OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Generation of Pattern-controlled Product Description in E-commerce
Authors: Tao Zhang, Jin Zhang, Chengfu Huo, Weijun Ren
Venue: WWW
Year: 2019

Nowadays, online shoppers have paid more and more attention to detailed product descriptions, since a well-written description is a huge factor in making online sales. However, for a website with billions of product data like Alibaba, the writing efficiency of human copywriters cannot match the growth rate of new products. To address this issue, we propose a novel pointer-generator neural network to generate product description. In particular, coordinate encoders and a pattern-controlled decoder are utilized to improve generation quality with an attention mechanism. The coordinate encoders equipped with a Transformer and a gated convolutional unit is introduced to learn the source input representations. In the decoding phase, a pattern controlled decoder is proposed to control the output description pattern (such as category, length, and style) to ensure the quality of the description. For evaluation, we build a substantial collection of real-world products along with human-written descriptions. An extensive set of experiments with both human annotated data demonstrate the advantage of the proposed method for generation qualities. Finally, an online deployment shows significant benefits of our model in a real online shopping scenario, as measured by the click-through rate.
Learning Influence Probabilities and Modelling Influence Diffusion in Twitter
Authors: Zizhu Zhang, Weiliang Zhao, Jian Yang, Cécile Paris, Surya Nepal
Venue: WWW
Year: 2019

Influence diffusion has been widely studied in social networks for applications such as service promotion and marketing. There are two challenging issues here: (1) how we measure people’s influence on others; (2) how we predict whom would be influenced by a particular person and when people would be influenced. Existing works have not captured the temporal and structural characteristics of influence diffusion in Twitter. In this paper, we firstly develop a model to learn influence probabilities between users in Twitter from their action history; secondly, we introduce diffusion models that are used to predict how information is propagated in Twitter. Experiment results show that our proposed models outperform existing models in terms of the balanced precision and recall.
Auto-EM: End-to-end Fuzzy Entity-Matching using Pre-trained Deep Models and Transfer Learning
Authors: Chen Zhao, Yeye He
Venue: WWW
Year: 2019

Entity matching (EM), also known as entity resolution, fuzzy join, and record linkage, refers to the process of identifying records corresponding to the same real-world entities from different data sources. It is an important and long-standing problem in data integration and data mining. So far progresses have been made mainly in the form of model improvements, where models with better accuracy are developed when large amounts of training data is available. In real-world applications we find that advanced approaches can often require too many labeled examples that is expensive to obtain, which has become a key obstacle to wider adoption. We in this work take a different tack, proposing a transfer-learning approach to EM, leveraging pre-trained EM models from large-scale, production knowledge bases (KB). Specifically, for each entity-type in KB, (e.g., location, organization, people, etc.), we use rich synonymous names of known entities in the KB as training data, to pre-train type-detection and EM models for each type, using a novel hierarchical neural network architecture we develop. Given a new EM task, with little or no training data, we can either fine-tune or directly leverage pre-trained EM models, to build end-to-end, high-quality EM systems. Experiments on a variety of real EM tasks suggest that the pre-trained approach is effective and outperforms existing EM methods.1.
Abstractive Meeting Summarization via Hierarchical Adaptive Segmental Network Learning
Authors: Zhou Zhao, Haojie Pan, Changjie Fan, Yan Liu, Linlin Li, Min Yang
Venue: WWW
Year: 2019

Abstractive meeting summarization is a challenging problem in natural language understanding, which automatically generates the condensed summary covering the important points in the meeting conversation. However, the existing abstractive summarization works mainly focus on the structured text documents, which may be ineffectively applied to the meeting summarization task due to the lack of modeling the unstructured long-form conversational contents. In this paper, we consider the problem of abstractive meeting summarization from the viewpoint of hierarchical adaptive segmental encoder-decoder network learning. We propose the hierarchical neural encoder based on adaptive recurrent networks to learn the semantic representation of meeting conversation with adaptive conversation segmentation. We then develop the reinforced decoder network to generate the high-quality summaries for abstractive meeting summarization. We conduct the extensive experiments on the well-known AMI meeting conversation dataset to validate the effectiveness of our proposed method.
An Attention-based Model for Joint Extraction of Entities and Relations with Implicit Entity Features
Authors: Yan Zhou, Longtao Huang, Tao Guo, Songlin Hu, Jizhong Han
Venue: WWW
Year: 2019

