OpenRaiser/Intern-Atlas huggingface dataset:
Web Table Extraction, Retrieval and Augmentation
Authors: Shuo Zhang, Krisztian Balog
Venue: Conference SIGIR 2019
Year: 2019

This tutorial synthesizes and presents research on web tables over the past two decades. We group the tasks into six main categories of information access tasks: (i) table extraction, (ii) table interpretation, (iii) table search, (iv) question answering on tables, (v) knowledge base augmentation, and (vi) table completion. For each category, we identify and introduce seminal approaches, present relevant resources, and point out interdependencies among the different tasks.
Predicting pinterest: Automating a distributed human computation
Authors: Changtao Zhong, Dmytro Karamshuk, Nishanth Sastry
Venue: Conference WWW 2015
Year: 2015

Everyday, millions of users save content items for future use on sites like Pinterest, by ''pinning'' them onto carefully categorised personal pinboards, thereby creating personal taxonomies of the Web. This paper seeks to understand Pinterest as a distributed human computation that categorises images from around the Web. We show that despite being categorised onto personal pinboards by individual actions, there is a generally a global agreement in implicitly assigning images into a coarse-grained global taxonomy of 32 categories, and furthermore, users tend to specialise in a handful of categories. By exploiting these characteristics, and augmenting with image-related features drawn from a state-of-the-art deep convolutional neural network, we develop a cascade of predictors that together automate a large fraction of Pinterest actions. Our end-to-end model is able to both predict whether a user will repin an image onto her own pinboard, and also which pinboard she might choose, with an accuracy of 0.69 ([email protected] of 0.75).
Hegemony in Social Media and the effect of recommendations
Authors: Ana-Andreea Stoica, Augustin Chaintreau
Venue: Conference WWW 2019
Year: 2019

As today’s media landscape is carved by social media endorsements and built on automated recommendations, both of these are often criticized for inducing vicious dynamics, such as the filter bubble effect, echo chamber, or polarization. We introduce a new model featuring a mild version of homophily and two well-known popularity dynamics. These broadly reproduce the organic activity and the algorithmic filtering, respectively, of which the latter is now commonplace within social media or other online services. Surprisingly, we show this is all that is needed to create hegemony: a single viewpoint (or side) not only receives undue attention, but it also captures all the attention given to “top trending” items.
Am I a Real or Fake Celebrity? Evaluating Face Recognition and Verification APIs under Deepfake Impersonation Attack
Authors: Shahroz Tariq, Sowon Jeon, Simon S. Woo
Venue: Conference WWW 2022
Year: 2022

Recent advancements in web-based multimedia technologies, such as face recognition web services powered by deep learning, have been significant. As a result, companies such as Microsoft, Amazon, and Naver provide highly accurate commercial face recognition web services for a variety of multimedia applications. Naturally, such technologies face persistent threats, as virtually anyone with access to deepfakes can quickly launch impersonation attacks. These attacks pose a serious threat to authentication services, which rely heavily on the performance of their underlying face recognition technologies. Despite its gravity, deepfake abuse involving commercial web services and their robustness have not been thoroughly measured and investigated. By conducting a case study on celebrity face recognition, we examine the robustness of black-box commercial face recognition web APIs and open-source tools against Deepfake Impersonation (DI) attacks. While the majority of APIs do not make specific claims of deepfake robustness, we find that authentication mechanisms may get built one top of them, nonetheless. We demonstrate the vulnerability of face recognition technologies to DI attacks, achieving respective success rates of 78.0% for targeted (TA) attacks; we also propose mitigation strategies, lowering respective attack success rates to as low as 1.26% for TA attacks with adversarial training.
Ud-gnn: Uncertainty-aware debiased training on semi-homophilous graphs
Authors: Yang Liu 0200, Xiang Ao 0001, Fuli Feng, Qing He 0003
Venue: Conference KDD 2022
Year: 2022

