OpenRaiser/Intern-Atlas huggingface dataset:
Two-Stage Adversarial Training for Deep Hashing via Representation Distillation
Authors: Fei Zhu, Huashan Chen, Wanqian Zhang, Lin Wang, Zheng Lin, Bo Li
Venue: SIGIR
Year: 2025

In recent years, the study on defending deep hashing models against adversarial attacks has garnered increasing attention. Among them, adversarial training is an effective method to train robust deep hashing models. Existing adversarial training methods for deep hashing simultaneously optimize original deep hashing loss and proposed adversarial training loss to train a robust model. However, we argue that directly using the original deep hashing loss will guide the model to learn excessive non-robust patterns from clean examples when extracting discriminative semantic information, thereby limiting model robustness. To tackle this, we propose a novel Clean model Representation Distillation based Adversarial Training (CRDAT) method, which enables the robust model to learn both discriminative semantic information and robust patterns by separating these two losses into two stages, i.e., standard training stage of a clean teacher model and adversarial training stage of a robust student model. Specifically, we propose a novel representation distillation based adversarial training loss, which distills the representations of the teacher model on clean examples at both the hash code level and feature level to guide the student model's learning on adversarial examples. Extensive experiments on multiple datasets and deep hashing methods demonstrate that our CRDAT method can greatly improve model robustness and achieve state-of-the-art defense performance.
FinIR: The 2nd Workshop on Financial Information Retrieval in the Era of Generative AI
Authors: Fengbin Zhu, Yunshan Ma, Fuli Feng, Chao Wang, Huanbo Luan, Guangnan Ye, Shuo Zhang, Dhagash Mehta, Pingping Chen, Bing Xiang, Tat-Seng Chua
Venue: SIGIR
Year: 2025

Recent advancements in Generative AI, such as Large Language Models (LLMs), have demonstrated remarkable success across various general tasks. Extensive studies have explored leveraging generative models in finance, but significant challenges persist. This half-day workshop explores potential approaches and research directions to address these challenges by equipping generative models with advanced Information Retrieval (IR) models. Specifically, this workshop seeks to provide a platform for discussing innovative ideas that facilitate the advancement of IR technology to enrich generative models in finance from four key perspectives: (i) financial IR techniques (ii) financial IR benchmarking and evaluation (iii) financial systems and agents/assistants (iv) and trustworthiness, privacy and security when applying financial IR and generative models. This workshop aims to deepen understanding, accelerate progress, and support the advancement of IR technology to enhance generative models to address financial challenges.
Brain Image Reconstruction with Retrieval-Augmented Diffusion
Authors: Shuqi Zhu, Ziyi Ye, Yi Zhong, Qingyao Ai, Yujia Zhou, Yiqun Liu
Venue: SIGIR
Year: 2025

Reconstructing visual images from brain signals is a rapidly evolving research with promising applications in brain-computer interfaces, cognitive neuroscience, and assistive technologies. While visual reconstruction based on functional Magnetic Resonance Imaging (fMRI) has previously achieved notable success, this paper explores cost-effective brain signals, i.e., electroencephalography (EEG) and magnetoencephalography (MEG). These signals are less precise than fMRI, which presents greater challenges for reconstruction. To address this problem, we propose BReAD (Brain Image Reconstruction with Retrieval-Augmented Diffusion), a novel framework that combines EEG/MEG signals with retrieval-augmented diffusion models to improve image reconstruction quality. BReAD utilizes the semantics decoded from brain signals for (1) retrieving semantic priors from a large-scale image database and (2) serving as a conditional constraint during the diffusion process. Extensive experiments demonstrate that BReAD significantly outperforms existing approaches in both qualitative and quantitative evaluations, paving the way for more robust and practical brain-to-image reconstruction systems. Our codes are available at https://github.com/Promise-Z5Q2SQ/BReAD.
Document Screenshot Retrievers are Vulnerable to Pixel Poisoning Attacks
Authors: Shengyao Zhuang, Ekaterina Khramtsova, Xueguang Ma, Bevan Koopman, Jimmy Lin, Guido Zuccon
Venue: SIGIR
Year: 2025

Recent advancements in dense retrieval have introduced vision-language model (VLM)-based retrievers, such as DSE and ColPali, which leverage document screenshots embedded as vectors to enable effective search and offer a simplified pipeline over traditional text-only methods. In this study, we propose three pixel poisoning attack methods designed to compromise VLM-based retrievers and evaluate their effectiveness under various attack settings and parameter configurations. Our empirical results demonstrate that injecting even a single adversarial screenshot into the retrieval corpus can significantly disrupt search results, poisoning the top-10 retrieved documents for 41.9% of queries in the case of DSE and 26.4% for ColPali. These vulnerability rates notably exceed those observed with equivalent attacks on text-only retrievers. Moreover, when targeting a small set of known queries, the attack success rate raises, achieving complete success in certain cases. By exposing the vulnerabilities inherent in vision-language models, this work highlights the potential risks associated with their deployment.
Towards Lossless Token Pruning in Late-Interaction Retrieval Models
Authors: Yuxuan Zong, Benjamin Piwowarski
Venue: SIGIR
Year: 2025

