OpenRaiser/Intern-Atlas huggingface dataset:
AttentionFlow: Visualising Influence in Networks of Time Series
Authors: Minjeong Shin, Alasdair Tran, Siqi Wu, Alexander Patrick Mathews, Rong Wang, Georgiana Lyall, Lexing Xie
Venue: Conference WSDM 2021
Year: 2021

The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest. Moreover, attention trends of different items exhibit mutual influence via mechanisms such as hyperlinks or recommendations. Many visualisation tools exist for time series, network evolution, or network influence; however, few systems connect all three. In this work, we present AttentionFlow, a new system to visualise networks of time series and the dynamic influence they have on one another. Centred around an ego node, our system simultaneously presents the time series on each node using two visual encodings: a tree ring for an overview and a line chart for details. AttentionFlow supports interactions such as overlaying time series of influence, and filtering neighbours by time or flux. We demonstrate AttentionFlow using two real-world datasets, VevoMusic and WikiTraffic. We show that attention spikes in songs can be explained by external events such as major awards, or changes in the network such as the release of a new song. Separate case studies also demonstrate how an artist's influence changes over their career, and that correlated Wikipedia traffic is driven by cultural interests. More broadly, AttentionFlow can be generalised to visualise networks of time series on physical infrastructures such as road networks, or natural phenomena such as weather and geological measurements.
Vandalism Detection in OpenStreetMap via User Embeddings
Authors: Yinxiao Li, T. Jennings Anderson, Yiqi Niu
Venue: Conference CIKM 2021
Year: 2021

OpenStreetMap (OSM) is a free and openly-editable database of geographic information. Over the years, OSM has evolved into the world's largest open knowledge base of geospatial data, and protecting OSM from the risk of vandalized and falsified information has become paramount to ensuring its continued success. However, despite the increasing usage of OSM and a wide interest in vandalism detection on open knowledge bases such as Wikipedia and Wikidata, OSM has not attracted as much attention from the research community, partially due to a lack of publicly available vandalism corpus. In this paper, we report on the construction of the first OSM vandalism corpus, and release it publicly. We describe a user embedding approach to create OSM user embeddings and add embedding features to a machine learning model to improve vandalism detection in OSM. We validate the model against our vandalism corpus, and observe solid improvements in key metrics. The validated model is deployed into production for vandalism detection on Daylight Map.
NTAM: Neighborhood-temporal attention model for disk failure prediction in cloud platforms
Authors: Chuan Luo, Pu Zhao, Bo Qiao, Youjiang Wu, Hongyu Zhang, Wei Wu, Weihai Lu, Yingnong Dang, Saravanakumar Rajmohan, Qingwei Lin, Dongmei Zhang
Venue: Conference WWW 2021
Year: 2021

With the rapid deployment of cloud platforms, high service reliability is of critical importance. An industrial cloud platform contains a huge number of disks, and disk failure is a common cause of service unreliability. In recent years, many machine learning based disk failure prediction approaches have been proposed, and they can predict disk failures based on disk status data before the failures actually happen. In this way, proactive actions can be taken in advance to improve service reliability. However, existing approaches treat each disk individually and do not explore the influence of the neighboring disks. In this paper, we propose Neighborhood-Temporal Attention Model (NTAM), a novel deep learning based approach to disk failure prediction. When predicting whether or not a disk will fail in near future, NTAM is a novel approach that not only utilizes a disk’s own status data, but also considers its neighbors’ status data. Moreover, NTAM includes a novel attention-based temporal component to capture the temporal nature of the disk status data. Besides, we propose a data enhancement method, called Temporal Progressive Sampling (TPS), to handle the extreme data imbalance issue. We evaluate NTAM on a public dataset as well as two industrial datasets collected from millions of disks in Microsoft Azure. Our experimental results show that NTAM significantly outperforms state-of-the-art competitors. Also, our empirical evaluations indicate the effectiveness of the neighborhood-ware component and the temporal component underlying NTAM as well as the effectiveness of TPS. More encouragingly, we have successfully applied NTAM and TPS to Microsoft cloud platforms (including Microsoft Azure and Microsoft 365) and obtained benefits in industrial practice.
Finding interesting posts in twitter based on retweet graph analysis
Authors: Min-Chul Yang, Jung-Tae Lee, Seung-Wook Lee, Hae-Chang Rim
Venue: Conference SIGIR 2012
Year: 2012