Recent studies on Graph Neural Networks (GNNs) point out that most GNNs depend on the homophily assumption but fail to generalize to graphs with heterophily where dissimilar nodes connect. The concept of homophily or heterophily defined previously is a global measurement of the whole graph and cannot describe the local connectivity of a node. From the node-level perspective, we find that real-world graph structures exhibit a mixture of homophily and heterophily, which refers to the co-existence of both homophilous and heterophilous nodes. Under such a mixture, we reveal that GNNs are severely biased towards homophilous nodes, suffering a sharp performance drop on heterophilous nodes. To mitigate the bias issue, we explore an Uncertainty-aware Debiasing (UD) framework, which retains the knowledge of the biased model on certain nodes and compensates for the nodes with high uncertainty. In particular, UD estimates the uncertainty of the GNN output to recognize heterophilous nodes. UD then trains a debiased GNN by pruning the biased parameters with certain nodes and retraining the pruned parameters on nodes with high uncertainty. We apply UD on both homophilous GNNs (GCN and GAT) and heterophilous GNNs (Mixhop and GPR-GNN) and conduct extensive experiments on synthetic and benchmark datasets, where the debiased model consistently performs better and narrows the performance gap between homophilous and heterophilous nodes.
MCL: Mixed-Centric Loss for Collaborative Filtering
Authors: Zhaolin Gao, Zhaoyue Cheng, Felipe Pérez, Jianing Sun, Maksims Volkovs
Venue: Conference WWW 2022
Year: 2022

The majority of recent work in latent Collaborative Filtering (CF) has focused on developing new model architectures to learn accurate user and item representations. Typically, a standard pairwise loss function (BPR, Triplet, etc.) is used in these models, and little exploration is done on how to optimally extract signals from the available preference information. In the implicit setting, negative examples are sampled, and these losses allocate weights that solely depend on the difference in user distance between observed (positive) and negative item pairs. This can ignore valuable global information from other users and items, and lead to sub-optimal results. Motivated by this problem, we propose a novel loss which first leverages mining to select the most informative pairs, followed by a weighing process to allocate more weight to harder examples. Our weighting process consists of four different components, and incorporates distance information from other users, enabling the model to better position the learned representations. We conduct extensive experiments and demonstrate that our loss can be applied to different types of CF models leading to significant gains with each type. In particular, by applying our loss to the graph convolutional architecture, we achieve new state-of-the-art results on four different datasets. Further analysis shows that through our loss the model is able to learn better user-item representation space compared to other losses. Full code for this work is available here: https://github.com/layer6ai-labs/MCL.
MemStream: Memory-Based Streaming Anomaly Detection
Authors: Siddharth Bhatia, Arjit Jain, Shivin Srivastava, Kenji Kawaguchi, Bryan Hooi
Venue: Conference WWW 2022
Year: 2022

Given a stream of entries over time in a multi-dimensional data setting where concept drift is present, how can we detect anomalous activities? Most of the existing unsupervised anomaly detection approaches seek to detect anomalous events in an offline fashion and require a large amount of data for training. This is not practical in real-life scenarios where we receive the data in a streaming manner and do not know the size of the stream beforehand. Thus, we need a data-efficient method that can detect and adapt to changing data trends, or concept drift, in an online manner. In this work, we propose MemStream, a streaming anomaly detection framework, allowing us to detect unusual events as they occur while being resilient to concept drift. We leverage the power of a denoising autoencoder to learn representations and a memory module to learn the dynamically changing trend in data without the need for labels. We prove the optimum memory size required for effective drift handling. Furthermore, MemStream makes use of two architecture design choices to be robust to memory poisoning. Experimental results show the effectiveness of our approach compared to state-of-the-art streaming baselines using $2$ synthetic datasets and $11$ real-world datasets.
Improving Search Engines Using Human Computation Games
Authors: Hao Ma, Raman Chandrasekar, Chris Quirk, Abhishek Gupta
Venue: Conference CIKM 2009
Year: 2009

