OpenRaiser/Intern-Atlas huggingface dataset:
Conquering Cross-source Failure for News Credibility: Learning Generalizable Representations beyond Content Embedding
Authors: Yen-Hao Huang, Ting-Wei Liu, Ssu-Rui Lee, Fernando Henrique Calderon Alvarado, Yi-Shin Chen
Venue: Conference WWW 2020
Year: 2020

False information on the Internet has caused severe damage to society. Researchers have proposed methods to determine the credibility of news and have obtained good results. As different media sources (publishers) have different content generators (writers) and may focus on different topics or aspects, the word/topic distribution for each media source is divergent from others. We expose a challenge in the generalizability of existing content-based methods to perform consistently when applied to news from media sources non-existing in the training set, namely the cross-source failure. A cross-source setting can cause a decrease beyond in accuracy for current methods; content-sensitive features are considered one of the major causes of cross-source failure for a content-based approach. To overcome this challenge, we propose a syntactic network for news credibility (SYNC), which focuses on function words and syntactic structure to learn generalizable representations for news credibility and further reinforce the cross-source robustness for different media. Experiments with cross-validation on 194 real-world media sources showed that the proposed method could learn the generalizable features and outperformed the state-of-the-art methods on unseen media sources. Extensive analysis on the embedding feature representation represents a strength of the proposed method compared to current content embedding feature approaches. We envision that the proposed method is more robust for real-life application with SYNC on account of its good generalizability.
Assessment of tweet credibility with LDA features
Authors: Jun Ito, Jing Song, Hiroyuki Toda, Yoshimasa Koike, Satoshi Oyama
Venue: Conference WWW 2015
Year: 2015

With the fast development of Social Networking Services (SNS) such as Twitter, which enable users to exchange short messages online, people can get information not only from the traditional news media but also from the masses of SNS users. However, SNS users sometimes propagate spurious or misleading information, so an effective way to automatically assess the credibility of information is required. In this paper, we propose methods to assess information credibility on Twitter, methods that utilize the "tweet topic" and "user topic" features derived from the Latent Dirichlet Allocation (LDA) model. We collected two thousand tweets labeled by seven annotators each, and designed effective features for our classifier on the basis of data analysis results. An experiment we conducted showed a 3% improvement in Area Under Curve (AUC) scores compared with existing methods, leading us to conclude that using topical features is an effective way to assess tweet credibility.
Demand-aware charger planning for electric vehicle sharing
Authors: Bowen Du, Yongxin Tong, Zimu Zhou, Qian Tao, Wenjun Zhou
Venue: Conference KDD 2018
Year: 2018

Cars of the future have been predicted as shared and electric. There has been a rapid growth in electric vehicle (EV) sharing services worldwide in recent years. For EV-sharing platforms to excel, it is essential for them to offer private charging infrastructure for exclusive use that meets the charging demand of their clients. Particularly, they need to plan not only the places to build charging stations, but also the amounts of chargers per station, to maximally satisfy the requirements on global charging coverage and local charging demand. Existing research efforts are either inapplicable for their different problem formulations or are at a coarse granularity. In this paper, we formulate the \underlineE lectric \underlineV ehicle \underlineC harger \underlineP lanning (EVCP) problem especially for EV-sharing. We prove that the \shortpro problem is NP-hard, and design an approximation algorithm to solve the problem with a theoretical bound of $1-\frac1 e $. We also devise some optimization techniques to speed up the solution. Extensive experiments on real-world datasets validate the effectiveness and the efficiency of our proposed solutions.
Efficient and Effective Express via Contextual Cooperative Reinforcement Learning
Authors: Yexin Li, Yu Zheng, Qiang Yang
Venue: Conference KDD 2019
Year: 2019

Express systems are widely deployed in many major cities. Couriers in an express system load parcels at transit station and deliver them to customers. Meanwhile, they also try to serve the pick-up requests which come stochastically in real time during the delivery process. Having brought much convenience and promoted the development of e-commerce, express systems face challenges on courier management to complete the massive number of tasks per day. Considering this problem, we propose a reinforcement learning based framework to learn a courier management policy. Firstly, we divide the city into independent regions, in each of which a constant number of couriers deliver parcels and serve requests cooperatively. Secondly, we propose a soft-label clustering algorithm named Balanced Delivery-Service Burden (BDSB) to dispatch parcels to couriers in each region. BDSB guarantees that each courier has almost even delivery and expected request-service burden when departing from transit station, giving a reasonable initialization for online management later. As pick-up requests come in real time, a Contextual Cooperative Reinforcement Learning (CCRL) model is proposed to guide where should each courier deliver and serve in each short period. Being formulated in a multi-agent way, CCRL focuses on the cooperation among couriers while also considering the system context. Experiments on real-world data from Beijing are conducted to confirm the outperformance of our model.
Tagme: On-the-fly Annotation of Short Text Fragments (by Wikipedia Entities
Authors: Paolo Ferragina, Ugo Scaiella
Venue: Conference CIKM 2010
Year: 2010

