OpenRaiser/Intern-Atlas huggingface dataset:
Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering
Authors: Zheyu Chen, Jinfeng Xu, Yutong Wei, Ziyue Peng
Venue: SIGIR
Year: 2025

Contrastive Learning (CL) has recently emerged as a powerful technique in recommendation systems, particularly for its capability to harness self-supervised signals from perturbed views to mitigate the data sparsity problem. The process of constructing perturbed views of the user-item bipartite graph and performing CL between perturbed views in a graph convolutional network (GCN) is called graph contrastive learning (GCL). Although existing GCL-based models are effective, the weight assignment method for perturbed views has not been fully explored. A critical problem in existing GCL-based models is the irrational allocation of feature attention. This problem limits the model's ability to effectively leverage crucial features, resulting in suboptimal performance. To address this, we propose a Weighted Graph Contrastive Learning framework (WeightedGCL). Specifically, WeightedGCL applies a robust perturbation strategy, which perturbs only the view of the final GCN layer. In addition, WeightedGCL incorporates a squeeze and excitation network to dynamically weight the features of the perturbed views. Our WeightedGCL strengthens the model's focus on crucial features and reduces the impact of less relevant information. Extensive experiments on widely used datasets demonstrate that our WeightedGCL achieves significant accuracy improvements compared to competitive baselines.
ir_explain: A Python Library of Explainable IR Methods
Authors: Sourav Saha, Harsh Agarwal, Venktesh V, Avishek Anand, Swastik Mohanty, Debapriyo Majumdar, Mandar Mitra
Venue: SIGIR
Year: 2025

While recent advancements in Neural Ranking Models have resulted in significant improvements over traditional statistical retrieval models, it is generally acknowledged that the use of large neural architectures and the application of complex language models in Information Retrieval (IR) have reduced the transparency of retrieval methods. Consequently, Explainability and Interpretability have emerged as important research topics in IR. Several axiomatic and post-hoc explanation methods, as well as approaches that attempt to be interpretable-by-design, have been proposed. We present ir_explain, an open-source Python library that implements a variety of well-known techniques for Explainable IR (ExIR) within a common, extensible framework. It supports the three standard categories of post-hoc explanations, namely pointwise, pairwise, and listwise explanations. The library is designed to make it easy to reproduce state-of-the-art ExIR baselines on standard test collections, as well as to explore new approaches to explaining IR models and methods. To facilitate adoption, ir_explain is well-integrated with widely-used toolkits such as Pyserini, PyTerrier (work in progress) and ir_datasets. Downstream applications of ir_explain include explaining the Retrieval-Augmented Generation (RAG) pipeline. The development version of the library is available on GitHub. We release the library as a pip package (https://pypi.org/project/ir-explain/); source code is available from https://github.com/souravsaha/ir_explain.
Constrained Auto-Regressive Decoding Constrains Generative Retrieval
Authors: Shiguang Wu, Zhaochun Ren, Xin Xin, Jiyuan Yang, Mengqi Zhang, Zhumin Chen, Maarten de Rijke, Pengjie Ren
Venue: SIGIR
Year: 2025