Work on evaluating and improving the relevance of web search engines typically use human relevance judgments or clickthrough data. Both these methods look at the problem of learning the mapping from queries to web pages. In this paper, we identify some issues with this approach, and suggest an alternative approach, namely, learning a mapping from web pages to queries. In particular, we use human computation games to elicit data about web pages from players that can be used to improve search. We describe three human computation games that we developed, with a focus on Page Hunt, a single-player game. We describe experiments we conducted with several hundred game players, highlight some interesting aspects of the data obtained and define the 'findability' metric. We also show how we automatically extract query alterations for use in query refinement using techniques from bitext matching. The data that we elicit from players has several other applications including providing metadata for pages and identifying ranking issues.
Learning effective representations for person-job fit by feature fusion
Authors: Junshu Jiang, Songyun Ye, Wei Wang, Jingran Xu, Xiaosheng Luo
Venue: Conference CIKM 2020
Year: 2020

Person-job fit is to match candidates and job posts on online recruitment platforms using machine learning algorithms. The effectiveness of matching algorithms heavily depends on the learned representations for the candidates and job posts. In this paper, we propose to learn comprehensive and effective representations of the candidates and job posts via feature fusion. First, in addition to applying deep learning models for processing the free text in resumes and job posts, which is adopted by existing methods, we extract semantic entities from the whole resume (and job post) and then learn features for them. By fusing the features from the free text and the entities, we get a comprehensive representation for the information explicitly stated in the resume and job post. Second, however, some information of a candidate or a job may not be explicitly captured in the resume or job post. Nonetheless, the historical applications including accepted and rejected cases can reveal some implicit intentions of the candidates or recruiters. Therefore, we propose to learn the representations of implicit intentions by processing the historical applications using LSTM. Last, by fusing the representations for the explicit and implicit intentions, we get a more comprehensive and effective representation for person-job fit. Experiments over 10 months real data show that our solution outperforms existing methods with a large margin. Ablation studies confirm the contribution of each component of the fused representation. The extracted semantic entities help interpret the matching results during the case study.
FactCheck Editor: Multilingual Text Editor with End-to-End fact-checking
Authors: Vinay Setty
Venue: Conference SIGIR 2024
Year: 2024

We introduce 'FactCheck Editor', an advanced text editor designed to automate fact-checking and correct factual inaccuracies. Given the widespread issue of misinformation, often a result of unintentional mistakes by content creators, our tool aims to address this challenge. It supports over 90 languages and utilizes transformer models to assist humans in the labor-intensive process of fact verification. This demonstration showcases a complete workflow that detects text claims in need of verification, generates relevant search engine queries, and retrieves appropriate documents from the web. It employs Natural Language Inference (NLI) to predict the veracity of claims and uses LLMs to summarize the evidence and suggest textual revisions to correct any errors in the text. Additionally, the effectiveness of models used in claim detection and veracity assessment is evaluated across multiple languages.
Surprising Efficacy of Fine-Tuned Transformers for Fact- Checking over Larger Language Models
Authors: Vinay Setty
Venue: Conference SIGIR 2024
Year: 2024

In this paper, we explore the challenges associated with establishing an end-to-end fact-checking pipeline in a real-world context, covering over 90 languages. Our real-world experimental benchmarks demonstrate that fine-tuning Transformer models specifically for fact-checking tasks, such as claim detection and veracity prediction, provide superior performance over large language models (LLMs) like GPT-4, GPT-3.5-Turbo, and Mistral-7b. However, we illustrate that LLMs excel in generative tasks such as question decomposition for evidence retrieval. Through extensive evaluation, we show the efficacy of fine-tuned models for fact-checking in a multilingual setting and complex claims that include numerical quantities.
Adversarial feature translation for multi-domain recommendation
Authors: Xiaobo Hao, Yudan Liu, Ruobing Xie, Kaikai Ge, Linyao Tang, Xu Zhang, Leyu Lin
Venue: Conference KDD 2021
Year: 2021