Late interaction neural IR models like ColBERT offer a competitive effectiveness-efficiency trade-off across many benchmarks. However, they require a huge memory space to store the contextual representation for all the document tokens. Some works have proposed using either heuristics or statistical-based techniques to prune tokens from each document. This however doesn't guarantee that the removed tokens have no impact on the retrieval score. Our work uses a principled approach to define how to prune tokens without impacting the score between a document and a query. We introduce three regularization losses, that induce a solution with high pruning ratios, as well as two pruning strategies. We study them experimentally (in and out-domain), showing that we can preserve ColBERT's performance while using only 30% of the tokens.
PSCon: Product Search Through Conversations
Authors: Jie Zou, Mohammad Aliannejadi, Evangelos Kanoulas, Shuxi Han, Heli Ma, Zheng Wang, Yang Yang, Heng Tao Shen
Venue: SIGIR
Year: 2025

Conversational Product Search ( CPS ) systems interact with users via natural language to offer personalized and context-aware product lists. However, most existing research on CPS is limited to simulated conversations, due to the lack of a real CPS dataset driven by human-like language. Moreover, existing conversational datasets for e-commerce are constructed for a particular market or a particular language and thus can not support cross-market and multi-lingual usage. In this paper, we propose a CPS data collection protocol and create a new CPS dataset, called PSCon, which assists product search through conversations with human-like language. The dataset is collected by a coached human-human data collection protocol and is available for dual markets and two languages. By formulating the task of CPS, the dataset allows for comprehensive and in-depth research on six subtasks: user intent detection, keyword extraction, system action prediction, question selection, item ranking, and response generation. Moreover, we present a concise analysis of the dataset and propose a benchmark model on the proposed CPS dataset. Our proposed dataset and model will be helpful for facilitating future research on CPS.
ReviewHQ: An API-Based System for Reviewer Assignment and Quality Control in Research Conferences
Authors: Guido Zuccon
Venue: SIGIR
Year: 2025

Managing the review process for large-scale academic conferences poses significant challenges in effectively matching papers to the right reviewers, detecting conflicts of interest, ensuring review quality, and addressing potential ethical issues such as dual submissions. In this demonstration paper, we introduce ReviewHQ, an API-based system designed to streamline conference management. For matching reviewers to publications, ReviewHQ constructs expertise profiles for potential reviewers by mining their publication records and representing both papers and reviewers as dense vectors. It then formulates paper assignment as a constrained optimization problem, leveraging dense vector similarity scores and other reviewer's and submission's features to produce high-quality assignments. Beyond its core functionality, ReviewHQ identifies conflicts of interest by analyzing co-authorship histories and flags suspected dual submissions by comparing manuscripts against submission information from other conferences. Further, the system employs methods to detect low-quality or automated (AI-generated) reviews and pinpoint discrepancies between reviewers' recommendations and final acceptance decisions. ReviewHQ has been used since 2024 across the SIGIR and SIGIR-AP conferences.
R2LLMs: Retrieval and Ranking with LLMs
Authors: Guido Zuccon, Shengyao Zhuang, Xueguang Ma
Venue: SIGIR
Year: 2025

Generative Large Language Models (LLMs) like GPT, Gemini, and Llama are transforming Information Retrieval, enabling new and more effective approaches to document retrieval and ranking. The switch from the previous generation pre-trained language models backbones (e.g., BERT, T5) to the new generative LLMs backbones has required the field to adapt training processes; it also has provided unprecedented capabilities and opportunities, stimulating research into zero-shot approaches, reasoning approaches, reinforcement learning based training, and multilingual and multimodal applications. This tutorial will provide a structured overview of LLM-based retrievers and rankers, covering fundamental architectures, training paradigms, real-world deployment considerations, and open challenges and research directions.
Preferential behavior in online groups
Authors: Lars Backstrom, Ravi Kumar, Cameron Marlow, Jasmine Novak, Andrew Tomkins
Venue: WSDM
Year: 2008

