OpenRaiser/Intern-Atlas huggingface dataset:
MultiFS: Automated Multi- Scenario Feature Selection in Deep Recommender Systems
Authors: Dugang Liu, Chaohua Yang, Xing Tang, Yejing Wang, Fuyuan Lyu, Weihong Luo, Xiuqiang He, Zhong Ming, Xiangyu Zhao
Venue: Conference WSDM 2024
Year: 2024

Multi-scenario recommender systems (MSRSs) have been increasingly used in real-world industrial platforms for their excellent advantages in mitigating data sparsity and reducing maintenance costs. However, conventional MSRSs usually use all relevant features indiscriminately and ignore that different kinds of features have varying importance under different scenarios, which may cause confusion and performance degradation. In addition, existing feature selection methods for deep recommender systems may lack the exploration of scenario relations. In this paper, we propose a novel automated multi-scenario feature selection (MultiFS) framework to bridge this gap, which is able to consider scenario relations and utilize a hierarchical gating mechanism to select features for each scenario. Specifically, MultiFS first efficiently obtains feature importance across all the scenarios through a scenario-shared gate. Then, some scenario-specific gate aims to identify feature importance to individual scenarios from a subset of the former with lower importance. Subsequently, MultiFS imposes constraints on the two gates to make the learning mechanism more feasible and combines the two to select exclusive features for different scenarios. We evaluate MultiFS and demonstrate its ability to enhance the multi-scenario model performance through experiments over two public multi-scenario benchmarks.
Federated Adversarial Debiasing for Fair and Transferable Representations
Authors: Junyuan Hong, Zhuangdi Zhu, Shuyang Yu, Zhangyang Wang, Hiroko H. Dodge, Jiayu Zhou
Venue: Conference KDD 2021
Year: 2021

Federated learning is a distributed learning framework that is communication efficient and provides protection over participating users' raw training data. One outstanding challenge of federate learning comes from the users' heterogeneity, and learning from such data may yield biased and unfair models for minority groups. While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework. In this work, we study these barriers and address them by proposing a novel approach Federated Adversarial DEbiasing (FADE). FADE does not require users' sensitive group information for debiasing and offers users the freedom to opt-out from the adversarial component when privacy or computational costs become a concern. We show that ideally, FADE can attain the same global optimality as the one by the centralized algorithm. We then analyze when its convergence may fail in practice and propose a simple yet effective method to address the problem. Finally, we demonstrate the effectiveness of the proposed framework through extensive empirical studies, including the problem settings of unsupervised domain adaptation and fair learning. Our codes and pretrained models are available at: https://github.com/illidanlab/FADE.
The effect of Topic Set Size on Retrieval Experiment Error
Authors: Ellen M. Voorhees, Chris Buckley
Venue: Conference SIGIR 2002
Year: 2002

Retrieval mechanisms are frequently compared by computing the respective average scores for some effectiveness metric across a common set of information needs or topics, with researchers concluding one method is superior based on those averages. Since comparative retrieval system behavior is known to be highly variable across topics, good experimental design requires that a "sufficient" number of topics be used in the test. This paper uses TREC results to empirically derive error rates based on the number of topics used in a test and the observed difference in the average scores. The error rates quantify the likelihood that a different set of topics of the same size would lead to a different conclusion. We directly compute error rates for topic sets up to size 25, and extrapolate those rates for larger topic set sizes. The error rates found are larger than anticipated, indicating researchers need to take care when concluding one method is better than another, especially if few topics are used.
CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation
Authors: Chumeng Liang, Zherui Huang, Yicheng Liu, Zhanyu Liu, Guanjie Zheng, Hanyuan Shi, Kan Wu, Yuhao Du, Fuliang Li, Zhenhui Jessie Li
Venue: Conference KDD 2023
Year: 2023

Traffic simulation provides interactive data for the optimization of traffic control policies. However, existing traffic simulators are limited by their lack of scalability and shortage in input data, which prevents them from generating interactive data from traffic simulation in the scenarios of real large-scale city road networks.
Dataset condensation for time series classification via dual domain matching
Authors: Zhanyu Liu, Ke Hao, Guanjie Zheng, Yanwei Yu
Venue: Conference KDD 2024
Year: 2024