Real-world super platforms such as Google and WeChat usually have different recommendation scenarios to provide heterogeneous items for users' diverse demands. Multi-domain recommendation (MDR) is proposed to improve all recommendation domains simultaneously, where the key point is to capture informative domain-specific features from all domains. To address this problem, we propose a novel Adversarial feature translation (AFT) model for MDR, which learns the feature translations between different domains under a generative adversarial network framework. Precisely, in the multi-domain generator, we propose a domain-specific masked encoder to highlight inter-domain feature interactions, and then aggregate these features via a transformer and a domain-specific attention. In the multi-domain discriminator, we explicitly model the relationships between item, domain and users' general/domain-specific representations with a two-step feature translation inspired by the knowledge representation learning. In experiments, we evaluate AFT on a public and an industrial MDR datasets and achieve significant improvements. We also conduct an online evaluation on a real-world MDR system. We further give detailed ablation tests and model analyses to verify the effectiveness of different components. Currently, we have deployed AFT on WeChat Top Stories. The source code is in https://github.com/xiaobocser/AFT.
Fair-srs: a fair session-based recommendation system
Authors: Naime Ranjbar Kermany, Jian Yang, Jia Wu, Luiz Pizzato
Venue: Conference WSDM 2022
Year: 2022

This paper demonstrates Fair-SRS, a Fair Session-based Recommendation System that predicts user's next click based on their historical and current sessions. Fair-SRS provides personalized and diversified recommendations in two main steps: (1) forming user's session graph embeddings based on their long- and short-term interests, and (2) computing user's level of interest in diversity based on their recently-clicked items' similarity. In real-world scenarios, users tend to interact with more or fewer contents at different times, and providers expect to receive more exposure for their items. To achieve the objectives of both sides, the proposed Fair-SRS optimizes recommendations by making a trade-off between accuracy and personalized diversity.
Dimension relation modeling for click-through rate prediction
Authors: Zihao Zhao, Zhiwei Fang, Yong Li, Changping Peng, Yongjun Bao, Weipeng Yan
Venue: Conference CIKM 2020
Year: 2020

Embedding mechanism plays an important role in Click-Through-Rate (CTR) prediction. Essentially, it tries to learn a new feature space with some learned latent properties as the basis, and maps the high dimensional and categorical raw data to dense, rich and expressive representations, i.e., the embedding features. Current researches usually focus on learning the interactions through operations on the whole embedding features without considering the relations among the learned latent properties. In this paper, we find it has clear positive effects on CTR prediction to model such relations and propose a novel Dimension Relation Module (DRM) to capture them through dimension recalibration. We show that DRM can improve the performance of existing models consistently and the improvements are more obvious when the embedding dimension is higher. We further boost Field-wise and Element-wise embedding methods with our DRM and name this new model FED network. Extensive experiments demonstrate that FED is very powerful in CTR prediction task and achieves new state-of-the-art results on Criteo, Avazu and JD.com datasets.
MARIO: modality-aware attention and modality-preserving decoders for multimedia recommendation
Authors: Taeri Kim, Yeon-Chang Lee, Kijung Shin, Sang-Wook Kim
Venue: Conference CIKM 2022
Year: 2022

We address the multimedia recommendation problem, which utilizes items' multimodal features, such as visual and textual modalities, in addition to interaction information. While a number of existing multimedia recommender systems have been developed for this problem, we point out that none of these methods individually capture the influence of each modality at the interaction level. More importantly, we experimentally observe that the learning procedures of existing works fail to preserve the intrinsic modality-specific properties of items. To address above limitations, we propose an accurate multimedia recommendation framework, named MARIO, based on modality-aware attention and modality-preserving decoders. MARIO predicts users' preferences by considering the individual influence of each modality on each interaction while obtaining item embeddings that preserve the intrinsic modality-specific properties. The experiments on four real-life datasets demonstrate that MARIO consistently and significantly outperforms seven competitors in terms of the recommendation accuracy: MARIO yields up to 14.61% higher accuracy, compared to the best competitor.
Look Before You Leap: Confirming Edge Signs in Random Walk with Restart for Personalized Node Ranking in Signed Networks
Authors: Wonchang Lee, Yeon-Chang Lee, Dongwon Lee 0001, Sang-Wook Kim
Venue: Conference SIGIR 2021
Year: 2021