Generative retrieval seeks to replace traditional search index data structures with a single large-scale neural network, offering the potential for improved efficiency and seamless integration with generative large language models. As an end-to-end paradigm, generative retrieval adopts a learned differentiable search index to conduct retrieval by directly generating document identifiers through corpus-specific constrained decoding. The generalization capabilities of generative retrieval on out-of-distribution corpora have gathered significant attention. Recent advances primarily focus on the problems arising from training strategies, and addressing them through various learning techniques. However, the fundamental challenges of generalization arising from constrained auto-regressive decoding still remain unexplored and systematically understudied. In this paper, we examine the inherent limitations of constrained auto-regressive generation from two essential perspectives: constraints and beam search. We begin with the Bayes-optimal setting where the generative retrieval model exactly captures the underlying relevance distribution of all possible documents. Then we apply the model to specific corpora by simply adding corpus-specific constraints. Our main findings are two-fold: (i) For the effect of constraints, we derive a lower bound of the error, in terms of the KL divergence between the ground-truth and the model-predicted step-wise marginal distributions. This error arises due to the unawareness of future constraints during generation and is shown to depend on the average Simpson diversity index of the relevance distribution. (ii) For the beam search algorithm used during generation, we reveal that the usage of marginal distributions may not be an ideal approach. Specifically, we prove that for sparse relevance distributions, beam search can achieve perfect top-1 precision but suffer from poor top-k recall performance. To support our theoretical findings, we conduct experiments on synthetic and real-world datasets, validating the existence of the error from adding constraints and the recall performance drop due to beam search. This paper aims to improve our theoretical understanding of the generalization capabilities of the auto-regressive decoding retrieval paradigm, laying a foundation for its limitations and inspiring future advancements toward more robust and generalizable generative retrieval.
Unleashing the Potential of Diffusion Models Towards Diversified Sequential Recommendations
Authors: Zhuo Cai, Shoujin Wang, Victor W. Chu, Usman Naseem, Yang Wang, Fang Chen
Venue: SIGIR
Year: 2025

Sequential recommender systems (SRSs) aim to recommend the next items to well match users' preferences. In addition to recommendation accuracy, diversity is another critical aspect in evaluating SRSs. Recently, the emerging diffusion models (DMs) have been widely adopted in SRSs. Their employed learning-to-generate paradigm allows them to cover a much broader range of users' preferences and thus generate more diversified items. However, existing DM-based SRSs still face two significant gaps that prevent them from further improving the recommendation diversity: (1) they often rely on non-diversified users' preferences as guidance to direct the training of diffusion networks, restricting networks' ability to generate diverse items; and (2) they are based on a homogeneous diffusion inference mechanism to generate the next items and thus can only accommodate users' major preferences. Such a practice neglects users' heterogeneous preferences towards various types of items, further limiting recommendation diversity. To bridge these two critical gaps and to further unleash the potential of DMs in enhancing the recommendation diversity of SRSs, we propose a novel diversity-guided diffusion model for sequential recommendations, called DiffDiv for short. To be specific, first, a new diversity-aware guidance learning mechanism is devised to direct the training of DMs to effectively capture users' diversified preferences from their historical interactions. Then, a novel heterogeneous diffusion inference mechanism is designed to generate diversified items to accommodate users' heterogeneous preferences, further boosting the recommendation diversity. Extensive experiments on real-world datasets validate the effectiveness of DiffDiv in terms of both recommendation accuracy and diversity.
NLQxform-UI: An Interactive and Intuitive Scholarly Question Answering System
Authors: Ruijie Wang, Zhiruo Zhang, Luca Rossetto, Florian Ruosch, Abraham Bernstein
Venue: SIGIR
Year: 2025

Most scholarly search services only provide basic text-matching or similarity-based searches, with limited operations that require manual configuration, such as sorting and filtering by specific metadata attributes. These capabilities are insufficient for researchers who often have queries that involve complex constraints and operations, such as ''enumerating the authors of a given paper along with the venues where they have published other papers.'' In this work, we develop an interactive and intuitive scholarly question answering system called NLQxform-UI, which allows users to pose complex queries in the form of natural language questions. It is capable of automatically translating these questions into SPARQL queries that can be executed over the DBLP knowledge graph to retrieve expected answers. Furthermore, the users can interact with each step of the answering process and browse the final results in a web-based interface. A video recording of our system is available at https://youtu.be/elq8CPykiyk Additionally, the system has been completely open-sourced: https://github.com/ruijie-wang-uzh/NLQxform-UI
Assessing Effective Token Length of Multimodal Models for Text-to-Image Retrieval
Authors: Le Nguyen, Preet Jain, Krutik Panchal, Md Tanvirul Alam, Nidhi Rastogi
Venue: SIGIR
Year: 2025

