OpenRaiser/Intern-Atlas huggingface dataset:
Session-aware information embedding for e-commerce product recommendation
Authors: Chen Wu, Ming Yan
Venue: Conference CIKM 2017
Year: 2017

Most of the existing recommender systems assume that user's visiting history can be constantly recorded. However, in recent online services, the user identification may be usually unknown and only limited online user behaviors can be used. It is of great importance to model the temporal online user behaviors and conduct recommendation for the anonymous users. In this paper, we propose a list-wise deep neural network based architecture to model the limited user behaviors within each session. To train the model efficiently, we first design a session embedding method to pre-train a session representation, which incorporates different kinds of user search behaviors such as clicks and views. Based on the learnt session representation, we further propose a list-wise ranking model to generate the recommendation result for each anonymous user session. We conduct quantitative experiments on a recently published dataset from an e-commerce company. The evaluation results validate the effectiveness of the proposed method, which can outperform the state-of-the-art significantly.
Fast dictionary-based compression for inverted indexes
Authors: Giulio Ermanno Pibiri, Matthias Petri, Alistair Moffat
Venue: Conference WSDM 2019
Year: 2019

Dictionary-based compression schemes provide fast decoding operation, typically at the expense of reduced compression effectiveness compared to statistical or probability-based approaches. In this work, we apply dictionary-based techniques to the compression of inverted lists, showing that the high degree of regularity that these integer sequences exhibit is a good match for certain types of dictionary methods, and that an important new trade-off balance between compression effectiveness and compression efficiency can be achieved. Our observations are supported by experiments using the document-level inverted index data for two large text collections, and a wide range of other index compression implementations as reference points. Those experiments demonstrate that the gap between efficiency and effectiveness can be substantially narrowed.
A structured approach to understanding recovery and relapse in AA
Authors: Yue Zhang, Arti Ramesh, Jennifer Golbeck, Dhanya Sridhar, Lise Getoor
Venue: Conference WWW 2018
Year: 2018

Alcoholism, also known as Alcohol Use Disorder (AUD), is a serious problem affecting millions of people worldwide. Recovery from AUD is known to be challenging and often leads to relapse at various points after enrolling in a rehabilitation program such as Alcoholics Anonymous (AA). In this work, we take a structured approach to understand recovery and relapse from AUD using social media data. To do so, we combine linguistic and psychological attributes of users with relational features that capture useful structure in the user interaction network. We evaluate our models on AA-attending users extracted from the Twitter social network and predict recovery at two different points---90 days and 1 year after the user joins AA, respectively. Our experiments reveal that our structured approach is helpful in predicting recovery in these users. We perform extensive quantitative analysis of different groups of features and dependencies among them. Our analysis sheds light on the role of each feature group and how they combine to predict recovery and relapse. Finally, we present a qualitative analysis of the different reasons behind users relapsing to AUD. Our models and analysis are helpful in making meaningful predictions in scenarios where only a subset of features are available and can potentially be helpful in identifying and preventing relapse early.
Physical simulation for probabilistic motion tracking
Authors: Marek Vondrak, Leonid Sigal, Odest Chadwicke Jenkins
Venue: Conference CVPR 2008
Year: 2008

Human motion tracking is an important problem in computer vision. Most prior approaches have concentrated on efficient inference algorithms and prior motion models; however, few can explicitly account for physical plausibility of recovered motion. The primary purpose of this work is to enforce physical plausibility in the tracking of a single articulated human subject. Towards this end, we propose a full-body 3D physical simulation-based prior that explicitly incorporates motion control and dynamics into the Bayesian filtering framework. We consider the humanpsilas motion to be generated by a ldquocontrol looprdquo. In this control loop, Newtonian physics approximates the rigid-body motion dynamics of the human and the environment through the application and integration of forces. Collisions generate interaction forces to prevent physically impossible hypotheses. This allows us to properly model human motion dynamics, ground contact and environment interactions. For efficient inference in the resulting high-dimensional state space, we introduce exemplar-based control strategy to reduce the effective search space. As a result we are able to recover the physically-plausible kinematic and dynamic state of the body from monocular and multi-view imagery. We show, both quantitatively and qualitatively, that our approach performs favorably with respect to standard Bayesian filtering methods.
SISP: a new framework for searching the informative subgraph based on PSO
Authors: Chen Chen, Guoren Wang, Huilin Liu, Junchang Xin, Ye Yuan
Venue: Conference CIKM 2011
Year: 2011

