OpenRaiser/Intern-Atlas huggingface dataset:
Collaborative Crawling: Mining User Experiences for Topical Resource Discovery
Authors: Charu C. Aggarwal
Venue: Conference KDD 2002
Year: 2002

The rapid growth of the world wide web had made the problem of topic specific resource discovery an important one in recent years. In this problem, it is desired to find web pages which satisfy a predicate specified by the user. Such a predicate could be a keyword query, a topical query, or some arbitrary contraint. Several techniques such as focussed crawling and intelligent crawling have recently been proposed for topic specific resource discovery. All these crawlers are linkage based, since they use the hyperlink behavior in order to perform resource discovery. Recent studies have shown that the topical correlations in hyperlinks are quite noisy and may not always show the consistency necessary for a reliable resource discovery process. In this paper, we will approach the problem of resource discovery from an entirely different perspective; we will mine the significant browsing patterns of world wide web users in order to model the likelihood of web pages belonging to a specified predicate. This user behavior can be mined from the freely available traces of large public domain proxies on the world wide web. We refer to this technique as collaborative crawling because it mines the collective user experiences in order to find topical resources. Such a strategy is extremely effective because the topical consistency in world wide web browsing patterns turns out to very reliable. In addition, the user-centered crawling system can be combined with linkage based systems to create an overall system which works more effectively than a system based purely on either user behavior or hyperlinks.
Wondering why data are missing from query results?: Ask conseil why-not
Authors: Melanie Herschel
Venue: Conference CIKM 2013
Year: 2013

In analyzing and debugging data transformations, or more specifically relational queries, a subproblem is to understand why some data are not part of the query result. This problem has recently been addressed from different perspectives for various fragments of relational queries. The different perspectives yield different, yet complementary explanations of such missing-answers.
Querying minimal steiner maximum-connected subgraphs in large graphs
Authors: Jiafeng Hu, Xiaowei Wu, Reynold Cheng, Siqiang Luo, Yixiang Fang
Venue: Conference CIKM 2016
Year: 2016

Given a graph G and a set Q of query nodes, we examine the Steiner Maximum-Connected Subgraph (SMCS). The SMCS, or G's induced subgraph that contains Q with the largest connectivity, can be useful for customer prediction, product promotion, and team assembling. Despite its importance, the SMCS problem has only been recently studied. Existing solutions evaluate the maximum SMCS, whose number of nodes is the largest among all the SMCSs of Q. However, the maximum SMCS, which may contain a lot of nodes, can be difficult to interpret. In this paper, we investigate the minimal SMCS, which is the minimal subgraph of G with the maximum connectivity containing Q. The minimal SMCS contains much fewer nodes than its maximum counterpart, and is thus easier to be understood. However, the minimal SMCS can be costly to evaluate. We thus propose efficient Expand-Refine algorithms, as well as their approximate versions with accuracy guarantees. Extensive experiments on six large real graph datasets validate the effectiveness and efficiency of our approaches.
Ettu: Analyzing query intents in corporate databases
Authors: Gökhan Kul, Duc Luong, Ting Xie, Patrick Coonan, Varun Chandola, Oliver Kennedy, Shambhu J. Upadhyaya
Venue: Conference WWW 2016
Year: 2016

Insider threats to databases in the financial sector have become a very serious and pervasive security problem. This paper proposes a framework to analyze access patterns to databases by clustering SQL queries issued to the database. Our system Ettu works by grouping queries with other similarly structured queries. The small number of intent groups that result can then be efficiently labeled by human operators. We show how our system is designed and how the components of the system work. Our preliminary results show that our system accurately models user intent.
Debiasing learning based cross-domain recommendation
Authors: Siqing Li, Liuyi Yao, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Tonglei Guo, Bolin Ding, Ji-Rong Wen
Venue: Conference KDD 2021
Year: 2021

As it becomes prevalent that user information exists in multiple platforms or services, cross-domain recommendation has been an important task in industry. Although it is well known that users tend to show different preferences in different domains, existing studies seldom model how domain biases affect user preferences. Focused on this issue, we develop a casual-based approach to mitigating the domain biases when transferring the user information cross domains. To be specific, this paper presents a novel debiasing learning based cross-domain recommendation framework with causal embedding. In this framework, we design a novel Inverse-Propensity-Score (IPS) estimator designed for cross-domain scenario, and further propose three kinds of restrictions for propensity score learning. Our framework can be generally applied to various recommendation algorithms for cross-domain recommendation. Extensive experiments on both public and industry datasets have demonstrated the effectiveness of the proposed framework.
Community trend prediction on heterogeneous graph in e-commerce
Authors: Jiahao Yuan, Zhao Li, Pengcheng Zou, Xuan Gao, Jinwei Pan, Wendi Ji, Xiaoling Wang
Venue: Conference WSDM 2022
Year: 2022