Millions of posts are being generated in real-time by users in social networking services, such as Twitter. However, a considerable number of those posts are mundane posts that are of interest to the authors and possibly their friends only. This paper investigates the problem of automatically discovering valuable posts that may be of potential interest to a wider audience. Specifically, we model the structure of Twitter as a graph consisting of users and posts as nodes and retweet relations between the nodes as edges. We propose a variant of the HITS algorithm for producing a static ranking of posts. Experimental results on real world data demonstrate that our method can achieve better performance than several baseline methods.
Social media dark side content detection using transfer learning emphasis on hate and conflict
Authors: Zewdie Mossie
Venue: Conference WWW 2020
Year: 2020

Although online content continues to grow, the prevalence of dark side content such as hate, misinformation, disinformation, conflicting, fake, and so on continues to grow and has become a problem for online and offline society. Consequently, work into automated analytical and detection methods has gained much attention. The scarcity of the labeled dataset has, however, become one of the major challenges in both machine and deep learning to develop an effective supervised learning model. As a result, most State-of-the-Art (SOTA) approaches focus on English languages for the detection of such content. The identification task of such content has become a problem due to the diversity of languages used on social media platforms. We propose transfer learning since it needs only access to a large unlabeled text available on social media platforms. Since we use data from Amharic Language, which is in the low-resource language family for machine leaarning, transfer learning is found effective. First, we prepare a topic and word embedding models using Facebook data as a task-specific and a general corpus from different web domains respectively. Second, we combine topic embedding and word embedding and then send the features to a fully-connected Recurrent Neural Networks (RNNs). Our preliminary experimental results from the newly proposed attention-based topic model combined with word embedding outperform the baselines.
Are your friends also haters? Identification of hater networks on social media: Data Paper
Authors: Maximilian Wich, Melissa Breitinger, Wienke Strathern, Marlena Naimarevic, Georg Groh, Jürgen Pfeffer
Venue: Conference WWW 2021
Year: 2021

Hate speech on social media platforms has become a severe issue in recent years. To cope with it, researchers have developed machine learning-based classification models. Due to the complexity of the problem, the models are far from perfect. A promising approach to improve them is to integrate social network data as additional features in the classification. Unfortunately, there is a lack of datasets containing text and social network data to investigate this phenomenon. Therefore, we develop an approach to identify and collect hater networks on Twitter that uses a pre-trained classification model to focus on hateful content. The contributions of this article are (1) an approach to identify hater networks and (2) an anonymized German offensive language dataset that comprises social network data. The dataset consists of 4,647,200 labeled tweets and a social graph with 49,353 users and 122,053 edges.
Improving end-to-end sequential recommendations with intent-aware diversification
Authors: Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, Maarten de Rijke
Venue: Conference CIKM 2020
Year: 2020

Sequential recommenders that capture users' dynamic intents by modeling sequential behavior, are able to accurately recommend items to users. Previous studies on sequential recommendations (SRs) mostly focus on optimizing the recommendation accuracy, thus ignoring the diversity of recommended items. Many existing methods for improving the diversity of recommended items are not applicable to SRs because they assume that user intents are static and rely on post-processing the list of recommended items to promote diversity. We consider both accuracy and diversity by reformulating SRs as a list generation task, and propose an integrated approach with an end-to-end neural model, called intent-aware diversified sequential recommendation (IDSR). Specifically, we introduce an implicit intent mining (IIM) module for SR to capture multiple user intents reflected in sequences of user behavior. We design an intent-aware diversity promoting (IDP) loss function to supervise the learning of the IIM module and guide the model to take diversity into account during training. Extensive experiments on four datasets show that IDSR significantly outperforms state-of-the-art methods in terms of recommendation diversity while yielding comparable or superior recommendation accuracy.
A Topic and Concept Integrated Model for Thread Recommendation in Online Health Communities
Authors: Mingda Li, Weiting Gao, Yi Chen
Venue: Conference CIKM 2020
Year: 2020

Online health communities (OHCs) provide a popular channel for users to seek information, suggestions and support during their medical treatment and recovery processes. To help users find relevant information easily, we present CLIR, an effective system for recommending relevant discussion threads to users in OHCs. We identify that thread content and user interests can be categorized in two dimensions: topics and concepts. CLIR leverages Latent Dirichlet Allocation model to summarize the topic dimension and uses Convolutional Neural Network to encode the concept dimension. It then builds a thread neural network to capture thread characteristics and builds a user neural network to capture user interests by integrating these two dimensions and their interactions. Finally, it matches the target thread's characteristics with candidate users' interests to make recommendations. Experimental evaluation with multiple OHC datasets demonstrates the performance advantage of CLIR over the state-of-the-art recommender systems on various evaluation metrics.
Globally optimized matchmaking in online games
Authors: Qilin Deng, Hao Li, Kai Wang, Zhipeng Hu, Runze Wu, Linxia Gong, Jianrong Tao, Changjie Fan, Peng Cui
Venue: Conference KDD 2021
Year: 2021

