OpenRaiser/Intern-Atlas huggingface dataset:
Exact computation of influence spread by binary decision diagrams
Authors: Takanori Maehara, Hirofumi Suzuki, Masakazu Ishihata
Venue: Conference WWW 2017
Year: 2017

Evaluating influence spread in social networks is a fundamental procedure to estimate the word-of-mouth effect in viral marketing. There are enormous studies about this topic; however, under the standard stochastic cascade models, the exact computation of influence spread is known to be #P-hard. Thus, the existing studies have used Monte-Carlo simulation-based approximations to avoid exact computation. We propose the first algorithm to compute influence spread exactly under the independent cascade model. The algorithm first constructs binary decision diagrams (BDDs) for all possible realizations of influence spread, then computes influence spread by dynamic programming on the constructed BDDs. To construct the BDDs efficiently, we designed a new frontier-based search-type procedure. The constructed BDDs can also be used to solve other influence-spread related problems, such as random sampling without rejection, conditional influence spread evaluation, dynamic probability update, and gradient computation for probability optimization problems. We conducted computational experiments to evaluate the proposed algorithm. The algorithm successfully computed influence spread on real-world networks with a hundred edges in a reasonable time, which is quite impossible by the naive algorithm. We also conducted an experiment to evaluate the accuracy of the Monte-Carlo simulation-based approximation by comparing exact influence spread obtained by the proposed algorithm.
What is next when sequential prediction meets implicitly hard interaction?
Authors: Kaixi Hu, Lin Li, Qing Xie, Jianquan Liu, Xiaohui Tao
Venue: Conference CIKM 2021
Year: 2021

Hard interaction learning between source sequences and their next targets is challenging, which exists in a myriad of sequential prediction tasks. During the training process, most existing methods focus on explicitly hard interactions caused by wrong responses. However, a model might conduct correct responses by capturing a subset of learnable patterns, which results in implicitly hard interactions with some unlearned patterns. As such, its generalization performance is weakened. The problem gets more serious in sequential prediction due to the interference of substantial similar candidate targets. To this end, we propose a Hardness Aware Interaction Learning framework (HAIL) that mainly consists of two base sequential learning networks and mutual exclusivity distillation (MED). The base networks are initialized differently to learn distinctive view patterns, thus gaining different training experiences. The experiences in the form of the unlikelihood of correct responses are drawn from each other by MED, which provides mutual exclusivity knowledge to figure out implicitly hard interactions. Moreover, we deduce that the unlikelihood essentially introduces additional gradients to push the pattern learning of correct responses. Our framework can be easily extended to more peer base networks. Evaluation is conducted on four datasets covering cyber and physical spaces. The experimental results demonstrate that our framework outperforms several state-of-the-art methods in terms of top-k based metrics.
Workshop on Document Intelligence Understanding
Authors: Soyeon Caren Han, Yihao Ding, Siwen Luo, Josiah Poon, Hee-Guen Yoon, Zhe Huang, Paul Duuring, Eun-Jung Holden
Venue: Conference CIKM 2023
Year: 2023

Document understanding and information extraction include different tasks to understand a document and extract valuable information automatically. Recently, there has been a rising demand for developing document understanding among different domains, including business, law, and medicine, to boost the efficiency of work that is associated with a large number of documents. This workshop aims to bring together researchers and industry developers in the field of document intelligence and understanding diverse document types to boost automatic document processing and understanding techniques. We also released a data challenge on the recently introduced document-level VQA dataset, PDFVQA. The PDFVQA challenge examines the structural and contextual understandings of proposed models on the natural full document level of multiple consecutive document pages by including questions with a sequence of answers extracted from multi-pages of the full document. This task helps to boost the document understanding step from the single-page level to the full document level understanding.
Counterfactual graph augmentation for consumer unfairness mitigation in recommender systems
Authors: Ludovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, Giacomo Medda
Venue: Conference CIKM 2023
Year: 2023