In online shopping, ever-changing fashion trends make merchants need to prepare more differentiated products to meet the diversified demands, and e-commerce platforms need to capture the market trend with a prophetic vision. For the trend prediction, the attribute tags, as the essential description of items, can genuinely reflect the decision basis of consumers. However, few existing works explore the attribute trend in the specific community for e-commerce. In this paper, we focus on the community trend prediction on the item attribute and propose a unified framework that combines the dynamic evolution of two graph patterns to predict the attribute trend in a specific community. Specifically, we first design a community-attribute bipartite graph at each time step to learn the collaboration of different communities. Next, we transform the bipartite graph into a hypergraph to exploit the associations of different attribute tags in one community. Lastly, we introduce a dynamic evolution component based on the recurrent neural networks to capture the fashion trend of attribute tags. Extensive experiments on three real-world datasets in a large e-commerce platform show the superiority of the proposed approach over several strong alternatives and demonstrate the ability to discover the community trend in advance.
4sdrug: Symptom-based set-to-set small and safe drug recommendation
Authors: Yanchao Tan, Chengjun Kong, Leisheng Yu, Pan Li 0005, Chaochao Chen 0001, Xiaolin Zheng, Vicki Hertzberg, Carl Yang 0001
Venue: Conference KDD 2022
Year: 2022

Drug recommendation is an important task of AI for healthcare. To recommend proper drugs, existing methods rely on various clinical records (e.g., diagnosis and procedures), which are commonly found in data such as electronic health records (EHRs). However, detailed records as such are often not available and the inputs might merely include a set of symptoms provided by doctors. Moreover, existing drug recommender systems usually treat drugs as individual items, ignoring the unique requirements that drug recommendation has to be done on a set of items (drugs), which should be as small as possible and safe without harmful drug-drug interactions (DDIs).
Mining heterogeneous information networks: The next frontier
Authors: Jiawei Han
Venue: Conference KDD 2012
Year: 2012

Real world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. For example, in a medical care network, objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits, diagnosis, and treatments are intertwined together, providing rich information and forming heterogeneous information networks. Effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge.
DEKR: Description enhanced knowledge graph for machine learning method recommendation
Authors: Xianshuai Cao, Yuliang Shi, Han Yu 0001, Jihu Wang, Xinjun Wang 0003, Zhongmin Yan, Zhiyong Chen
Venue: Conference SIGIR 2021
Year: 2021

The huge number of machine learning (ML) methods has resulted in significant information overload. Faced with an overwhelming number of ML methods, it is challenging to select appropriate ones for the given dataset and task. In general, the names of ML methods or datasets are rather condensed, thus lacking specific explanations, while the rich latent relationships between ML entities are not fully explored. In this paper, we propose a description-enhanced machine learning knowledge graph-based approach - DEKR - to help recommend appropriate ML methods for given ML datasets. The proposed knowledge graph (KG) not only includes the connections between entities but also contains the descriptions of the dataset and method entities. DEKR fuses the structural information with the description information of entities in the knowledge graph. It is a deep hybrid recommendation framework, which incorporates the knowledge graph-based and text-based methods, overcoming the limitations of previous knowledge graph-based recommendation systems that ignore the description information. There are two key components of DEKR: 1) a graph neural network aggregating information from multi-order neighbors with attention to enrich the seed (i.e. dataset or method) node's own representation, and 2) a deep collaborative filtering network based on the description text to obtain the linear and nonlinear interactions of description features. Through extensive experiments, we demonstrated the efficiency of DEKR, which outperforms the current state-of-the-art baselines by a large margin.
Streaming session-based recommendation
Authors: Lei Guo, Hongzhi Yin, Qinyong Wang, Tong Chen, Alexander Zhou, Nguyen Quoc Viet Hung
Venue: Conference KDD 2019
Year: 2019

Session-based Recommendation (SR) is the task of recommending the next item based on previously recorded user interactions. In this work, we study SR in a practical streaming scenario, namely Streaming Session-based Recommendation (SSR), which is a more challenging task due to (1) the uncertainty of user behaviors, and (2) the continuous, large-volume, high-velocity nature of the session data. Recent studies address (1) by exploiting the attention mechanism in Recurrent Neural Network (RNN) to better model the user's current intent, which leads to promising improvements. However, the proposed attention models are based solely on the current session. Moreover, existing studies only perform SR under static offline settings and none of them explore (2). In this work, we target SSR and propose a Streaming Session-based Recommendation Machine (SSRM) to tackle these two challenges. Specifically, to better understand the uncertainty of user behaviors, we propose a Matrix Factorization (MF) based attention model, which improves the commonly used attention mechanism by leveraging the user's historical interactions. To deal with the large-volume and high-velocity challenge, we introduce a reservoir-based streaming model where an active sampling strategy is proposed to improve the efficiency of model updating. We conduct extensive experiments on two real-world datasets. The experimental results demonstrate the superiority of the SSRM method compared to several state-of-the-art methods in terms of MRR and Recall.
CheatAgent: Attacking LLM-empowered recommender systems via LLM agent
Authors: Liang-Bo Ning 0001, Shijie Wang 0002, Wenqi Fan, Qing Li 0001, Xin Xu 0002, Hao Chen 0062, Feiran Huang
Venue: Conference KDD 2024
Year: 2024