Online communities in the form of message boards, listservs, and newsgroups continue to represent a considerable amount of the social activity on the Internet. Every year thousands of groups ourish while others decline into relative obscurity; likewise, millions of members join a new community every year, some of whom will come to manage or moderate the conversation while others simply sit by the sidelines and observe. These processes of group formation, growth, and dissolution are central in social science, and in an online venue they have ramifications for the design and development of community software
On placing skips optimally in expectation
Authors: Flavio Chierichetti, Silvio Lattanzi, Federico Mari, Alessandro Panconesi
Venue: WSDM
Year: 2008

We study the problem of optimal skip placement in an inverted list. Assuming the query distribution to be known in advance, we formally prove that an optimal skip placement can be computed quite efficiently. Our best algorithm runs in time O (n log n), n being the length of the list.
Fast learning of document ranking functions with the committee perceptron
Authors: Jonathan L. Elsas, Vitor R. Carvalho, Jaime G. Carbonell
Venue: WSDM
Year: 2008

This paper presents a new variant of the perceptron algorithm using selective committee averaging (or voting). We apply this agorithm to the problem of learning ranking functions for document retrieval, known as the "Learning to Rank" problem. Most previous algorithms proposed to address this problem focus on minimizing the number of misranked document pairs in the training set. The committee perceptron algorithm improves upon existing solutions by biasing the final solution towards maximizing an arbitrary rank-based performance metrics. This method performs comparably or better than two state-of-the-art rank learning algorithms, and also provides significant training time improvements over those methods, showing over a 45-fold reduction in training time compared to ranking SVM
Web information management: past, present and future
Authors: Hector Garcia-Molina
Venue: WSDM
Year: 2008

In this talk I will give a brief retrospective on Web Information Management, and will discuss some of the key challenges for the future. I will not give a survey of all work in the area; instead I will give my personal perspective based on work in the InfoLab at Stanford. In particular, I will touch on our lab's work on crawling, indexing, ranking, personalization, and more recently on spam detection and social networking.
An empirical analysis of sponsored search performance in search engine advertising
Authors: Anindya Ghose, Sha Yang
Venue: WSDM
Year: 2008

The phenomenon of sponsored search advertising - where advertisers pay a fee to Internet search engines to be displayed alongside organic (non-sponsored) web search results - is gaining ground as the largest source of revenues for search engines. Despite the growth of search advertising, we have little understanding of how consumers respond to contextual and sponsored search advertising on the Internet. Using a unique panel dataset of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different metrics such as click-through rates, conversion rates, bid prices and keyword ranks. Our paper proposes a novel framework and data to better understand what drives these differences. We use a Hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo (MCMC) methods. We empirically estimate the impact of keyword attributes on consumer search and purchase behavior as well as on firms' decision-making behavior on bid prices and ranks. We find that the presence of retailer-specific information in the keyword increases click-through rates, and the presence of brand-specific information in the keyword increases conversion rates. We also demonstrate that as suggested by anecdotal evidence, search engines like Google factor in both the auction bid price as well as prior click-through rates before allotting a final rank to an advertisement. To the best of our knowledge, this is the first study that uses real world data from an advertiser and jointly estimates the effect of sponsored search advertising at a keyword level on consumer search, click and purchase behavior in electronic markets
Disorder inequality: a combinatorial approach to nearest neighbor search
Authors: Navin Goyal, Yury Lifshits, Hinrich Schütze
Venue: WSDM
Year: 2008

We say that an algorithm for nearest neighbor search is combinatorial if only direct comparisons between two pairwise similarity values are allowed. Combinatorial algorithms for nearest neighbor search have two important advantages: (1) they do not map similarity values to artificial distance values and do not use the triangle inequality for the latter, and (2) they work for arbitrarily complicated data representations and similarity functions.
Collaboration over time: characterizing and modeling network evolution
Authors: Jian Huang, Ziming Zhuang, Jia Li, C. Lee Giles
Venue: WSDM
Year: 2008

A formal type of scientific and academic collaboration is coauthorship which can be represented by a coauthorship network. Coauthorship networks are among some of the largest social networks and offer us the opportunity to study the mechanisms underlying large-scale real world networks. We construct such a network for the Computer Science field covering research collaborations from 1980 to 2005, based on a large dataset of 451,305 papers authored by 283,174 distinct researchers. By mining this network, we first present a comprehensive study of the network statistical properties for a longitudinal network at the overall network level as well as for the intermediate community level. Major observations are that the database community is the best connected while the AI community is the most assortative, and that the Computer Science field as a whole shows a collaboration pattern more similar to Mathematics than to Biology. Moreover, the small world phenomenon and the scale-free degree distribution accompany the growth of the network. To study the individual collaborations, we propose a novel stochastic model, Stochastic Poisson model with Optimization Tree (Spot)to efficiently predict any increment of collaboration based on the local neighborhood structure. Spot models the non-stationary Poisson process by maximizing the log-likelihood with a tree structure. Empirical results show that Spot outperforms Support Vector Regression by better fitting collaboration records and predicting the rate of collaboration
Ranking web sites with real user traffic
Authors: Mark R. Meiss, Filippo Menczer, Santo Fortunato, Alessandro Flammini, Alessandro Vespignani
Venue: WSDM
Year: 2008