Multimodal embedding models have been widely adopted in text-to-image retrieval, enabling direct comparison between text and image modalities. However, how well they handle long text is poorly understood. For instance, Long-CLIP found that OpenAI's CLIP model, despite having a 77-token input limit, maintains optimal performance for only 20 tokens- its effective token length. In this paper, we build on the Long-CLIP study, and extend the analysis to other widely used multimodal models and find their effective token length. Unlike Long-CLIP, we examine how domain-specific language influences changes in effective token length and explore its implications on different domains. Based on our findings, we create a comprehensive reference of various models' effective token length across different domains; offering deeper insights into the true limitations of multimodal models used in text-to-image retrieval. Finally, we introduce a systematic benchmark that determines the effective token length of any multimodal model using a given dataset. Our results show that the effective token length is consistently lower than the input token limit for all models, meaning that these models cannot utilize all the text that can be given to them. We also find that the effective token length varies by dataset, with domain-specific language influencing how much text a model can use before retrieval performance plateaus. Our code is available for reproducibility at https://github.com/aiforsec/EffectiveTokenLength-MModels
Generative Recommender with End-to-End Learnable Item Tokenization
Authors: Enze Liu, Bowen Zheng, Cheng Ling, Lantao Hu, Han Li, Wayne Xin Zhao
Venue: SIGIR
Year: 2025

Generative recommender systems have gained increasing attention as an innovative approach that directly generates item identifiers for recommendation tasks. Despite their potential, a major challenge is the effective construction of item identifiers that align well with recommender systems. Current approaches often treat item tokenization and generative recommendation training as separate processes, which can lead to suboptimal performance. To overcome this issue, we introduce ETEGRec, a novel End-To-End Generative Recommender that unifies item tokenization and generative recommendation into a cohesive framework. Built on a dual encoder-decoder architecture, ETEGRec consists of an item tokenizer and a generative recommender. To enable synergistic interaction between these components, we propose a recommendation-oriented alignment strategy, which includes two key optimization objectives: sequence-item alignment and preference-semantic alignment. These objectives tightly couple the learning processes of the item tokenizer and the generative recommender, fostering mutual enhancement. Additionally, we develop an alternating optimization technique to ensure stable and efficient end-to-end training of the entire framework. Extensive experiments demonstrate the superior performance of our approach compared to traditional sequential recommendation models and existing generative recommendation baselines. Our code is available at https://github.com/RUCAIBox/ETEGRec.
HTGformer: Heterogeneous Temporal Graph Transformer
Authors: Yili Wang
Venue: SIGIR
Year: 2025

In recent years, heterogeneous temporal graphs (HTGs) have attracted substantial attention in applications of information retrieval such as recommender systems and social networks. To enhance representation learning in HTGs, numerous tailored neural networks have recently been proposed. Despite these successes, existing methods adopt independent parameterization strategies to handle various data distributions in HTGs, leading to optimization challenges and speed bottlenecks. To bridge this gap, this paper proposes a novel transformer-based representation learning paradigm for HTGs called HTGformer. Specifically, assisted by two major modules, i.e., a graph embedding layer and a heterogeneous-temporal encoder, HTGformer can effectively and efficiently capture spatio-temporal heterogeneous information in HTGs for comprehensive node representations. Extensive experiments demonstrate that HTGformer achieves up to 6× speed-up compared to the state-of-the-art baseline while maintaining the best forecasting accuracy.
Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User
Authors: Xiaolei Wang, Chunxuan Xia, Junyi Li, Fanzhe Meng, Lei Huang, Jinpeng Wang, Wayne Xin Zhao, Ji-Rong Wen
Venue: SIGIR
Year: 2025

Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations. A fundamental challenge in CRSs lies in effectively understanding user preferences from conversations. Previous research primarily focuses on the issue of insufficient contextual information in conversations. They address this by introducing external knowledge sources, such as knowledge graphs, large language models (LLMs), and conversational recommendation corpora. Based on this, they design specific alignment strategies (e.g., prompt learning and instruction tuning) to integrate such knowledge for user preference understanding and item recommendation. However, user preferences can be multifaceted and complex, posing significant challenges for accurate recommendations even with access to abundant external knowledge. While interaction with users can clarify their true preferences, frequent user involvement may lead to a degraded user experience. To address this problem, we propose a Generative Reward model based Simulated User, named GRSU, for automatic interaction with CRSs. The simulated user provides feedback to the items recommended by CRSs, enabling them to better capture intricate user preferences through multi-turn interaction. Inspired by generative reward models, we design two types of feedback actions for the simulated user: i.e., generative item scoring, which offers coarse-grained feedback, and attribute-based item critiquing, which provides fine-grained feedback. To ensure seamless integration, these feedback actions are unified into an instruction-based format, allowing the development of a unified simulated user via instruction tuning on synthesized data. With this simulated user, automatic multi-turn interaction with CRSs can be effectively conducted. Furthermore, to strike a balance between effectiveness and efficiency, we draw inspiration from the paradigm of reward-guided search in complex reasoning tasks and employ beam search for the interaction process. On top of this, we propose an efficient candidate ranking method to improve the recommendation results derived from interaction. Extensive experiments on public datasets demonstrate the effectiveness, efficiency, and transferability of our approach.
LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation
Authors: Haitao Li, Yifan Chen, Yiran Hu, Qingyao Ai, Junjie Chen, Xiaoyu Yang, Jianhui Yang, Yueyue Wu, Zeyang Liu, Yiqun Liu
Venue: SIGIR
Year: 2025

Retrieval-augmented generation (RAG) has proven highly effective in improving large language models (LLMs) across various domains. However, there is no benchmark specifically designed to assess the effectiveness of RAG in the legal domain, which restricts progress in this area. To fill this gap, we propose LexRAG, the first benchmark to evaluate RAG systems for multi-turn legal consultations. LexRAG consists of 1,013 multi-turn dialogue samples and 17,228 candidate legal articles. Each sample is annotated by legal experts and consists of five rounds of progressive questioning. LexRAG includes two key tasks: (1) Conversational knowledge retrieval, requiring accurate retrieval of relevant legal articles based on multi-turn context. (2) Response generation, focusing on producing legally sound answers. To ensure reliable reproducibility, we develop LexiT, a legal RAG toolkit that provides a comprehensive implementation of RAG system components tailored for the legal domain. Additionally, we introduce an LLM-as-a-judge evaluation pipeline to enable detailed and effective assessment. Through experimental analysis of various LLMs and retrieval methods, we reveal the key limitations of existing RAG systems in handling legal consultation conversations. LexRAG establishes a new benchmark for the practical application of RAG systems in the legal domain, with its code and data available at https://github.com/CSHaitao/LexRAG.
Graph-Based Multimodal Contrastive Learning for Chart Question Answering
Authors: Yue Dai, Soyeon Caren Han, Wei Liu
Venue: SIGIR
Year: 2025

Chart question answering (ChartQA) is challenged by the heterogeneous composition of chart elements and the subtle data patterns they encode. This work introduces a novel joint multimodal scene graph framework that explicitly models the relationships among chart components and their underlying structures. The framework integrates both visual and textual graphs to capture structural and semantic characteristics, while a graph contrastive learning strategy aligns node representations across modalities-enabling their seamless incorporation into a transformer decoder as soft prompts. Moreover, a set of tailored Chain-of-Thought (CoT) prompts is proposed to enhance multimodal large language models (MLLMs) in zero-shot scenarios by mitigating hallucinations. Extensive evaluations on benchmarks including ChartQA, OpenCQA, and ChartX demonstrate significant performance improvements and validate the efficacy of the proposed approach.
Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation
Authors: Yuhao Wang, Ruiyang Ren, Yucheng Wang, Wayne Xin Zhao, Jing Liu, Hua Wu, Haifeng Wang
Venue: SIGIR
Year: 2025