Recently, Large Language Model (LLM)-empowered recommender systems (RecSys) have brought significant advances in personalized user experience and have attracted considerable attention. Despite the impressive progress, the research question regarding the safety vulnerability of LLM-empowered RecSys still remains largely under-investigated. Given the security and privacy concerns, it is more practical to focus on attacking the black-box RecSys, where attackers can only observe the system's inputs and outputs. However, traditional attack approaches employing reinforcement learning (RL) agents are not effective for attacking LLM-empowered RecSys due to the limited capabilities in processing complex textual inputs, planning, and reasoning. On the other hand, LLMs provide unprecedented opportunities to serve as attack agents to attack RecSys because of their impressive capability in simulating human-like decision-making processes. Therefore, in this paper, we propose a novel attack framework called CheatAgent by harnessing the human-like capabilities of LLMs, where an LLM-based agent is developed to attack LLM-Empowered RecSys. Specifically, our method first identifies the insertion position for maximum impact with minimal input modification. After that, the LLM agent is designed to generate adversarial perturbations to insert at target positions. To further improve the quality of generated perturbations, we utilize the prompt tuning technique to improve attacking strategies via feedback from the victim RecSys iteratively. Extensive experiments across three real-world datasets demonstrate the effectiveness of our proposed attacking method.
Graph anomaly detection with adaptive node mixup
Authors: Qinghai Zhou, Yuzhong Chen, Zhe Xu, Yuhang Wu, Menghai Pan, Mahashweta Das, Hao Yang, Hanghang Tong
Venue: Conference CIKM 2024
Year: 2024

Graph anomaly detection (GAD) aims to find network elements (e.g., nodes, edges) with significantly atypical patterns and has a profound impact in a variety of application domains, including social network analysis, security, Web, finance, and many more. Most of the existing methods have been developed in an unsupervised manner or with extremely limited supervision, due to the high cost of acquiring ground-truth information. Consequently, the identified anomalies may turn out to be noises or uneventful instances because of the lack of prior knowledge on graph anomalies. To address the data scarcity issue in GAD, in this paper, we propose, gADAM, a novel graph neural network-based GAD framework, which consolidates (1) an innovative mixup approach to augment the original training data by adaptively interpolating data instances in the embedding space, and (2) an efficacious sampling method to obtain high-quality negative samples for model training. Additionally, to advance the representation learning for GAD, we further equip the proposed framework with a generic prototype-based learning module. Through extensive empirical evaluations, we corroborate the superiority of the proposed gADAM framework on graph anomaly detection w.r.t. various metrics.
Temporal knowledge graph extraction and modeling across multiple documents for health risk prediction
Authors: Rochana Chaturvedi
Venue: Conference WWW 2024
Year: 2024

Clinical text in electronic health records (EHR) holds vital cues into a patient's journey, often absent in structured EHR data. Evidence-based healthcare decisions demand accurate extraction and modeling of these cues. The goal of our study is to predict Type-II Diabetes by utilizing concept-based models of visit sequences from longitudinal EHR data. We undertake the challenging task of fine-grained temporal information extraction from clinical text using a recent span-based approach with pre-trained transformers. We achieve a new state-of-the-art in end-to-end relation extraction from 2012 clinical temporal relations corpus. We propose to apply our model to a new dataset and extract patient-centric temporal knowledge graphs from their visits-fusing temporal orderings within documents and across visits. Beyond the current focus of our work on Type-II Diabetes risk prediction from EHR, our versatile framework can be extended to other domains including web-based healthcare systems for personalized medicine. It can not only model health outcomes having long progression timelines but also various socio-economic outcomes such as conflict, natural disasters, and financial markets by leveraging news, reports, and social-media text for extracting and modeling irregular time-series and help inform a variety of web-based applications and policies.
Fast Alignment Using Probabilistic Indexing
Authors: Clark F. Olson
Venue: Conference CVPR 1993
Year: 1993