We designed and implemented TAGME, a system that is able to efficiently and judiciously augment a plain-text with pertinent hyperlinks to Wikipedia pages. The specialty of TAGME with respect to known systems [5,8] is that it may annotate texts which are short and poorly composed, such as snippets of search-engine results, tweets, news, etc.. This annotation is extremely informative, so any task that is currently addressed using the bag-of-words paradigm could benefit from using this annotation to draw upon (the millions of) Wikipedia pages and their inter-relations.
A novel tf-idf weighting scheme for effective ranking
Authors: Jiaul H. Paik
Venue: Conference SIGIR 2013
Year: 2013

Term weighting schemes are central to the study of information retrieval systems. This article proposes a novel TF-IDF term weighting scheme that employs two different within document term frequency normalizations to capture two different aspects of term saliency. One component of the term frequency is effective for short queries, while the other performs better on long queries. The final weight is then measured by taking a weighted combination of these components, which is determined on the basis of the length of the corresponding query.
Unified point-of-interest recommendation with temporal interval assessment
Authors: Yanchi Liu, Chuanren Liu, Bin Liu, Meng Qu, Hui Xiong
Venue: Conference KDD 2016
Year: 2016

Point-of-interest (POI) recommendation, which helps mobile users explore new places, has become an important location-based service. Existing approaches for POI recommendation have been mainly focused on exploiting the information about user preferences, social influence, and geographical influence. However, these approaches cannot handle the scenario where users are expecting to have POI recommendation for a specific time period. To this end, in this paper, we propose a unified recommender system, named the 'Where and When to gO' (WWO) recommender system, to integrate the user interests and their evolving sequential preferences with temporal interval assessment. As a result, the WWO system can make recommendations dynamically for a specific time period and the traditional POI recommender system can be treated as the special case of the WWO system by setting this time period long enough. Specifically, to quantify users' sequential preferences, we consider the distributions of the temporal intervals between dependent POIs in the historical check-in sequences. Then, to estimate the distributions with only sparse observations, we develop the low-rank graph construction model, which identifies a set of bi-weighted graph bases so as to learn the static user preferences and the dynamic sequential preferences in a coherent way. Finally, we evaluate the proposed approach using real-world data sets from several location-based social networks (LBSNs). The experimental results show that our method outperforms the state-of-the-art approaches for POI recommendation in terms of various metrics, such as F-measure and NDCG, with a significant margin.
Time is of the essence: a joint hierarchical rnn and point process model for time and item predictions
Authors: Bjørnar Vassøy, Massimiliano Ruocco, Eliezer de Souza da Silva, Erlend Aune
Venue: Conference WSDM 2019
Year: 2019

In recent years session-based recommendation has emerged as an increasingly applicable type of recommendation. As sessions consist of sequences of events, this type of recommendation is a natural fit for Recurrent Neural Networks (RNNs). Several additions have been proposed for extending such models in order to handle specific problems or data. Two such extensions are 1.) modeling of inter-session relations for catching long term dependencies over user sessions, and 2.) modeling temporal aspects of user-item interactions. The former allows the session-based recommendation to utilize extended session history and inter-session information when providing new recommendations. The latter has been used to both provide state-of-the-art predictions for when the user will return to the service and also for improving recommendations. In this work, we combine these two extensions in a joint model for the tasks of recommendation and return-time prediction. The model consists of a Hierarchical RNN for the inter-session and intra-session items recommendation extended with a Point Process model for the time-gaps between the sessions. The experimental results indicate that the proposed model improves recommendations significantly on two datasets over a strong baseline, while simultaneously improving return-time predictions over a baseline return-time prediction model.
Analyzing and predicting viral tweets
Authors: Maximilian Jenders, Gjergji Kasneci, Felix Naumann
Venue: Conference WWW 2013
Year: 2013

Twitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of opinion formation and many other opinion mining tasks.
Fact-Checking, Fake News, Propaganda, Media Bias, and the COVID-19 Infodemic
Authors: Preslav Nakov, Giovanni Da San Martino, Firoj Alam
Venue: Conference WSDM 2022
Year: 2022