A significant number of applications on graph require the key relations among a group of query nodes. Given a relational graph such as social network or biochemical interaction, an informative subgraph is urgent, which can best explain the relationships among a group of given query nodes. Based on Particle Swarm Optimization (PSO), a new framework of SISP (Searching the Informative Subgraph based on PSO) is proposed. SISP contains three key stages. In the initialization stage, a random spreading method is proposed, which can effectively guarantee the connectivity of the nodes in each particle; In the calculating stage of fitness, a fitness function is designed by incorporating a sign function with the goodness score; In the update stage, the intersection-based particle extension method and rule-based particle compression method are proposed. To evaluate the qualities of returned subgraphs, the appropriate calculating of goodness score is studied. Considering the importance and relevance of a node together, we present the PNR method, which makes the definition of informativeness more reliable and the returned subgraph more satisfying. At last, we present experiments on a real dataset and a synthetic dataset separately. The experimental results confirm that the proposed methods achieve increased accuracy and are efficient for any query set.
MiCRon: making sense of news via relationship subgraphs
Authors: Zixian Huang, Shuxin Li, Gong Cheng, Evgeny Kharlamov, Yuzhong Qu
Venue: Conference CIKM 2019
Year: 2019

Knowledge graphs (KGs) have been extensively used to annotate text, e.g., news articles, in order to enhance its comprehension by readers. This requires to map entities occurring in the news to the target entities of the KG and to extract a so-called relationship sub-graph (RSG) that spans these entities. RSG extraction is computationally demanding and cannot scale to large KGs. Existing approximation algorithms that focus on structurally compact RSGs are not satisfactory since they often return no answers. We address this problem and develop an efficient algorithm to find approximations that connect the most salient subset of the target entities. Moreover, we propose a context-aware method to rank RSGs by their relevance to the news and their semantic cohesion. In the demo we will present our approach and the attendees will be able to experience how our system MiCRon helps to make sense of news article by computing and presenting RSGs relevant to these articles.
MING: mining informative entity relationship subgraphs
Authors: Gjergji Kasneci, Shady Elbassuoni, Gerhard Weikum
Venue: Conference CIKM 2009
Year: 2009

Many modern applications are faced with the task of knowledge discovery in entity-relationship graphs, such as domain-specific knowledge bases or social networks. Mining an "informative" subgraph that can explain the relations between k(>= 2) given entities of interest is a frequent knowledge discovery scenario on such graphs. We present MING, a principled method for extracting an informative subgraph for given query nodes. MING builds on a new notion of informativeness of nodes. This is used in a random-walk-with-restarts process to compute the informativeness of entire subgraphs.
Sequence and Time Aware Neighborhood for Session-Based Recommendations: STAN
Authors: Diksha Garg, Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff
Venue: Conference SIGIR 2019
Year: 2019

Recent advances in sequence-aware approaches for session-based recommendation, such as those based on recurrent neural networks, highlight the importance of leveraging sequential information from a session while making recommendations. Further, a session based k-nearest-neighbors approach (SKNN) has proven to be a strong baseline for session-based recommendations. However, SKNN does not take into account the readily available sequential and temporal information from sessions. In this work, we propose Sequence and Time Aware Neighborhood (STAN), with vanilla SKNN as its special case. STAN takes into account the following factors for making recommendations: i) position of an item in the current session, ii) recency of a past session w.r.t. to the current session, and iii) position of a recommendable item in a neighboring session. The importance of above factors for a specific application can be adjusted via controllable decay factors. Despite being simple, intuitive and easy to implement, empirical evaluation on three real-world datasets shows that STAN significantly improves over SKNN, and is even comparable to the recently proposed state-of-the-art deep learning approaches. Our results suggest that STAN can be considered as a strong baseline for evaluating session-based recommendation algorithms in future.
Unlocking the value of privacy: trading aggregate statistics over private correlated data
Authors: Chaoyue Niu, Zhenzhe Zheng, Fan Wu, Shaojie Tang, Xiaofeng Gao, Guihai Chen
Venue: Conference KDD 2018
Year: 2018

With the commoditization of personal privacy, pricing private data has become an intriguing problem. In this paper, we study noisy aggregate statistics trading from the perspective of a data broker in data markets. We thus propose ERATO, which enables aggrEgate statistics pRicing over privATe cOrrelated data. On one hand, ERATO guarantees arbitrage freeness against cunning data consumers. On the other hand, ERATO compensates data owners for their privacy losses using both bottom-up and top-down designs. We further apply ERATO to three practical aggregate statistics, namely weighted sum, probability distribution fitting, and degree distribution, and extensively evaluate their performances on MovieLens dataset, 2009 RECS dataset, and two SNAP large social network datasets, respectively. Our analysis and evaluation results reveal that ERATO well balances utility and privacy, achieves arbitrage freeness, and compensates data owners more fairly than differential privacy based approaches.
Servicegroup: A human-machine cooperation solution for group chat customer service
Authors: Minghui Yang, Hengbin Cui, Shaosheng Cao, Yafang Wang, Xiaolong Li 0005
Venue: Conference SIGIR 2020
Year: 2020