In this paper, we address the personalized node ranking (PNR) problem for signed networks, which aims to rank nodes in an order most relevant to a given seed node in a signed network. The recently-proposed PNR methods introduce the concept of the signed random surfer, denoted as SRSurfer, that performs the score propagation between nodes using the balance theory. However, in real settings of signed networks, edge relationships often do not strictly follow the rules of the balance theory. Therefore, SRSurfer-based PNR methods frequently perform incorrect score propagation to nodes, thereby degrading the accuracy of PNR. To address this limitation, we propose a novel random-walk based PNR approach with sign verification, named as OBOE (lOok Before yOu lEap). Specifically, OBOE carefully verifies the score propagation of SRSurfer by using the topological features of nodes. Then, OBOE corrects all incorrect score propagation cases by exploiting the statistics of a given network. The experiments on 3 real-world signed networks show that OBOE consistently and significantly outperforms 5 competing methods with improvement up to 13%, 95%, and 249% in top-k PNR, bottom-k PNR, and troll identification tasks, respectively. All OBOE codes and datasets are available at: http://github.com/wonchang24/OBOE.
Creating and evaluating multi-document sentence extract summaries
Authors: Jade Goldstein, Vibhu O. Mittal, Jaime G. Carbonell, James P. Callan
Venue: Conference CIKM 2000
Year: 2000

This paper discusses passage extraction approaches to multidocument summarization that use available information about the document set as a whole and the relationships between the documents to build on single document summarization methodology. Multi-document summarization di ers from single in that the issues of compression, speed, redundancy and passage selection are critical in the formation of useful summaries, as well as the user's goals in creating the summary. Our approach addresses these issues by using domain-independent techniques based mainly on fast, statistical processing, a metric for reducing redundancy and maximizing diversity in the selected passages, and a modular framework to allow easy parameterization for di erent genres, corpora characteristics and user requirements. We examined how humans create multi-document summaries as well as the characteristics of such summaries and use these summaries to evaluate the performance of various multidocument summarization algorithms.
Scoring missing terms in information retrieval tasks
Authors: Egidio Terra, Charles L. A. Clarke
Venue: Conference CIKM 2004
Year: 2004

An usual approach to address mismatching vocabulary problem is to augment the original query using dictionaries and other lexical resources and/or by looking at pseudo-relevant documents. Either way, terms are added to form a new query that will be used to score all documents in a subsequent retrieval pass, and as consequence the original query's focus may drift because of the newly added terms. We propose a new method to address the mismatching vocabulary problem, expanding original query terms only when necessary and complementing the user query for missing terms while scoring documents. It allows related semantic aspects to be included in a conservative and selective way, thus reducing the possibility of query drift. Our results using replacements for the <i>missing query terms</i> in modified document and passages retrieval methods show significant improvement over the original ones.
Automatic transliteration for Japanese-to-English text retrieval
Authors: Yan Qu, Gregory Grefenstette, David A. Evans
Venue: Conference SIGIR 2003
Year: 2003

For cross language information retrieval (CLIR) based on bilingual translation dictionaries, good performance depends upon lexical coverage in the dictionary. This is especially true for languages possessing few inter-language cognates, such as between Japanese and English. In this paper, we describe a method for automatically creating and validating candidate Japanese transliterated terms of English words. A phonetic English dictionary and a set of probabilistic mapping rules are used for automatically generating transliteration candidates. A monolingual Japanese corpus is then used for automatically validating the transliterated terms. We evaluate the usage of the extracted English-Japanese transliteration pairs with Japanese to English retrieval experiments over the CLEF bilingual test collections. The use of our automatically derived extension to a bilingual translation dictionary improves average precision, both before and after pseudo-relevance feedback, with gains ranging from 2.5% to 64.8%.