Time series data has been demonstrated to be crucial in various research fields. The management of large quantities of time series data presents challenges in terms of deep learning tasks, particularly for training a deep neural network. Recently, a technique named \textit{Dataset Condensation} has emerged as a solution to this problem. This technique generates a smaller synthetic dataset that has comparable performance to the full real dataset in downstream tasks such as classification. However, previous methods are primarily designed for image and graph datasets, and directly adapting them to the time series dataset leads to suboptimal performance due to their inability to effectively leverage the rich information inherent in time series data, particularly in the frequency domain. In this paper, we propose a novel framework named Dataset \textit{\textbf{Cond}}ensation for \textit{\textbf{T}}ime \textit{\textbf{S}}eries \textit{\textbf{C}}lassification via Dual Domain Matching (\textbf{CondTSC}) which focuses on the time series classification dataset condensation task. Different from previous methods, our proposed framework aims to generate a condensed dataset that matches the surrogate objectives in both the time and frequency domains. Specifically, CondTSC incorporates multi-view data augmentation, dual domain training, and dual surrogate objectives to enhance the dataset condensation process in the time and frequency domains. Through extensive experiments, we demonstrate the effectiveness of our proposed framework, which outperforms other baselines and learns a condensed synthetic dataset that exhibits desirable characteristics such as conforming to the distribution of the original data.
Kernel ridge regression-based graph dataset distillation
Authors: Zhe Xu 0007, Yuzhong Chen 0004, Menghai Pan, Huiyuan Chen, Mahashweta Das, Hao Yang 0007, Hanghang Tong
Venue: Conference KDD 2023
Year: 2023

The huge volume of emerging graph datasets has become a double-bladed sword for graph machine learning. On the one hand, it empowers the success of a myriad of graph neural networks (GNNs) with strong empirical performance. On the other hand, training modern graph neural networks on huge graph data is computationally expensive. How to distill the given graph dataset while retaining most of the trained models' performance is a challenging problem. Existing efforts try to approach this problem by solving meta-learning-based bilevel optimization objectives. A major hurdle lies in that the exact solutions of these methods are computationally intensive and thus, most, if not all, of them are solved by approximate strategies which in turn hurt the distillation performance. In this paper, inspired by the recent advances in neural network kernel methods, we adopt a kernel ridge regression-based meta-learning objective which has a feasible exact solution. However, the computation of graph neural tangent kernel is very expensive, especially in the context of dataset distillation. As a response, we design a graph kernel, named LiteGNTK, tailored for the dataset distillation problem which is closely related to the classic random walk graph kernel. An effective model named Kernel rıdge regression-based graph Dataset Distillation (KIDD) and its variants are proposed. KIDD shows nice efficiency in both the forward and backward propagation processes. At the same time, KIDD shows strong empirical performance over 7 real-world datasets compared with the state-of-the-art distillation methods. Thanks to the ability to find the exact solution of the distillation objective, the learned training graphs by KIDD can sometimes even outperform the original whole training set with as few as 1.65% training graphs.
Ad-load Balancing via Off-policy Learning in a Content Marketplace
Authors: Hitesh Sagtani, Madan Gopal Jhawar, Rishabh Mehrotra, Olivier Jeunen
Venue: Conference WSDM 2024
Year: 2024

Ad-load balancing is a critical challenge in online advertising systems, particularly in the context of social media platforms, where the goal is to maximize user engagement and revenue while maintaining a satisfactory user experience. This requires the optimization of conflicting objectives, such as user satisfaction and ads revenue. Traditional approaches to ad-load balancing rely on static allocation policies, which fail to adapt to changing user preferences and contextual factors. In this paper, we present an approach that leverages off-policy learning and evaluation from logged bandit feedback. We start by presenting a motivating analysis of the ad-load balancing problem, highlighting the conflicting objectives between user satisfaction and ads revenue. We emphasize the nuances that arise due to user heterogeneity and the dependence on the user's position within a session. Based on this analysis, we define the problem as determining the optimal ad-load for a particular feed fetch. To tackle this problem, we propose an off-policy learning framework that leverages unbiased estimators such as Inverse Propensity Scoring (IPS) and Doubly Robust (DR) to learn and estimate the policy values using offline collected stochastic data. We present insights from online A/B experiments deployed at scale across over 80 million users generating over 200 million sessions, where we find statistically significant improvements in both user satisfaction metrics and ads revenue for the platform.
Ads allocation in feed via constrained optimization
Authors: Jinyun Yan, Zhiyuan Xu, Birjodh Singh Tiwana, Shaunak Chatterjee
Venue: Conference KDD 2020
Year: 2020

Social networks and content publishing platforms have newsfeed applications, which show both organic content to drive engagement, and ads to drive revenue. This paper focuses on the problem of ads allocation in a newsfeed to achieve an optimal balance of revenue and engagement. To the best of our knowledge, we are the first to report practical solutions to this business-critical and popular problem in industry.
Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery
Authors: Wenchong He, Zhe Jiang 0001, Marcus Kriby, Yiqun Xie, Xiaowei Jia, Da Yan 0001, Yang Zhou 0001
Venue: Conference KDD 2022
Year: 2022

