effective knowledge transfer. Therefore, we present a novel plug-in contrastive learning for CDR (PicCDR), which utilizes textual semantics to disentangle and enhance domain-invariant and domain-specific representations via LLMs. First, PicCDR introduces the CoT prompting to generate and encode content-independent domain-invariant and domain-specific texts. Next, a contrastive domain-disentangled augmentation strategy is used to align domain-invariant and domain-specific representations in the semantic space with those of ID space via MI estimation. To further enhance representations, we present contrastive MI lower-bound and upper-bound approximations to optimize MI maximization and minimization terms. We also provide theoretical proof to reveal the superiority of our contrastive strategy. Lastly, we encapsulate PicCDR into a plug-and-play framework. This allows PicCDR to be plugged into any existing CDR model. Extensive experiments show the efficiency, robustness, and generalization of PicCDR.
Designing Search Engine Result Pages for Immersive Virtual Reality: Insights from Eye-Tracking and User Perception Data
Authors: Austin R. Ward, Bogeum Choi, Robert Capra
Venue: SIGIR
Year: 2025

Extensive research exists on user interactions with search engine result pages (SERPs) in desktop and mobile environments. However, relatively little work has focused on understanding how virtual reality (VR) users interact with SERPs in 3D immersive virtual environments (IVEs). Unlike 2D displays with established paradigms (e.g., ranked lists), 3D IVEs lack standardized methods for presenting search results. This work explores how different information arrangements in 3D virtual space impact users' search behaviors and preferences. This paper presents the results of an exploratory within-subjects user study that investigated search behaviors, perceptions, and eye-tracking behaviors for four different spatial arrangements of search results (''list'' - 2D list; ''curve3'' - 3x3 curved grid; ''curve4'' - 4x4 curved grid; and ''sphere'' - 4x4 semi-spherical grid) in a virtual reality across two different task types (Find All relevant, Pick 3 best). Thirty-two (32) participants completed 5 search trials in 8 experimental conditions (4 displays x 2 task types) for 40 total search trials. The findings from our analysis show: (1) participants were accepting of and more often preferred using the more immersive displays than the conventional list display (2) they generally preferred turning their heads (''physical navigation'') to scrolling (''virtual navigation'') for viewing results (3) they perceived scrolling back up a display as an act of backtracking but did not perceived head movements to previously viewed results as backtracking, and (4) they spent less time gazing at the right-most columns in the exhaustive search task. We discuss the implications of the results on designs for SERPs in 3D IVEs.
Deep Multiple Quantization Network on Long Behavior Sequence for Click-Through Rate Prediction
Authors: Zhuoxing Wei, Qi Liu, Qingchen Xie
Venue: SIGIR
Year: 2025

In Click-Through Rate (CTR) prediction, the long behavior sequence, comprising the user's long period of historical interactions with items has a vital influence on assessing the user's interest in the candidate item. Existing approaches strike efficiency and effectiveness through a two-stage paradigm: first retrieving hundreds of candidate-related items and then extracting interest intensity vector through target attention. However, we argue that the discrepancy in target attention's relevance distribution between the retrieved items and the full long behavior sequence inevitably leads to a performance decline. To alleviate the discrepancy, we propose the Deep Multiple Quantization Network (DMQN) to process long behavior sequence end-to-end through compressing the long behavior sequence. Firstly, the entire spectrum of long behavior sequence will be quantized into multiple codeword sequences based on multiple independent codebooks. Hierarchical Sequential Transduction Unit is incorporated to facilitate the interaction of reduced codeword sequences. Then, attention between the candidate and multiple codeword sequences will output the interest vector. To enable online serving, intermediate representations of the codeword sequences are cached, significantly reducing latency. Our extensive experiments on both industrial and public datasets confirm the effectiveness and efficiency of DMQN. The A/B test in our advertising system shows that DMQN improves CTR by 3.5% and RPM by 2.0%.
MSCRS: Multi-modal Semantic Graph Prompt Learning Framework for Conversational Recommender Systems
Authors: Yibiao Wei, Jie Zou, Weikang Guo, Guoqing Wang, Xing Xu, Yang Yang
Venue: SIGIR
Year: 2025

Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by interacting with users through conversations. Most existing studies of CRS focus on extracting user preferences from conversational contexts. However, due to the short and sparse nature of conversational contexts, it is difficult to fully capture user preferences by conversational contexts only. We argue that multi-modal semantic information can enrich user preference expressions from diverse dimensions (e.g., a user preference for a certain movie may stem from its magnificent visual effects and compelling storyline). In this paper, we propose a multi-modal semantic graph prompt learning framework for CRS, named MSCRS. First, we extract textual and image features of items mentioned in the conversational contexts. Second, we capture higher-order semantic associations within different semantic modalities (collaborative, textual, and image) by constructing modality-specific graph structures. Finally, we propose an innovative integration of multi-modal semantic graphs with prompt learning, harnessing the power of large language models to comprehensively explore high-dimensional semantic relationships. Experimental results demonstrate that our proposed method significantly improves accuracy in item recommendation, as well as generates more natural and contextually relevant content in response generation. Code and extended multi-modal CRS datasets are available at https://github.com/BIAOBIAO12138/MSCRS-main.
Retrieval Augmented Generation for Dynamic Graph Modeling
Authors: Yuxia Wu, Lizi Liao, Yuan Fang
Venue: SIGIR
Year: 2025

Modeling dynamic graphs, such as those found in social networks, recommendation systems, and e-commerce platforms, is crucial for capturing evolving relationships and delivering relevant insights over time. Traditional approaches primarily rely on graph neural networks with temporal components or sequence generation models, which often focus narrowly on the historical context of target nodes. This limitation restricts the ability to adapt to new and emerging patterns in dynamic graphs. To address this challenge, we propose a novel framework, Retrieval-Augmented Generation for Dy namic Graph modeling (RAG4DyG ), which enhances dynamic graph predictions by incorporating contextually and temporally relevant examples from broader graph structures. Our approach includes a time- and context-aware contrastive learning module to identify high-quality demonstrations and a graph fusion strategy to effectively integrate these examples with historical contexts. The proposed framework is designed to be effective in both transductive and inductive scenarios, ensuring adaptability to previously unseen nodes and evolving graph structures. Extensive experiments across multiple real-world datasets demonstrate the effectiveness of RAG4DyG in improving predictive accuracy and adaptability for dynamic graph modeling. The code and datasets are publicly available at https://github.com/YuxiaWu/RAG4DyG.
Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding
Authors: Yunjia Xi, Hangyu Wang, Bo Chen, Jianghao Lin, Menghui Zhu, Weiwen Liu, Ruiming Tang, Zhewei Wei, Weinan Zhang, Yong Yu
Venue: SIGIR
Year: 2025

The past few years have witnessed a growing interest in LLM-based recommender systems (RSs), although their industrial deployment remains in a preliminary stage. Most existing deployments leverage LLMs offline as feature enhancers, generating augmented knowledge for downstream tasks. However, in recommendation scenarios with numerous users and items, even offline knowledge generation with LLMs demands significant time and computational resources. This inefficiency arises from the autoregressive nature of LLMs. A promising solution is speculative decoding, a Draft-Then-Verify approach that increases the number of tokens generated per decoding step. In this work, we first identify recommendation knowledge generation as a highly fitting use case for retrieval-based speculative decoding. Then, we discern its two characteristics: (1) the vast number of items and users in RSs leads to retrieval inefficiency, and (2) RSs exhibit high diversity tolerance for LLM-generated text. Building on these insights, we introduce Lossless Acceleration via Speculative Decoding for LLM-based Recommender Systems (LASER), which features a Customized Retrieval Pool to enhance retrieval efficiency and Relaxed Verification to improve the acceptance rate of draft tokens. LASER achieves a 3-5x speedup on public datasets and saves about 67% of computational resources during the online A/B test on a large-scale advertising scenario with lossless downstream recommendation performance. Our code is available at https://github.com/YunjiaXi/LASER
SuperRS: Multi Scenario Reciprocal-Aware Dual MoE for Unified Recommendation-Search Ranking
Authors: Zihan Xia, Chuanyu Xu, Tao Zhang, Chengfu Huo
Venue: SIGIR
Year: 2025

In e-commerce, search and recommendation rankings require a deep understanding of user behaviors and personalized scoring of products. While existing systems maintain separate pipelines for search and recommendation, these two scenarios share aligned objectives and exhibit consistent data patterns during ranking. To address this, we propose a joint modeling approach for search-recommendation ranking that enables information gain exchange between the two scenarios, thus facilitating enhanced modeling of users' cross-scenario behaviors. Our proposed SuperRS framework employs a Dual-layer Multi-MoE (DualMoE) architecture to tackle scenario-specific disparities and achieve multi-interest fusion perception. A key aspect is the Search-Recommendation Sequence Fusion Unit, which integrates user interaction sequences from both scenarios. Additionally, we introduce a unified Representation Extraction method utilizing Reciprocal Scenario Interest Attention (RSIA) for feature alignment. Dynamic Feature Integration (DFI) employs a dual gating mechanism for controlled information fusion while preserving scenario identities, combined with multi-objective optimization. On the 1688 App, our framework demonstrates superior performance to baseline models across both offline evaluation metrics and online business indicators.