In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider. However, prior works have mostly addressed them separately, for instance by explaining to consumers why a certain item was recommended or mitigating disparate impacts in recommendation utility. None of them has leveraged explainability techniques to inform unfairness mitigation. In this paper, we propose an approach that relies on counterfactual explanations to augment the set of user-item interactions, such that using them while inferring recommendations leads to fairer outcomes. Modeling user-item interactions as a bipartite graph, our approach augments the latter by identifying new user-item edges that not only can explain the original unfairness by design, but can also mitigate it. Experiments on two public data sets show that our approach effectively leads to a better trade-off between fairness and recommendation utility compared with state-of-the-art mitigation procedures. We further analyze the characteristics of added edges to highlight key unfairness patterns. Source code available at https://github.com/jackmedda/RS-BGExplainer/tree/cikm2023.
Modeling Domains as Distributions with Uncertainty for Cross-Domain Recommendation
Authors: Xianghui Zhu, Mengqun Jin, Hengyu Zhang, Chang Meng, Daoxin Zhang, Xiu Li
Venue: Conference SIGIR 2024
Year: 2024

In the field of dual-target Cross-Domain Recommendation (DTCDR), improving the performance in both the information sparse domain and rich domain has been a mainstream research trend. However, prior embedding-based methods are insufficient to adequately describe the dynamics of user actions and items across domains. Moreover, previous efforts frequently lacked a comprehensive investigation of the entire domain distributions. This paper proposes a novel framework entitled Wasserstein Cross-Domain Recommendation (WCDR) that captures uncertainty in Wasserstein space to address above challenges. In this framework, we abstract user/item actions as Elliptical Gaussian distributions and divide them into local-intrinsic and global-domain parts. To further model the domain diversity, we adopt shared-specific pattern for global-domain distributions and present Masked Domain-aware Sub-distribution Aggregation (MDSA) module to produce informative and diversified global-domain distributions, which incorporates attention-based aggregation method and masking strategy that alleviates negative transfer issues. Extensive experiments on two public datasets and one business dataset are conducted. Experimental results demonstrate the superiority of WCDR over state-of-the-art methods.
Smooth-AUC: Smoothing the Path Towards Rank-based CTR Prediction
Authors: Shuang Tang, Fangyuan Luo, Jun Wu
Venue: Conference SIGIR 2022
Year: 2022

Deep neural networks (DNNs) have been a key technique for click-through rate (CTR) estimation, yet existing DNNs-based CTR models neglect the inconsistency between their optimization objectives (e.g., Binary Cross Entropy, BCE) and CTR ranking metrics (e.g., Area Under the ROC Curve, AUC). It is noteworthy that directly optimizing AUC by gradient-descent methods is difficult due to the non-differentiable Heaviside function built-in AUC. To this end, we propose a smooth approximation of AUC, called smooth-AUC (SAUC), towards the rank-based CTR prediction. Specifically, SAUC relaxes the Heaviside function via sigmoid with a temperature coefficient (aiming at controlling the function sharpness) in order to facilitate the gradient-based optimization. Furthermore, SAUC is a plug-and-play objective that can be used in any DNNs-based CTR model. Experimental results on two real-world datasets demonstrate that SAUC consistently improves the recommendation accuracy of current DNNs-based CTR models.
Multimedia recommendation: technology and techniques
Authors: Jialie Shen, Meng Wang, Shuicheng Yan, Peng Cui
Venue: Conference SIGIR 2013
Year: 2013