Given raster imagery features and imperfect vector training labels with registration uncertainty, this paper studies a deep learning framework that can quantify and reduce the registration uncertainty of training labels as well as train neural network parameters simultaneously. The problem is important in broad applications such as streamline classification on Earth imagery or tissue segmentation on medical imagery, whereby annotating precise vector labels is expensive and time-consuming. However, the problem is challenging due to the gap between the vector representation of class labels and the raster representation of image features and the need for training neural networks with uncertain label locations. Existing research on uncertain training labels often focuses on uncertainty in label class semantics or characterizes label registration uncertainty at the pixel level (not contiguous vectors). To fill the gap, this paper proposes a novel learning framework that explicitly quantifies vector labels' registration uncertainty. We propose a registration-uncertainty-aware loss function and design an iterative uncertainty reduction algorithm by re-estimating the posterior of true vector label locations distribution based on a Gaussian process. Evaluations on real-world datasets in National Hydrography Dataset refinement show that the proposed approach significantly outperforms several baselines in the registration uncertainty estimations performance and classification performance.
CurvaNet: Geometric Deep Learning based on Directional Curvature for 3D Shape Analysis
Authors: Wenchong He, Zhe Jiang 0001, Chengming Zhang 0006, Arpan Man Sainju
Venue: Conference KDD 2020
Year: 2020

Over the last decade, deep learning research has achieved tremendous success in computer vision and natural language processing. The current widely successful deep learning models are largely based on convolution and pooling operations on a Euclidean plane with a regular grid (e.g., image and video data) and thus cannot be directly applied to the non-Euclidean surface. Geometric deep learning aims to fill the gap by generalizing deep learning models from a 2D Euclidean plane to a 3D geometric surface. The problem has important applications in human-computer interaction, biochemistry, and mechanical engineering, but is uniquely challenging due to the lack of a regular grid framework and the difficulties in learning geometric features on a non-Euclidean manifold. Existing works focus on generalizing deep learning models from 2D image to graphs (e.g., graph neural networks) or 3D mesh surfaces but without fully learning geometric features from a differential geometry perspective. In contrast, this paper proposes a novel geometric deep learning model called CurvaNet that integrates differential geometry with graph neural networks. The key idea is to learn direction sensitive 3D shape features through directional curvature filters. We design a U-Net like architecture with downsampling and upsampling paths based on mesh pooling and unpooling operations. Evaluation on real-world datasets shows that the proposed model outperforms several baseline methods in classification accuracy.
Learning random walk models for inducing word dependency distributions
Authors: Kristina Toutanova, Christopher D. Manning, Andrew Y. Ng
Venue: Conference ICML 2004
Year: 2004

Many NLP tasks rely on accurately estimating word dependency probabilities P(ω1|ω2), where the words w1 and w2 have a particular relationship (such as verb-object). Because of the sparseness of counts of such dependencies, smoothing and the ability to use multiple sources of knowledge are important challenges. For example, if the probability P(N|V) of noun N being the subject of verb V is high, and V takes similar objects to V', and V' is synonymous to V", then we want to conclude that P(N|V") should also be reasonably high---even when those words did not cooccur in the training data.To capture these higher order relationships, we propose a Markov chain model, whose stationary distribution is used to give word probability estimates. Unlike the manually defined random walks used in some link analysis algorithms, we show how to automatically learn a rich set of parameters for the Markov chain's transition probabilities. We apply this model to the task of prepositional phrase attachment, obtaining an accuracy of 87.54%.
Retrieval and novelty detection at the sentence level
Authors: James Allan, Courtney Wade, Alvaro Bolivar
Venue: Conference SIGIR 2003
Year: 2003

Previous research in novelty detection has focused on the task of finding novel material, given a set or stream of documents on a certain topic. This study investigates the more difficult two-part task defined by the TREC 2002 novelty track: given a topic and a group of documents relevant to that topic, 1) find the relevant sentences from the documents, and 2) find the novel sentences from the collection of relevant sentences. Our research shows that the former step appears to be the more difficult part of this task, and that the performance of novelty measures is very sensitive to the presence of non-relevant sentences.
Topical locality in the web
Authors: Brian D. Davison
Venue: Conference SIGIR 2000
Year: 2000

Most web pages are linked to others with related content. This idea, combined with another that says that text in, and possibly around, HTML anchors describe the pages to which they point, is the foundation for a usable World-Wide Web. In this paper, we examine to what extent these ideas hold by empirically testing whether topical locality mirrors spatial locality of pages on the Web. In particular, we find that the likelihood of linked pages having similar textual content to be high; the similarity of sibling pages increases when the links from the parent are close together; titles, descriptions, and anchor text represent at least part of the target page; and that anchor text may be a useful discriminator among unseen child pages. These results show the foundations necessary for the success of many web systems, including search engines, focused crawlers, linkage analyzers, and intelligent web agents.
Document Expansion for Speech Retrieval
Authors: Amit Singhal, Fernando C. N. Pereira
Venue: Conference SIGIR 1999
Year: 1999