With the rapid growth of B2B (Business-to-Business), how to efficiently respond to various customer questions is becoming an important issue. In this scenario, customer questions always involve many aspects of the products, so there are usually multiple customer service agents to response respectively. To improve efficiency, we propose a human-machine cooperation solution called ServiceGroup, where relevant agents and customers are invited into the same group, and the system can provide a series of intelligent functions, including question notification, question recommendation and knowledge extraction. With the assistance of our developed ServiceGroup, the response rate within 15 minutes is improved twice. Until now, our ServiceGroup has already supported thousands of enterprises by means of millions of groups in instant messaging softwares.
Exploring the user-generated content (ugc) uploading behavior on youtube
Authors: Jaimie Yejean Park, Jiyeon Jang, Alejandro Jaimes, Chin-Wan Chung, Sung-Hyon Myaeng
Venue: Conference WWW 2014
Year: 2014

YouTube is the world's largest video sharing platform where both professional and non-professional users participate in creating, uploading, and viewing content. In this work, we analyze content in the music category created by the non-professionals, which we refer to as user-generated content (UGC). Non-professional users frequently upload content (UGC) that are parodies, remakes, or covers of the music videos uploaded by professionals, namely the official record labels. Along with the success of official music videos on YouTube, we find the increased participation of users in creating the UGC related to the music videos. In this study, we characterize the UGC uploading behavior in terms of what, where, and when. Furthermore, we measure the relationship between the popularity of the original content and creation of the related UGC. We find that the UGC uploading behavior is different depending on the types of the UGC and across different genres of music videos. We also find that UGC sharing is a highly global activity; popular UGC is created from all over the world despite the fact that the popular music videos originate from a very limited number of locations. Our findings imply that utilizing the information on re-created UGC is important in order to understand and to predict the popularity of the original content.
Parallel lasso screening for big data optimization
Authors: Qingyang Li, Shuang Qiu, Shuiwang Ji, Paul M. Thompson, Jieping Ye, Jie Wang
Venue: Conference KDD 2016
Year: 2016

Lasso regression is a widely used technique in data mining for model selection and feature extraction. In many applications, it remains challenging to apply the regression model to large-scale problems that have massive data samples with high-dimensional features. One popular and promising strategy is to solve the Lasso problem in parallel. Parallel solvers run multiple cores in parallel on a shared memory system to speedup the computation, while the practical usage is limited by the huge dimension in the feature space. Screening is a promising method to solve the problem of high dimensionality by discarding the inactive features and removing them from optimization. However, when integrating screening methods with parallel solvers, most of solvers cannot guarantee the convergence on the reduced feature matrix. In this paper, we propose a novel parallel framework by parallelizing screening methods and integrating it with our proposed parallel solver. We propose two parallel screening algorithms: Parallel Strong Rule (PSR) and Parallel Dual Polytope Projection (PDPP). For the parallel solver, we proposed an Asynchronous Grouped Coordinate Descent method (AGCD) to optimize the regression problem in parallel on the reduced feature matrix. AGCD is based on a grouped selection strategy to select the coordinate that has the maximum descent for the objective function in a group of candidates. Empirical studies on the real-world datasets demonstrate that the proposed parallel framework has a superior performance compared to the state-of-the-art parallel solvers.
PatentMiner: Topic-driven patent analysis and mining
Authors: Jie Tang, Bo Wang, Yang Yang, Po Hu, Yanting Zhao, Xinyu Yan, Bo Gao, Minlie Huang, Peng Xu, Weichang Li, Adam K. Usadi
Venue: Conference KDD 2012
Year: 2012

Patenting is one of the most important ways to protect company's core business concepts and proprietary technologies. Analyzing large volume of patent data can uncover the potential competitive or collaborative relations among companies in certain areas, which can provide valuable information to develop strategies for intellectual property (IP), R&D, and marketing. In this paper, we present a novel topic-driven patent analysis and mining system. Instead of merely searching over patent content, we focus on studying the heterogeneous patent network derived from the patent database, which is represented by several types of objects (companies, inventors, and technical content) jointly evolving over time. We design and implement a general topic-driven framework for analyzing and mining the heterogeneous patent network. Specifically, we propose a dynamic probabilistic model to characterize the topical evolution of these objects within the patent network. Based on this modeling framework, we derive several patent analytics tools that can be directly used for IP and R&D strategy planning, including a heterogeneous network co-ranking method, a topic-level competitor evolution analysis algorithm, and a method to summarize the search results. We evaluate the proposed methods on a real-world patent database. The experimental results show that the proposed techniques clearly outperform the corresponding baseline methods.
Model-based oversampling for imbalanced sequence classification
Authors: Zhichen Gong, Huanhuan Chen
Venue: Conference CIKM 2016
Year: 2016