Extracting entities and relations is critical to the understanding of massive text corpora. Recently, neural joint models have shown promising results for this task. However, the entity features are not effectively used in these joint models. In this paper, we propose an approach to utilize the implicit entity features in the joint model and show these features can facilitate the joint extraction task. Particularly, we use the hidden-layer vectors extracted from a pre-trained named entity recognition model as the entity features. Thus, our method does not need to design the entity features by hand and can benefit from the new development of named entity recognition task. In addition, we introduce an attention mechanism in our model which can select the informative parts of the input sentence to the prediction. We conduct a series of experiments on a public dataset and the results show the effectiveness of our model.
Fake News Detection: An Interdisciplinary Research
Authors: Xinyi Zhou, Reza Zafarani
Venue: WWW
Year: 2019

The explosive growth of fake news and its erosion to democracy, journalism and economy has increased the demand for fake news detection. To achieve efficient and explainable fake news detection, an interdisciplinary approach is required, relying on scientific contributions from various disciplines, e.g., social sciences, engineering, among others. Here, we illustrate how such multidisciplinary contributions can help detect fake news by improving feature engineering, or by providing well-justified machine learning models. We demonstrate how news content, news propagation patterns, and users’ engagements with news can help detect fake news.
Tortoise or Hare? Quantifying the Effects of Performance on Mobile App Retention
Authors: Agustin Zuniga, Huber Flores, Eemil Lagerspetz, Petteri Nurmi, Sasu Tarkoma, Pan Hui, Jukka Manner
Venue: WWW
Year: 2019

We contribute by quantifying the effect of network latency and battery consumption on mobile app performance and retention, i.e., user's decisions to continue or stop using apps. We perform our analysis by fusing two large-scale crowdsensed datasets collected by piggybacking on information captured by mobile apps. We find that app performance has an impact in its retention rate. Our results demonstrate that high energy consumption and high latency decrease the likelihood of retaining an app. Conversely, we show that reducing latency or energy consumption does not guarantee higher likelihood of retention as long as they are within reasonable standards of performance. However, we also demonstrate that what is considered reasonable depends on what users have been accustomed to, with device and network characteristics, and app category playing a role. As our second contribution, we develop a model for predicting retention based on performance metrics. We demonstrate the benefits of our model through empirical benchmarks which show that our model not only predicts retention accurately, but generalizes well across application categories, locations and other factors moderating the effect of performance.
Entity Summarization in Knowledge Graphs: Algorithms, Evaluation, and Applications
Authors: Gong Cheng, Kalpa Gunaratna, Evgeny Kharlamov
Venue: WWW
Year: 2020

Knowledge graphs (KGs) encapsulate entities and relationships that describe the entities. The concise representation format and graph nature of KGs have resulted in creating many novel Web and industrial applications and enhancing existing ones. However, in a KG, dozens or hundreds of facts describing an entity could exceed the capacity of a typical user interface and overload users with excessive amounts of information. This has motivated fruitful research on entity summarization—automated generation of compact summaries for entities to satisfy users’ information needs efficiently and effectively. Over the recent years, researchers have contributed to this problem by proposing approaches ranging from pure ranking and mining techniques to machine and deep learning techniques. The state of the art has continuously improved and at the same time made it harder for the community and new comers to the problem to keep up with the recent contributions and basic building blocks in the space. This tutorial aims to fill this gap.
Graph Attention Topic Modeling Network
Authors: Liang Yang, Fan Wu, Junhua Gu, Chuan Wang, Xiaochun Cao, Di Jin, Yuanfang Guo
Venue: WWW
Year: 2020

Existing topic modeling approaches possess several issues, including the overfitting issue of Probablistic Latent Semantic Indexing (pLSI), the failure of capturing the rich topical correlations among topics in Latent Dirichlet Allocation (LDA), and high inference complexity. In this paper, we provide a new method to overcome the overfitting issue of pLSI by using the amortized inference with word embedding as input, instead of the Dirichlet prior in LDA. For generative topic model, the large number of free latent variables is the root of overfitting. To reduce the number of parameters, the amortized inference replaces the inference of latent variable with a function which possesses the shared (amortized) learnable parameters. The number of the shared parameters is fixed and independent of the scale of the corpus. To overcome the limited application of amortized inference to independent and identically distributed (i.i.d) data, a novel graph neural network, Graph Attention TOpic Network (GATON), is proposed to model the topic structure of non-i.i.d documents according to the following two observations. First, pLSI can be interpreted as stochastic block model (SBM) on a specific bi-partite graph. Second, graph attention network (GAT) can be explained as the semi-amortized inference of SBM, which relaxes the i.i.d data assumption of vanilla amortized inference. GATON provides a novel scheme, i.e. graph convolution operation based scheme, to integrate word similarity and word co-occurrence structure. Specifically, the bag-of-words document representation is modeled as a bi-partite graph topology. Meanwhile, word embedding, which captures the word similarity, is modeled as attribute of the word node and the term frequency vector is adopted as the attribute of the document node. Based on the weighted (attention) graph convolution operation, the word co-occurrence structure and word similarity patterns are seamlessly integrated for topic identification. Extensive experiments demonstrate that the effectiveness of GATON on topic identification not only benefits the document classification, but also significantly refines the input word embedding.
Provably and Efficiently Approximating Near-cliques using the Turán Shadow: PEANUTS
Authors: Shweta Jain, C. Seshadhri
Venue: WWW
Year: 2020