Social media have democratized content creation and have made it easy for anybody to spread information online. However, stripping traditional media from their gate-keeping role has left the public unprotected against biased, deceptive and disinformative content, which could now travel online at breaking-news speed and influence major public events. For example, during the COVID-19 pandemic, a new blending of medical and political disinformation has given rise to the first global infodemic. We offer an overview of the emerging and inter-connected research areas of fact-checking, disinformation, "fake news'', propaganda, and media bias detection. We explore the general fact-checking pipeline and important elements thereof such as check-worthiness estimation, spotting previously fact-checked claims, stance detection, source reliability estimation, detection of persuasion techniques, and detecting malicious users in social media. We also cover large-scale pre-trained language models, and the challenges and opportunities they offer for generating and for defending against neural fake news. Finally, we discuss the ongoing COVID-19 infodemic.
High- Dimensional Sparse Cross-Modal Hashing with Fine-Grained Similarity Embedding
Authors: Yongxin Wang, Zhen-Duo Chen, Xin Luo, Xin-Shun Xu
Venue: Conference WWW 2021
Year: 2021

Recently, with the discoveries in neurobiology, high-dimensional sparse hashing has attracted increasing attention. In contrast with general hashing that generates low-dimensional hash codes, the high-dimensional sparse hashing maps inputs into a higher dimensional space and generates sparse hash codes, achieving superior performance. However, the sparse hashing has not been fully studied in hashing literature yet. For example, how to fully explore the power of sparse coding in cross-modal retrieval tasks; how to discretely solve the binary and sparse constraints so as to avoid the quantization error problem. Motivated by these issues, in this paper, we present an efficient sparse hashing method, i.e., High-dimensional Sparse Cross-modal Hashing, HSCH for short. It not only takes the high-level semantic similarity of data into consideration, but also properly exploits the low-level feature similarity. In specific, we theoretically design a fine-grained similarity with two critical fusion rules. Then we take advantage of sparse codes to embed the fine-grained similarity into the to-be-learnt hash codes. Moreover, an efficient discrete optimization algorithm is proposed to solve the binary and sparse constraints, reducing the quantization error. In light of this, it becomes much more trainable, and the learnt hash codes are more discriminative. More importantly, the retrieval complexity of HSCH is as efficient as general hash methods. Extensive experiments on three widely-used datasets demonstrate the superior performance of HSCH compared with several state-of-the-art cross-modal hashing approaches.
Outage prediction and diagnosis for cloud service systems
Authors: Yujun Chen, Xian Yang, Qingwei Lin, Hongyu Zhang, Feng Gao, Zhangwei Xu, Yingnong Dang, Dongmei Zhang, Hang Dong, Yong Xu, Hao Li, Yu Kang
Venue: Conference WWW 2019
Year: 2019

With the rapid growth of cloud service systems and their increasing complexity, service failures become unavoidable. Outages, which are critical service failures, could dramatically degrade system availability and impact user experience. To minimize service downtime and ensure high system availability, we develop an intelligent outage management approach, called AirAlert, which can forecast the occurrence of outages before they actually happen and diagnose the root cause after they indeed occur. AirAlert works as a global watcher for the entire cloud system, which collects all alerting signals, detects dependency among signals and proactively predicts outages that may happen anywhere in the whole cloud system. We analyze the relationships between outages and alerting signals by leveraging Bayesian network and predict outages using a robust gradient boosting tree based classification method. The proposed outage management approach is evaluated using the outage dataset collected from a Microsoft cloud system and the results confirm the effectiveness of the proposed approach.
Causal inference-based root cause analysis for online service systems with intervention recognition
Authors: Mingjie Li 0005, Zeyan Li, Kanglin Yin, Xiaohui Nie, Wenchi Zhang, Kaixin Sui, Dan Pei
Venue: Conference KDD 2022
Year: 2022

Fault diagnosis is critical in many domains, as faults may lead to safety threats or economic losses. In the field of online service systems, operators rely on enormous monitoring data to detect and mitigate failures. Quickly recognizing a small set of root cause indicators for the underlying fault can save much time for failure mitigation. In this paper, we formulate the root cause analysis problem as a new causal inference task named intervention recognition. We proposed a novel unsupervised causal inference-based method named Causal Inference-based Root Cause Analysis (CIRCA). The core idea is a sufficient condition for a monitoring variable to be a root cause indicator, i.e., the change of probability distribution conditioned on the parents in the Causal Bayesian Network (CBN). Towards the application in online service systems, CIRCA constructs a graph among monitoring metrics based on the knowledge of system architecture and a set of causal assumptions. The simulation study illustrates the theoretical reliability of CIRCA. The performance on a real-world dataset further shows that CIRCA can improve the recall of the top-1 recommendation by 25% over the best baseline method.
Candidate generation with binary codes for large-scale top-n recommendation
Authors: Wang-Cheng Kang, Julian John McAuley
Venue: Conference CIKM 2019
Year: 2019