The alignment method is a model-based object recognition technique that determines possible object transformations from three hypothesized matches of model and image points. For images and/or models with many features, the running time of the alignment method can be large. Methods of reducing the number of matches that must be examined are presented. The techniques described are the use of the probabilistic indexing system, and the elimination of groups of model points that produce large errors in the transformation determined by the alignment method. Results are presented which show that it is possible to achieve a speedup of over two orders of magnitude while still finding a correct alignment.<<ETX>>
Efficient Guaranteed Search for Gray-Level Patterns
Authors: William Rucklidge
Venue: Conference CVPR 1997
Year: 1997

We address the problem of locating a gray-level pattern in a gray-level image. The pattern can have been transformed by an affine transformation, and may have undergone some additional changes. We define a difference function based on comparing each pixel of the pattern with a window in the image, and search efficiently for transformations that minimize the difference function. The search is guaranteed: it will always find the transformation minimising the difference function, and not get fooled by a local minimum; it is also efficient, in that it does not need to examine every transformation in order to achieve this guarantee. This technique can be applied to object location, motion tracking, optical flow, or block-based motion compensation in video image sequence compression (e.g., MPEG).
Integrated Treatment of Matching and Measurement Errors for Robust Model-Based Motion Tracking
Authors: David G. Lowe
Venue: Conference ICCV 1990
Year: 1990

A system is described for real-time motion tracking of 3-D objects, including those with variable internal parameters. Unlike most previous work on model-based motion tracking, this system provides for the integrated treatment of matching and measurement errors during motion tracking. These two sources of error have very different distributions and must be handled by separate computational mechanisms. These errors can be treated in an integrated way by using the computation of variance in predicted feature measurements to greatly decrease the amount of search required for subsequent matching. In return, the search process is used to eliminate the need to treat outliers during the analysis of measurement errors. The resulting system can robustly track models with many degrees of freedom while running on relatively inexpensive hardware. These same techniques can be used to speed verification during model-based recognition, and they point the way to merging of systems for recognition and motion tracking.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
An Assessment of Information Criteria for Motion Model Selection
Authors: Philip H. S. Torr
Venue: Conference CVPR 1997
Year: 1997

Rigid motion imposes constraints on the motion of image points between the two images. The matched points must conform to one of several possible constraints, such as that given by the fundamental matrix or image-image homography, and it is essential to know which model to fit to the data before recovery of structure, matching or segmentation can be performed successfully This paper compares several model selection methods with a particular emphasis on providing a method that will workfully automatically on real imagery.
Image Spaces and Video Trajectories: Using Isomap to Explore Video Sequences
Authors: Robert Pless
Venue: Conference ICCV 2003
Year: 2003

Dimensionality reduction techniques seek to represent a set of images as a set of points in a low dimensional space. Here we explore a video representation that considers a video as two parts - a space of possible images and a trajectory through that space. The non-linear dimensionality reduction technique of Isomap, gives, for many interesting scenes, a very low dimensional representation of the space of possible images. Analysis of the shape of the video trajectory through these image spaces gives new tools for video analysis. Experiments with natural video sequences illustrate methods for the very different tasks of classifying video clips and temporal super-resolution.
Segmentation Using Multiscale Cues
Authors: Stella X. Yu
Venue: Conference CVPR 2004
Year: 2004

Edges at multiple scales provide complementary grouping cues for image segmentation. These cues are reliable within different ranges. The larger the scale of an edge, the longer range the grouping cues it designates, and the greater impact it has on the final segmentation. A good segmentation respects grouping cues at each scale. These intuitions are formulated in a graph-theoretic framework where multiscale edges define pairwise pixel affinity at multiple grids, each captured in one graph. A novel criterion called average cuts of normalized affinity is proposed to evaluate a simultaneous segmentation through all these graphs. Its near-global optima can be solved efficiently. With a sparse yet complete characterization of pairwise pixel affinity, this graph-cuts approach leads to a hierarchy of coarse to fine segmentations that naturally take care of textured regions and weak contours.
Optimal Eigenfeature Selection by Optimal Image Registration
Authors: Haim Schweitzer
Venue: Conference CVPR 1999
Year: 1999

Large collections of images cam be indexed by projections on a few "eigenfeatures" the dominant eigenvectors of the images covariance matrix. A preliminary step of registering the images is common practice. A quantitative analysis is being gained by registration was not performed in previous work, and heuristics were used to determine on what to register the images. We show that the registration improves the accuracy of indexing and optimal improvement is obtained when the images are registered on their eigenfeatures subspace. Similarly, if multiple images are to be registered on a subspace, the optimal subspace is the one spanned by the dominant eigenfeatures of the registered images. An algorithm that simultaneously registers the images and computes their eigenfeatures is proposed. The key idea is to iterate the following two steps: 1. Eigenfeatures are computed from the images. 2. New images are computed by registering the images on the subspace of these eigenfeatures. In the next iteration, Step 1 is applied to the set of images that were most recently computed in Step 2. It is demonstrated that the algorithm produces improved eigenfeatures and registers multiple images.