In recent years, we have witnessed a rapid growth in the availability of digital multimedia on various application platforms and domains. Consequently, the problem of information overload has become more and more serious. In order to tackle the challenge, various multimedia recommendation technologies have been developed by different research communities (e.g., multimedia systems, information retrieval, machine learning and computer version). Meanwhile, many commercial web systems (e.g., Flick, YouTube, and Last.fm) have successfully applied recommendation techniques to provide users personalized content and services in a convenient and flexible way. When looking back, the information retrieval (IR) community has a long history of studying and contributing recommender system design and related issues. It has been proven that the recommender systems can effectively assist users in handling information overload and provide high-quality personalization. While several courses were dedicated to multimedia retrieval in the recent decade, to the best of our knowledge, the tutorial is the first one specifically focusing on multimedia recommender systems and their applications on various domains and media contents. We plan to summarize the research along this direction and provide an impetus for further research on this important topic
Friend or foe? mining suspicious behavior via graph capsule infomax detector against fraudsters
Authors: Xiangping Zheng, Bo Wu, Xun Liang, Wei Li
Venue: Conference WWW 2024
Year: 2024

Anomaly detection on graphs has recently attracted considerable attention due to its broad range of high-impact applications, including cybersecurity, financial transactions, and recommendation systems. Although many efforts have thus far been made, how to effectively handle the high inconsistency between users' behavior and labels, a fundamental issue in anomaly detection, has not yet received sufficient concern. Moreover, the inconsistency problem is hard to investigate and even deteriorates the performance of anomaly detectors. To this end, we propose a novel graph self-supervised learning framework, Capsule Graph Infomax (termed CapsGI), to overcome the inconsistency of anomaly detection. Inspired by the recent advances of capsules on images, we explore another possibility of reforming the node embedding by capsule ideas to represent the unique node's properties. Concretely, by disentangling heterogeneous factors underlying each node representation, we can establish node capsules such that their representation can reflect intrinsic node properties. To strengthen the connection among normal nodes, CapsGI further represents the part-whole contrastive learning between lower-level capsules (part) and higher-level capsules (whole) by explicitly considering the context graph relations. Extensive experiments on multiple real-world datasets demonstrate that our model significantly outperforms state-of-the-art models.
Online user representation learning across heterogeneous social networks
Authors: Weiqing Wang, Hongzhi Yin, Xingzhong Du, Wen Hua, Yongjun Li, Quoc Viet Hung Nguyen
Venue: Conference SIGIR 2019
Year: 2019

Accurate user representation learning has been proven fundamental for many social media applications, including community detection, recommendation, etc. A major challenge lies in that, the available data in a single social network are usually very limited and sparse. In real life, many people are members of several social networks in the same time. Constrained by the features and design of each, any single social platform offers only a partial view of a user from a particular perspective. In this paper, we propose MV-URL, a multi-view user representation learning model to enhance user modeling by integrating the knowledge from various networks. Different from the traditional network embedding frameworks where either the whole framework is single-network based or each network involved is a homogeneous network, we focus on multiple social networks and each network in our task is a heterogeneous network. It's very challenging to effectively fuse knowledge in this setting as the fusion depends upon not only the varying relatedness of information sources, but also the target application tasks. MV-URL focuses on two tasks: user account linkage (i.e., to predict the missing true user account linkage across social media) and user attribute prediction. Extensive evaluations have been conducted on two real-world collections of linked social networks, and the experimental results show the superiority of MV-URL compared with existing state-of-art embedding methods. It can be learned online, and is trivially parallelizable. These qualities make it suitable for real world applications.
Reducing the human overhead in text categorization
Authors: Arnd Christian König, Eric Brill
Venue: Conference KDD 2006
Year: 2006

Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from them (when using knowledge engineering). In this work, we describe a way to reduce this effort, while retaining the methods ’ accuracy, by constructing a hybrid classifier that utilizes human reasoning over automatically discovered text patterns to complement machine learning. Using a standard sentiment-classification dataset and real customer feedback data, we demonstrate that the resulting technique results in significant reduction of the human effort required to obtain a given classification accuracy. Moreover, the hybrid text classifier also results in a significant boost in accuracy over machine-learning based classifiers when a comparable amount of labeled data is used.
A procedure for unsupervised lexicon learning
Authors: Anand Venkataraman
Venue: Conference ICML 2001
Year: 2001