Generating the Top-N recommendations from a large corpus is computationally expensive to perform at scale. Candidate generation and re-ranking based approaches are often adopted in industrial settings to alleviate efficiency problems. However it remains to be fully studied how well such schemes approximate complete rankings (or how many candidates are required to achieve a good approximation), or to develop systematic approaches to generate high-quality candidates efficiently. In this paper, we seek to investigate these questions via proposing a candidate generation and re-ranking based framework (CIGAR), which first learns a preference-preserving binary embedding for building a hash table to retrieve candidates, and then learns to re-rank the candidates using real-valued ranking models with a candidate-oriented objective. We perform a comprehensive study on several large-scale real-world datasets consisting of millions of users/items and hundreds of millions of interactions. Our results show that CIGAR significantly boosts the Top-N accuracy against state-of-the-art recommendation models, while reducing the query time by orders of magnitude. We hope that this work could draw more attention to the candidate generation problem in recommender systems.
Slice: Scalable Linear Extreme Classifiers trained on 100 Million Labels for Related Searches
Authors: Himanshu Jain, Venkatesh Balasubramanian, Bhanu Chunduri, Manik Varma
Venue: Conference WSDM 2019
Year: 2019

This paper reformulates the problem of recommending related queries on a search engine as an extreme multi-label learning task. Extreme multi-label learning aims to annotate each data point with the most relevant subset of labels from an extremely large label set. Each of the top 100 million queries on Bing was treated as a separate label in the proposed reformulation and an extreme classifier was learnt which took the user's query as input and predicted the relevant subset of 100 million queries as output. Unfortunately, state-of-the-art extreme classifiers have not been shown to scale beyond 10 million labels and have poor prediction accuracies for queries. This paper therefore develops the Slice algorithm which can be accurately trained on low-dimensional, dense deep learning features popularly used to represent queries and which efficiently scales to 100 million labels and 240 million training points. Slice achieves this by reducing the training and prediction times from linear to logarithmic in the number of labels based on a novel negative sampling technique. This allows the proposed reformulation to address some of the limitations of traditional related searches approaches in terms of coverage, density and quality. Experiments on publically available extreme classification datasets with low-dimensional dense features as well as related searches datasets mined from the Bing logs revealed that slice could be more accurate than leading extreme classifiers while also scaling to 100 million labels. Furthermore, slice was found to improve the accuracy of recommendations by 10% as compared to state-of-the-art related searches techniques. Finally, when added to the ensemble in production in Bing, slice was found to increase the trigger coverage by 52%, the suggestion density by 33%, the overall success rate by 2.6% and the success rate for tail queries by 12.6%. Slice's source code can be downloaded from [21].
Rate: Overcoming noise and sparsity of textual features in real-time location estimation
Authors: Yu Zhang, Wei Wei, Binxuan Huang, Kathleen M. Carley, Yan Zhang
Venue: Conference CIKM 2017
Year: 2017

Real-time location inference of social media users is the fundamental of some spatial applications such as localized search and event detection. While tweet text is the most commonly used feature in location estimation, most of the prior works suffer from either the noise or the sparsity of textual features. In this paper, we aim to tackle these two problems. We use topic modeling as a building block to characterize the geographic topic variation and lexical variation so that "one-hot" encoding vectors will no longer be directly used. We also incorporate other features which can be extracted through the Twitter streaming API to overcome the noise problem. Experimental results show that our RATE algorithm outperforms several benchmark methods, both in the precision of region classification and the mean distance error of latitude and longitude regression.
Rethinking Graph Convolutional Networks in Knowledge Graph Completion
Authors: Zhanqiu Zhang, Jie Wang, Jieping Ye, Feng Wu
Venue: Conference WWW 2022
Year: 2022

Graph convolutional networks (GCNs) -- which are effective in modeling graph structures -- have been increasingly popular in knowledge graph completion (KGC). GCN-based KGC models first use GCNs to generate expressive entity representations and then use knowledge graph embedding (KGE) models to capture the interactions among entities and relations. However, many GCN-based KGC models fail to outperform state-of-the-art KGE models though introducing additional computational complexity. This phenomenon motivates us to explore the real effect of GCNs in KGC. Therefore, in this paper, we build upon representative GCN-based KGC models and introduce variants to find which factor of GCNs is critical in KGC. Surprisingly, we observe from experiments that the graph structure modeling in GCNs does not have a significant impact on the performance of KGC models, which is in contrast to the common belief. Instead, the transformations for entity representations are responsible for the performance improvements. Based on the observation, we propose a simple yet effective framework named LTE-KGE, which equips existing KGE models with linearly transformed entity embeddings. Experiments demonstrate that LTE-KGE models lead to similar performance improvements with GCN-based KGC methods, while being more computationally efficient. These results suggest that existing GCNs are unnecessary for KGC, and novel GCN-based KGC models should count on more ablation studies to validate their effectiveness. The code of all the experiments is available on GitHub at https://github.com/MIRALab-USTC/GCN4KGC.