Considering the inherent limitations of parametric knowledge in large language models (LLMs), retrieval-augmented generation (RAG) is widely employed to expand their knowledge scope. Since RAG has shown promise in knowledge-intensive tasks like open-domain question answering, its broader application to complex tasks and intelligent assistants has further advanced its utility. Despite this progress, the underlying knowledge utilization mechanisms of LLM-based RAG remain underexplored. In this paper, we present a systematic investigation of the intrinsic mechanisms by which LLMs integrate internal (parametric) and external (retrieved) knowledge in RAG scenarios. Specially, we employ knowledge stream analysis at the macroscopic level, and investigate the function of individual modules at the microscopic level. Drawing on knowledge streaming analyses, we decompose the knowledge utilization process into four distinct stages within LLM layers: knowledge refinement, knowledge elicitation, knowledge expression, and knowledge contestation. We further demonstrate that the relevance of passages guides the streaming of knowledge through these stages. At the module level, we introduce a new method, knowledge activation probability entropy (KAPE) for neuron identification associated with either internal or external knowledge. By selectively deactivating these neurons, we achieve targeted shifts in the LLM's reliance on one knowledge source over the other. Moreover, we discern complementary roles for multi-head attention and multi-layer perceptron layers during knowledge formation. These insights offer a foundation for improving interpretability and reliability in retrieval-augmented LLMs, paving the way for more robust and transparent generative solutions in knowledge-intensive domains.
AV-NAS: Audio-Visual Multi-Level Semantic Neural Architecture Search for Video Hashing
Authors: Yong Chen, Yuxiang Zhou, Hailiang Dong, Rui Liu, Zhouchen Lin, Dell Zhang
Venue: SIGIR
Year: 2025

Existing video hashing techniques for large-scale video retrieval often overlook inherent audio signals, which can potentially compromise retrieval performance. Incorporating both visual and audio signals, however, complicates neural architecture design, rendering the manual crafting of joint audio-visual neural network models challenging. To address this issue, we propose AV-NAS, a method that leverages data-driven Neural Architecture Search (NAS) within a tailored audio-visual network space to automatically discover the optimal video hashing network. Our approach offers: (1) a versatile multi-level semantic architecture based on audio-visual signals, defining a mixed search space encompassing diverse network modules such as MLP, CNN, Transformer, and Mamba, as well as operations like Add, Hadamard, SiLU, LayerNorm, and Skip; (2) a differentiable relaxation of the combinatorial search problem, converting it into a unified differentiable optimization problem which we tackle through our ''coarse search-pruning-finetuning'' strategy. Our experiments on large-scale video datasets show that AV-NAS can discover architectures distinct from expert designs and lead to substantial performance improvements over current state-of-the-art methods including the recently emerged AVHash.
Adaptive User Dynamic Interest Guidance for Generative Sequential Recommendation
Authors: Kai Zhu, Jing Li, Jia Wu, Yue He, Jun Chang, Guohao Li, Shuyi Zhang
Venue: SIGIR
Year: 2025

Recently, diffusion model-based methods have utilized user interest features as guidance conditions to achieve stable generation results in sequential recommendation tasks. However, these models struggle to capture users' dynamic interests, as the interests of different users are often inconsistent. Moreover, the fixed number of interests predefined by existing models cannot adapt to the diverse preferences of users, making it difficult to further improve recommendation performance. To address these issues, we propose a novel generative sequential recommendation framework named ADIGRec (Adaptive User Dynamic Interest Guidance for Generative Sequential Recommendation), which adaptively focuses on users' dynamic interest features. Specifically, our framework combines users' dynamic features and inherent interest features encoded from historical sequences as new guidance conditions. Furthermore, we introduce a module that injects dynamic interest features into the noise item embeddings, enabling explicit interaction with the guidance conditions during the generation phase. This approach essentially fits the noise in the target space rather than the user preference space, leading to improved recommendation diversity. Additionally, we propose a novel regularization method to mitigate the impact of user interest routing collapse on the generation results. Extensive experiments on three publicly available datasets demonstrate that our method achieves superior performance compared to established baseline methods.