Advances in automatic speech recognition allow us to search large speech collections using traditional information retrieval methods. The problem of \aboutness" for documents | is a document about a certain concept | has been at the core of document indexing for the entire history of IR. This problem is more di cult for speech indexing since automatic speech transcriptions often contain mistakes. In this study we s h o w t h a t document expansion can be successfully used to alleviate the e ect of transcription mistakes on speech r etrieval. The loss of retrieval e ectiveness due to automatic transcription errors can be reduced by document expansion from 15{27% relative t o r e t r i e v al from human transcriptions to only about 7{13%, even for automatic transcriptions with word error rates as high as 65%. For good automatic transcriptions (25% word error rate), retrieval e ectiveness with document expansion is indistinguishable from retrieval from human transcriptions. This makes speech retrieval from automatic transcriptions, even poor ones, competitive with retrieval from perfect transcriptions.
Parameterized generation of labeled datasets for text categorization based on a hierarchical directory
Authors: Dmitry Davidov, Evgeniy Gabrilovich, Shaul Markovitch
Venue: Conference SIGIR 2004
Year: 2004

Although text categorization is a burgeoning area of IR research, readily available test collections in this field are surprisingly scarce. We describe a methodology and system (named ACCIO) for automatically acquiring labeled datasets for text categorization from the World Wide Web, by capitalizing on the body of knowledge encoded in the structure of existing hierarchical directories such as the Open Directory. We define parameters of categories that make it possible to acquire numerous datasets with desired properties, which in turn allow better control over categorization experiments. In particular, we develop metrics that estimate the difficulty of a dataset by examining the host directory structure. These metrics are shown to be good predictors of categorization accuracy that can be achieved on a dataset, and serve as efficient heuristics for generating datasets subject to user's requirements. A large collection of automatically generated datasets are made available for other researchers to use.
Mining contrastive opinions on political texts using cross-perspective topic model
Authors: Yi Fang, Luo Si, Naveen Somasundaram, Zhengtao Yu
Venue: Conference WSDM 2012
Year: 2012

This paper presents a novel opinion mining research problem, which is called Contrastive Opinion Modeling (COM). Given any query topic and a set of text collections from multiple perspectives, the task of COM is to present the opinions of the individual perspectives on the topic, and furthermore to quantify their difference. This general problem subsumes many interesting applications, including opinion summarization and forecasting, government intelligence and cross-cultural studies. We propose a novel unsupervised topic model for contrastive opinion modeling. It simulates the generative process of how opinion words occur in the documents of different collections. The ad hoc opinion search process can be efficiently accomplished based on the learned parameters in the model. The difference of perspectives can be quantified in a principled way by the Jensen-Shannon divergence among the individual topic-opinion distributions. An extensive set of experiments have been conducted to evaluate the proposed model on two datasets in the political domain: 1) statement records of U.S. senators; 2) world news reports from three representative media in U.S., China and India, respectively. The experimental results with both qualitative and quantitative analysis have shown the effectiveness of the proposed model.
Citation recommendation without author supervision
Authors: Qi He, Daniel Kifer, Jian Pei, Prasenjit Mitra, C. Lee Giles
Venue: Conference WSDM 2011
Year: 2011

Automatic recommendation of citations for a manuscript is highly valuable for scholarly activities since it can substantially improve the efficiency and quality of literature search. The prior techniques placed a considerable burden on users, who were required to provide a representative bibliography or to mark passages where citations are needed. In this paper we present a system that considerably reduces this burden: a user simply inputs a query manuscript (without a bibliography) and our system automatically finds locations where citations are needed. We show that naïve approaches do not work well due to massive noise in the document corpus. We produce a successful approach by carefully examining the relevance between segments in a query manuscript and the representative segments extracted from a document corpus. An extensive empirical evaluation using the CiteSeerX data set shows that our approach is effective.
Timeline generation with social attention
Authors: Wayne Xin Zhao, Yanwei Guo, Rui Yan, Yulan He, Xiaoming Li
Venue: Conference SIGIR 2013
Year: 2013

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of any given topic. It thus attracts much attention from research communities in recent years. Nevertheless, existing work on timeline generation often ignores an important factor, the attention attracted to topics of interest (hereafter termed "social attention"). Without taking into consideration social attention, the generated timelines may not reflect users' collective interests. In this paper, we study how to incorporate social attention in the generation of timeline summaries. In particular, for a given topic, we capture social attention by learning users' collective interests in the form of word distributions from Twitter, which are subsequently incorporated into a unified framework for timeline summary generation. We construct four evaluation sets over six diverse topics. We demonstrate that our proposed approach is able to generate both informative and interesting timelines. Our work sheds light on the feasibility of incorporating social attention into traditional text mining tasks.