Sequence classification is critical in the data mining communities. It becomes more challenging when the class distribution is imbalanced, which occurs in many real-world applications. Oversampling algorithms try to re-balance the skewed class by generating synthetic data for minority classes, but most of existing oversampling approaches could not consider the temporal structure of sequences, or handle multivariate and long sequences. To address these problems, this paper proposes a novel oversampling algorithm based on the 'generative' models of sequences. In particular, a recurrent neural network was employed to learn the generative mechanics for sequences as representations for the corresponding sequences. These generative models are then utilized to form a kernel to capture the similarity between different sequences. Finally, oversampling is performed in the kernel feature space to generate synthetic data. The proposed approach can handle highly imbalanced sequential data and is robust to noise. The competitiveness of the proposed approach is demonstrated by experiments on both synthetic data and benchmark data, including univariate and multivariate sequences.
Early prediction on imbalanced multivariate time series
Authors: Guoliang He, Yong Duan, Tieyun Qian, Xu Chen
Venue: Conference CIKM 2013
Year: 2013

Multivariate time series (MTS) classification is an important topic in time series data mining, and lots of efficient models and techniques have been introduced to cope with it. However, early classification on imbalanced MTS data largely remains an open problem. To deal with this issue, we adopt a multiple under-sampling and dynamical subspace generation method to obtain initial training data, and each training data is used to learn a base learner. Finally, an ensemble classifier is introduced for early classification on imbalanced MTS data. Experimental results show that our proposed methods can achieve effective early prediction on imbalanced MTS data.
Imbalanced time series classification for flight data analyzing with nonlinear granger causality learning
Authors: Hao Huang, Chenxiao Xu, Shinjae Yoo, Weizhong Yan, Tianyi Wang, Feng Xue
Venue: Conference CIKM 2020
Year: 2020

Identifying the faulty class of multivariate time series is crucial for today's flight data analysis. However, most of the existing time series classification methods suffer from imbalanced data and lack of model interpretability, especially on flight data of which faulty events are usually uncommon with a limited amount of data. Here, we present a neural network classification model for imbalanced multivariate time series by leveraging the information learned from normal class, which can also learn the nonlinear Granger causality for each class, so that we can pinpoint how time series classes differ from each other. Experiments on simulated data and real flight data shows that this model can achieve high accuracy of identifying anomalous flights.
Time series analysis of nursing notes for mortality prediction via a state transition topic model
Authors: Yohan Jo, Natasha Loghmanpour, Carolyn P. Rosé
Venue: Conference CIKM 2015
Year: 2015

Accurate mortality prediction is an important task in intensive care units in order to channel prompt care to patients in the most critical condition and to reduce nurses' alarm fatigue. Nursing notes carry valuable information in this regard, but nothing has been reported about the effectiveness of temporal analysis of nursing notes in mortality prediction tasks.
Johan: A joint online hurricane trajectory and intensity forecasting framework
Authors: Ding Wang, Pang-Ning Tan
Venue: Conference KDD 2021
Year: 2021

The researcher dealt in this study with administrative and technical situation together with job descriptor and duties of the educational supervisors in structures in the secondary stage of education both in the theoretical and scientific level. Subject of the study. The role of the educate and management in upgrading leaching process, from point of view of administration management, This study
Non-metric affinity propagation for unsupervised image categorization
Authors: Delbert Dueck, Brendan J. Frey
Venue: Conference ICCV 2007
Year: 2007

Unsupervised categorization of images or image parts is often needed for image and video summarization or as a preprocessing step in supervised methods for classification, tracking and segmentation. While many metric-based techniques have been applied to this problem in the vision community, often, the most natural measures of similarity (e.g., number of matching SIFT features) between pairs of images or image parts is non-metric. Unsupervised categorization by identifying a subset of representative exemplars can be efficiently performed with the recently-proposed 'affinity propagation' algorithm. In contrast to k-centers clustering, which iteratively refines an initial randomly-chosen set of exemplars, affinity propagation simultaneously considers all data points as potential exemplars and iteratively exchanges messages between data points until a good solution emerges. When applied to the Olivetti face data set using a translation-invariant non-metric similarity, affinity propagation achieves a much lower reconstruction error and nearly halves the classification error rate, compared to state-of-the-art techniques. For the more challenging problem of unsupervised categorization of images from the CaltechlOl data set, we derived non-metric similarities between pairs of images by matching SIFT features. Affinity propagation successfully identifies meaningful categories, which provide a natural summarization of the training images and can be used to classify new input images.