Clique and near-clique counts are important graph properties with applications in graph generation, graph modeling, graph analytics, community detection among others. They are the archetypal examples of dense subgraphs. While there are several different definitions of near-cliques, most of them share the attribute that they are cliques that are missing a small number of edges. Clique counting is itself considered a challenging problem. Counting near-cliques is significantly harder more so since the search space for near-cliques is orders of magnitude larger than that of cliques. We give a formulation of a near-clique as a clique that is missing a constant number of edges. We exploit the fact that a near-clique contains a smaller clique, and use techniques for clique sampling to count near-cliques. This method allows us to count near-cliques with 1 or 2 missing edges, in graphs with tens of millions of edges. To the best of our knowledge, there was no known efficient method for this problem, and we obtain a 10x − 100x speedup over existing algorithms for counting near-cliques. Our main technique is a space efficient adaptation of the Turán Shadow sampling approach, recently introduced by Jain and Seshadhri (WWW 2017). This approach constructs a large recursion tree (called the Turán Shadow) that represents cliques in a graph. We design a novel algorithm that builds an estimator for near-cliques, using a online, compact construction of the Turán Shadow.
Scientific Collaboration Sustainability Prediction Based on H-index Reciprocity
Authors: Wei Wang, Junyang Chen, Weiwei Sun, Zhiguo Gong
Venue: WWW
Year: 2020

Despite the increasing interest to investigate and promote scientific collaborations, we know little about scientific collaboration sustainability. In this work-in-progress, we propose to investigate the extent to which scientific collaboration sustainability can be predicted. For this purpose, we design a reciprocity-based collaboration sustainability prediction model by injecting h-index reciprocity. Experiments indicate considering early-stage reciprocity can improve prediction performance compared with state-of-the-art methods.
Identifying Expert Intent Queries
Authors: Manoj K. Agarwal, Jaldeep Vasavada
Venue: WWW
Year: 2020

Search engines assess the quality of their results using nDCG@k for the queries in regular judgement sets. Since these sets are human annotated, the judgement sets are limited in size. Hence, these sets may not have proper representation from “Expert Topics”, e.g., , if an Expert Topic represents a tail segment. Therefore, query sets specific to expert topics are prepared to assess the search quality on such topics, which are judged by the domain experts. Such expert judgements are typically 5X-10X more expensive compared to regular judgements. Hence, “Expert Query Sets” must be prepared such that these queries are likely to be judged differently by an expert and a regular judge. In this paper, we define a novel problem, to identify a sub set of users queries, from a topic specific query set, such that the judgement difference between expert judges and regular judges, called yield rate, is high. Our results, across different expert topics, show that the expert query sets identified through our methodology have ∼15% more yield rate.
Apophanies or Epiphanies? How Crawlers Impact Our Understanding of the Web
Authors: Syed Suleman Ahmad, Muhammad Daniyal Dar, Muhammad Fareed Zaffar, Narseo Vallina-Rodriguez, Rishab Nithyanand
Venue: WWW
Year: 2020

Data generated by web crawlers has formed the basis for much of our current understanding of the Internet. However, not all crawlers are created equal and crawlers generally find themselves trading off between computational overhead, developer effort, data accuracy, and completeness. Therefore, the choice of crawler has a critical impact on the data generated and knowledge inferred from it. In this paper, we conduct a systematic study of the trade-offs presented by different crawlers and the impact that these can have on various types of measurement studies. We make the following contributions: First, we conduct a survey of all research published since 2015 in the premier security and Internet measurement venues to identify and verify the repeatability of crawling methodologies deployed for different problem domains and publication venues. Next, we conduct a qualitative evaluation of a subset of all crawling tools identified in our survey. This evaluation allows us to draw conclusions about the suitability of each tool for specific types of data gathering. Finally, we present a methodology and a measurement framework to empirically highlight the differences between crawlers and how the choice of crawler can impact our understanding of the web.
Statistical Modeling of Harassment against Reddit Moderators
Authors: Hind A. Al-Merekhi, Haewoon Kwak, Bernard J. Jansen
Venue: WWW
Year: 2020