As one of the core components of online games, matchmaking is the process of arranging multiple players into matches, where the quality of matchmaking systems directly determines player satisfaction and further affects the life cycle of game products. With the number of candidate players increases, the number of possible match combinations grows exponentially, which makes the current implementation for multiplayer matchmaking can only obtain locally optimal arrangement in an inefficient fashion. In this paper, we focus on the globally optimized matchmaking problem, in which the objective is to decide an optimal matching sequence for the queuing players. To tackle this challenging problem, we propose a novel data-driven matchmaking framework, called GloMatch, based on machine learning principles. Through transforming the matchmaking problem into a sequential decision problem, we solve it with the help of an effective policy-based deep reinforcement learning algorithm. Quantitative experiments on simulation and online game environments demonstrate the effectiveness of the presented framework.
ROSC: robust spectral clustering on multi-scale data
Authors: Xiang Li, Ben Kao, Siqiang Luo, Martin Ester
Venue: Conference WWW 2018
Year: 2018

We investigate the effectiveness of spectral methods in clustering multi-scale data, which is data whose clusters are of various sizes and densities. We review existing spectral methods that are designed to handle multi-scale data and propose an alternative approach that is orthogonal to existing methods. We put forward the algorithm ROSC, which computes an affinity matrix that takes into account both objects' feature similarity and reachability similarity. We perform extensive experiments comparing ROSC against 9 other methods on both real and synthetic datasets. Our results show that ROSC performs very well against the competitors. In particular, it is very robust in that it consistently performs well over all the datasets tested. Also, it outperforms others by wide margins for datasets that are highly multi-scale.
Query Classification with Multi-Objective Backoff Optimization
Authors: Hang Yu, Lester Litchfield
Venue: Conference SIGIR 2020
Year: 2020

E-commerce platforms greatly benefit from high-quality search that retrieves relevant search results in response to search terms. For the sake of search relevance, Query Classification (QC) has been widely adopted to make search engines robust against low text quality and complex category hierarchy. Generally, QC solutions categorize search queries and direct users to the suggested categories whereby the search results are then retrieved. In this way, the search scope is contextually constrained to increase search relevance. However, such operations might risk deteriorating e-commerce metrics when irrelevant categories are suggested. Thus, QC solutions are expected to demonstrate high accuracy. Unfortunately, existing QC methods mainly focus on the intrinsic performance of classifiers whereas fail to consider post-inference optimization that could further improve reliability. To fill up the research gap, we propose the Query Classification with Multi-objective Backoff (QCMB). The proposed solution consists of two steps: 1) hierarchical text classification that classifies search queries into multi-level categories; and 2) multi-objective backoff that substitutes potentially misclassified leaf categories with appropriate ancestors that optimize the trade-off between accuracy and depth. The proposed QCMB is evaluated using the real-world search data of Trade Me that is the largest e-commerce platform in New Zealand. Compared with the benchmarks, QCMB delivers superior solutions with flexible tuning to satisfy different users' demands. To the best of our knowledge, this work is the first attempt to enhance QC with multi-objective optimization.
A Dynamic Product-Aware Learning Model for E-Commerce Query Intent Understanding
Authors: Jiashu Zhao, Hongshen Chen, Dawei Yin
Venue: Conference CIKM 2019
Year: 2019

Query intent understanding is a fundamental and essential task in searching, which promotes personalized retrieval results and users' satisfaction. In E-commerce, query understanding is particularly referring to bridging the gap between query representations and product representations. In this paper, we aim to map the queries into the predefined tens of thousands of fine-grained categories extracted from the product descriptions. The problem is very challenging in several aspects. First, a query may be related to multiple categories and to identify all the best matching categories could eventually drive the search engine for high recall and diversity. Second, the same query may have dynamic intents under various scenarios and there is a need to distinguish the differences to promote accurate categories of products. Third, the tail queries are particularly difficult for understanding due to noise and lack of customer feedback information. To better understand the queries, we firstly conduct analysis on the search queries and behaviors in the E-commerce domain and identified the uniqueness of our problem (e.g. longer sessions). Then we propose a Dynamic Product-aware Hierarchical Attention (DPHA) framework to capture the explicit and implied meanings of a query given its context information in the session. Specifically, DPHA automatically learns the bidirectional query-level and self-attentional session-level representations which can capture both complex long range dependencies and structural information. Extensive experimental results on a real E-commerce query data set demonstrate the effectiveness of the proposed DPHA compared to the state-of-art baselines.
EXACT: Attributed Entity Extraction By Annotating Texts
Authors: Ke Chen, Lei Feng, Qingkuang Chen, Gang Chen, Lidan Shou
Venue: Conference SIGIR 2019
Year: 2019