We analyze the traffic-weighted Web host graph obtained from a large sample of real Web users over about seven months. A number of interesting structural properties are revealed by this complex dynamic network, some in line with the well-studied boolean link host graph and others pointing to important differences. We find that while search is directly involved in a surprisingly small fraction of user clicks, it leads to a much larger fraction of all sites visited. The temporal traffic patterns display strong regularities, with a large portion of future requests being statistically predictable by past ones. Given the importance of topological measures such as PageRank in modeling user navigation, as well as their role in ranking sites for Web search, we use the traffic data to validate the PageRank random surfing model. The ranking obtained by the actual frequency with which a site is visited by users differs significantly from that approximated by the uniform surfing/teleportation behavior modeled by PageRank, especially for the most important sites. To interpret this finding, we consider each of the fundamental assumptions underlying PageRank and show how each is violated by actual user behavior
Personal name classification in web queries
Authors: Dou Shen, Toby Walker, Zijian Zheng, Qiang Yang, Ying Li
Venue: WSDM
Year: 2008

Personal names are an important kind of Web queries in Web search, and yet they are special in many ways. Strategies for retrieving information on personal names should therefore be different from the strategies for other types of queries. To improve the search quality for personal names, a first step is to detect whether a query is a personal name. Despite the importance of this problem, relatively little previous research has been done on this topic. Since Web queries are usually short, conventional supervised machine-learning algorithms cannot be applied directly. An alternative is to apply some heuristic rules coupled with name-term dictionaries. However, when the dictionaries are small, this method tends to make false negatives; when the dictionaries are large, it tends to generate false positives. A more serious problem is that this method cannot provide a good tradeoff between precision and recall. To solve these problems, we propose an approach based on the construction of probabilistic name-term dictionaries and personal name grammars, and use this algorithm to predict the probability of a query to be a personal name. In this paper, we develop four different methods for building probabilistic name-term dictionaries in which a term is assigned with a probability value of the term being a name term. We compared our approach with baseline algorithms such as dictionary-based look-up methods and supervised classification algorithms including logistic regression and SVM on some manually labeled test sets. The results validate the effectiveness of our approach, whose F1 value is more than 79.8%, which outperforms the best baseline by more than 11.3%.
Deep classifier: automatically categorizing search results into large-scale hierarchies
Authors: Dikan Xing, Gui-Rong Xue, Qiang Yang, Yong Yu
Venue: WSDM
Year: 2008

Organizing Web search results into hierarchical categories facilitates users' browsing through Web search results, especially for ambiguous queries where the potential results are mixed together. Previous methods on search result classification are usually based on pre-training a classification model on some fixed and shallow hierarchical categories, where only the top-two-level categories of a Web taxonomy is used. Such classification methods may be too coarse for users to browse, since most search results would be classified into only two or three shallow categories. Instead, a deep hierarchical classifier must provide many more categories. However, the performance of such classifiers is usually limited because their classification effectiveness can deteriorate rapidly at the third or fourth level of a hierarchy. In this paper, we propose a novel algorithm known as Deep Classifier to classify the search results into detailed hierarchical categories with higher effectiveness than previous approaches. Given the search results in response to a query, the algorithm first prunes a wide-ranged hierarchy into a narrow one with the help of some Web directories. Different strategies are proposed to select the training data by utilizing the hierarchical structures. Finally, a discriminative naíve Bayesian classifier is developed to perform efficient and effective classification. As a result, the algorithm can provide more meaningful and specific class labels for search result browsing than shallow style of classification. We conduct experiments to show that the Deep Classifier can achieve significant improvement over state-of-the-art algorithms. In addition, with sufficient off-line preparation, the efficiency of the proposed algorithm is suitable for online application
Diversifying search results
Authors: Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, Samuel Ieong
Venue: WSDM
Year: 2009

We study the problem of answering ambiguous web queries in a setting where there exists a taxonomy of information, and that both queries and documents may belong to more than one category according to this taxonomy. We present a systematic approach to diversifying results that aims to minimize the risk of dissatisfaction of the average user. We propose an algorithm that well approximates this objective in general, and is provably optimal for a natural special case. Furthermore, we generalize several classical IR metrics, including NDCG, MRR, and MAP, to explicitly account for the value of diversification. We demonstrate empirically that our algorithm scores higher in these generalized metrics compared to results produced by commercial search engines.