We describe an incremental unsupervised procedure to learn words from transcribed continuous speech. The algorithm is based on a conservative and traditional statistical model, and results of empirical tests show that it is competitive with other algorithms that have been proposed recently for this task.
CWS: a comparative web search system
Authors: Jian-Tao Sun, Xuanhui Wang, Dou Shen, Hua-Jun Zeng, Zheng Chen
Venue: Conference WWW 2006
Year: 2006

In this paper, we define and study a novel search problem: Comparative Web Search (CWS). The task of CWS is to seek relevant and comparative information from the Web to help users conduct comparisons among a set of topics. A system called CWS is developed to effectively facilitate Web users' comparison needs. Given a set of queries, which represent the topics that a user wants to compare, the system is characterized by: (1) automatic retrieval and ranking of Web pages by incorporating both their relevance to the queries and the comparative contents they contain; (2) automatic clustering of the comparative contents into semantically meaningful themes; (3) extraction of representative keyphrases to summarize the commonness and differences of the comparative contents in each theme. We developed a novel interface which supports two types of view modes: a pair-view which displays the result in the page level, and a cluster-view which organizes the comparative pages into the themes and displays the extracted phrases to facilitate users' comparison. Experiment results show the CWS system is effective and efficient.
Using english information in non-english web search
Authors: Wei Gao, John Blitzer, Ming Zhou
Venue: Conference CIKM 2008
Year: 2008

The leading web search engines have spent a decade building highly specialized ranking functions for English web pages. One of the reasons these ranking functions are effective is that they are designed around features such as PageRank, automatic query and domain taxonomies, and click-through information, etc. Unfortunately, many of these features are absent or altered in other languages. In this work, we show how to exploit these English features for a subset of Chinese queries which we call linguistically non-local (LNL). LNL Chinese queries have a minimally ambiguous English translation which also functions as a good English query. We first show how to identify pairs of Chinese LNL queries and their English counterparts from Chinese and English query logs. Then we show how to effectively exploit these pairs to improve Chinese relevance ranking. Our improved relevance ranker proceeds by (1) translating a query into English, (2) computing a cross-lingual relational graph between the Chinese and English documents, and (3) employing the relational ranking method of Qin et al. [15] to rank the Chinese documents. Our technique gives consistent improvements over a state-of-the-art Chinese mono-lingual ranker on web search data from the Microsoft Live China search engine.
Creating tag hierarchies for effective navigation in social media
Authors: K. Selçuk Candan, Luigi Di Caro, Maria Luisa Sapino
Venue: Conference CIKM 2008
Year: 2008

In social media, such as blogs, since the content naturally evolves over time, it is hard or in many cases impossible to organize the content for effective navigation. Thus, one commonly has to resort to simple tools, such as tags and tag clouds, for presenting frequently used keywords to users to provide them at least some high level idea about the content of a given set of social media entries. Most visualizations of tag clouds vary the sizes of the fonts to differentiate important tags from those that are less important. We propose an alternative "contextual-layout" method, TMine, for analyzing and presenting tags that are extracted from textual content. In TMine tags are first mapped onto a latent semantic space. Then, TMine analyzes the relationships between tags relying on an extended boolean interpretation of the semantic space. The tag cloud is condensed into a hierarchy in a way that captures contextual relationships between tags: in particular, descendant terms in the hierarchy occur within the context defined by the ancestor terms. This provides a mechanism for navigation within the tag space as well as for classification of the text documents based on the contextual structure implied by the tags.
Structured retrieval for question answering
Authors: Matthew W. Bilotti, Paul Ogilvie, Jamie Callan, Eric Nyberg
Venue: Conference SIGIR 2007
Year: 2007