Attributed entity is an entity defined by its structural attributes. Extracting attributed entities from textual documents is an important problem for a variety of big-data applications. We propose a system called EXACT for extracting attributed entities from textual documents by performing explorative annotation tasks, which create attributes and bind them to tag values. To support efficient annotation, we propose a novel tag recommendation technique based on a few-shot learning scheme which can suggest tags for new annotation tasks given very few human-annotated samples. We also propose a document recommendation scheme to provide run-time context for the user. Using a novel attribute index, the system can generate the task-relevant attributed entities on-the-fly. We demonstrate how these techniques can be integrated behind a novel user interface to enable productive and efficient extraction of attributed entities at limited cost in human annotation.
A flexible learning system for wrapping tables and lists in html documents
Authors: William W. Cohen, Matthew Hurst, Lee S. Jensen
Venue: Conference WWW 2002
Year: 2002

A program that makes an existing website look like a database is called a wrapper. Wrapper learning is the problem of learning website wrappers from examples. We present a wrapper-learning system called WL2 that can exploit several different representations of a document. Examples of such different representations include DOM-level and token-level representations, as well as two-dimensional geometric views of the rendered page (for tabular data) and representations of the visual appearance of text asm it will be rendered. Additionally, the learning system is modular, and can be easily adapted to new domains and tasks. The learning system described is part of an "industrial-strength" wrapper management system that is in active use at WhizBang Labs. Controlled experiments show that the learner has broader coverage and a faster learning rate than earlier wrapper-learning systems.
Learning to extract local events from the web
Authors: John Foley, Michael Bendersky, Vanja Josifovski
Venue: Conference SIGIR 2015
Year: 2015

The goal of this work is extraction and retrieval of local events from web pages. Examples of local events include small venue concerts, theater performances, garage sales, movie screenings, etc. We collect these events in the form of retrievable calendar entries that include structured information about event name, date, time and location. Between existing information extraction techniques and the availability of information on social media and semantic web technologies, there are numerous ways to collect commercial, high-profile events. However, most extraction techniques require domain-level supervision, which is not attainable at web scale. Similarly, while the adoption of the semantic web has grown, there will always be organizations without the resources or the expertise to add machine-readable annotations to their pages. Therefore, our approach bootstraps these explicit annotations to massively scale up local event extraction. We propose a novel event extraction model that uses distant supervision to assign scores to individual event fields (event name, date, time and location) and a structural algorithm to optimally group these fields into event records. Our model integrates information from both the entire source document and its relevant sub-regions, and is highly scalable. We evaluate our extraction model on all 700 million documents in a large publicly available web corpus, ClueWeb12. Using the 217,000 unique explicitly annotated events as distant supervision, we are able to double recall with 85% precision and quadruple it with 65% precision, with no additional human supervision. We also show that our model can be bootstrapped for a fully supervised approach, which can further improve the precision by 30%. In addition, we evaluate the geographic coverage of the extracted events. We find that there is a significant increase in the geo-diversity of extracted events compared to existing explicit annotations, while maintaining high precision levels.
GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce
Authors: Sean Bell, Yiqun Liu 0006, Sami Alsheikh, Yina Tang, Edward Pizzi, M. Henning, Karun Singh, Omkar Parkhi, Fedor Borisyuk
Venue: Conference KDD 2020
Year: 2020

In this paper, we present GrokNet, a deployed image recognition system for commerce applications. GrokNet leverages a multi-task learning approach to train a single computer vision trunk. We achieve a 2.1x improvement in exact product match accuracy when compared to the previous state-of-the-art Facebook product recognition system. We achieve this by training on 7 datasets across several commerce verticals, using 80 categorical loss functions and 3 embedding losses. We share our experience of combining diverse sources with wide-ranging label semantics and image statistics, including learning from human annotations, user-generated tags, and noisy search engine interaction data. GrokNet has demonstrated gains in production applications and operates at Facebook scale.