Despite the dedication that some volunteer moderators of online communities display when performing their moderation duties, they become targets of hate and harassment by other users. To understand what causes the change in moderator role from heroes to victims, we analyze the responses of 1,818 moderators on Reddit to an online survey about moderation practices and harassment. We built a statistical model and found 6 significant independent variables that affect harassment on moderators, such as the knowledge of community norms, which increases harassment on moderators the most. Our findings imply that vulnerable moderators in toxic communities need countermeasures against harassment.
Are These Comments Triggering? Predicting Triggers of Toxicity in Online Discussions
Authors: Hind A. Al-Merekhi, Haewoon Kwak, Joni Salminen, Bernard J. Jansen
Venue: WWW
Year: 2020

Understanding the causes or triggers of toxicity adds a new dimension to the prevention of toxic behavior in online discussions. In this research, we define toxicity triggers in online discussions as a non-toxic comment that lead to toxic replies. Then, we build a neural network-based prediction model for toxicity trigger. The prediction model incorporates text-based features and derived features from previous studies that pertain to shifts in sentiment, topic flow, and discussion context. Our findings show that triggers of toxicity contain identifiable features and that incorporating shift features with the discussion context can be detected with a ROC-AUC score of 0.87. We discuss implications for online communities and also possible further analysis of online toxicity and its root causes.
Enhancing Web App Execution with Automated Reengineering
Authors: Kijin An
Venue: WWW
Year: 2020

The execution of modern web applications is affected by distribution, mobility, and heterogeneity. The design-time assumptions of web applications rarely correspond to their runtime conditions. As a result, the efficiency, performance, and reliability of web app execution can suffer. This dissertation research addresses this problem by introducing novel automated reengineering techniques. In particular, we put forward a series of novel domain-specific refactorings, semantics-preserving program transformations, that behind-the-scenes improve the efficiency, performance, and reliability of extant web applications. To that end, we apply state-of-the-art program analysis and transformation techniques, extending and adapting them for the domain of web applications. Our ultimate objective is to enhance the execution of real-world web apps to meet the requirements of modern users, so the web can remain the most versatile computing application deployment and delivery infrastructure. The intermediate results of this dissertation research have been accepted for publication as a full research paper in the proceedings of the Web Conference 2020 [3].
Client Insourcing: Bringing Ops In-House for Seamless Re-engineering of Full-Stack JavaScript Applications
Authors: Kijin An, Eli Tilevich
Venue: WWW
Year: 2020

Modern web applications are distributed across a browser-based client and a cloud-based server. Distribution provides access to remote resources, accessed over the web and shared by clients. Much of the complexity of inspecting and evolving web applications lies in their distributed nature. Also, the majority of mature program analysis and transformation tools works only with centralized software. Inspired by business process re-engineering, in which remote operations can be insourced back in house to restructure and outsource anew, we bring an analogous approach to the re-engineering of web applications. Our target domain are full-stack JavaScript applications that implement both the client and server code in this language. Our approach is enabled by Client Insourcing, a novel automatic refactoring that creates a semantically equivalent centralized version of a distributed application. This centralized version is then inspected, modified, and redistributed to meet new requirements. After describing the design and implementation of Client Insourcing, we demonstrate its utility and value in addressing changes in security, reliability, and performance requirements. By reducing the complexity of the non-trivial program inspection and evolution tasks performed to meet these requirements, our approach can become a helpful aid in the re-engineering of web applications in this domain.
Algorithmic Effects on the Diversity of Consumption on Spotify
Authors: Ashton Anderson, Lucas Maystre, Ian Anderson, Rishabh Mehrotra, Mounia Lalmas
Venue: WWW
Year: 2020

On many online platforms, users can engage with millions of pieces of content, which they discover either organically or through algorithmically-generated recommendations. While the short-term benefits of recommender systems are well-known, their long-term impacts are less well understood. In this work, we study the user experience on Spotify, a popular music streaming service, through the lens of diversity—the coherence of the set of songs a user listens to. We use a high-fidelity embedding of millions of songs based on listening behavior on Spotify to quantify how musically diverse every user is, and find that high consumption diversity is strongly associated with important long-term user metrics, such as conversion and retention. However, we also find that algorithmically-driven listening through recommendations is associated with reduced consumption diversity. Furthermore, we observe that when users become more diverse in their listening over time, they do so by shifting away from algorithmic consumption and increasing their organic consumption. Finally, we deploy a randomized experiment and show that algorithmic recommendations are more effective for users with lower diversity. Our work illuminates a central tension in online platforms: how do we recommend content that users are likely to enjoy in the short term while simultaneously ensuring they can remain diverse in their consumption in the long term?