Bag-of-words retrieval is popular among Question Answering (QA) system developers, but it does not support constraint checking and ranking on the linguistic and semantic information of interest to the QA system. We present anapproach to retrieval for QA, applying structured retrieval techniques to the types of text annotations that QA systems use. We demonstrate that the structured approach can retrieve more relevant results, more highly ranked, compared with bag-of-words, on a sentence retrieval task. We also characterize the extent to which structured retrieval effectiveness depends on the quality of the annotations.
Walk and learn: A two-stage approach for opinion words and opinion targets co-extraction
Authors: Liheng Xu, Kang Liu, Siwei Lai, Yubo Chen, Jun Zhao
Venue: Conference WWW 2013
Year: 2013

This paper proposes a novel two-stage method for opinion words and opinion targets co-extraction. In the first stage, a Sentiment Graph Walking algorithm is proposed, which naturally incorporates syntactic patterns in a graph to extract opinion word/target candidates. In the second stage, we adopt a self-Learning strategy to refine the results from the first stage, especially for filtering out noises with high frequency and capturing long-tail terms. Preliminary experimental evaluation shows that considering pattern confidence in the graph is beneficial and our approach achieves promising improvement over three competitive baselines.
Learning to combine representations for medical records search
Authors: Nut Limsopatham, Craig Macdonald, Iadh Ounis
Venue: Conference SIGIR 2013
Year: 2013

The complexity of medical terminology raises challenges when searching medical records. For example, 'cancer', 'tumour', and 'neoplasms', which are synonyms, may prevent a traditional search system from retrieving relevant records that contain only synonyms of the query terms. Prior works use bag-of-concepts approaches, to deal with this by representing medical terms sharing the same meanings using concepts from medical resources (e.g. MeSH). The relevance scores are then combined with a traditional bag-of-words representation, when inferring the relevance of medical records. Even though the existing approaches are effective, the predicted retrieval effectiveness of either the bag-of-words or bag-of-concepts representation, which may be used to effectively model the score combination and hence improve retrieval performance, is not taken into account. In this paper, we propose a novel learning framework that models the importance of the bag-of-words and the bag-of-concepts representations, combining their scores on a per-query basis. Our proposed framework leverages retrieval performance predictors, such as the clarity score and AvIDF, calculated on both representations as learning features. We evaluate our proposed framework using the TREC Medical Records track's test collections. As our proposed framework can significantly outperform an existing approach that linearly merges the relevance scores, we conclude that retrieval performance predictors can be effectively leveraged when combining the relevance scores.
The Role of Knowledge in Conceptual Retrieval: A Study in the Domain of Clinical Medicine
Authors: Jimmy Lin, Dina Demner-Fushman
Venue: Conference SIGIR 2006
Year: 2006

Despite its intuitive appeal, the hypothesis that retrieval at the level of "concepts" should outperform purely term-based approaches remains unverified empirically. In addition, the use of "knowledge" has not consistently resulted in performance gains. After identifying possible reasons for previous negative results, we present a novel framework for "conceptual retrieval" that articulates the types of knowledge that are important for information seeking. We instantiate this general framework in the domain of clinical medicine based on the principles of evidence-based medicine (EBM). Experiments show that an EBM-based scoring algorithm dramatically outperforms a state-of-the-art baseline that employs only term statistics. Ablation studies further yield a better understanding of the performance contributions of different components. Finally, we discuss how other domains can benefit from knowledge-based approaches.
Combining multi-level evidence for medical record retrieval
Authors: Dongqing Zhu, Ben Carterette
Venue: Conference CIKM 2012
Year: 2012

The increasing prevalence of electronic health records containing rich information about a patient's health and physical condition has the potential to transform research in health and medicine. In this work, we present a health record search system for finding patients matching certain inclusion criteria (specified as keyword queries) for clinical studies. In particular, our system aggregates multi-level evidence and combines proven statistical IR models, both in an innovative way, and achieves a 20% MAP (mean average precision) improvement over a strong baseline. Moreover, our cross-validation results show that the overall performance of our system is comparable to other top-performing systems on the same task.