OpenRaiser/Intern-Atlas huggingface dataset:
Extracting Knowledge from Web Text with Monte Carlo Tree Search
Authors: Guiliang Liu, Xu Li, Jiakang Wang, Mingming Sun, Ping Li
Venue: WWW
Year: 2020

To extract knowledge from general web text, it requires to build a domain-independent extractor that scales to the entire web corpus. This task is known as Open Information Extraction (OIE). This paper proposes to apply Monte-Carlo Tree Search (MCTS) to accomplish OIE. To achieve this goal, we define a Markov Decision Process for OIE and build a simulator to learn the reward signals, which provides a complete reinforcement learning framework for MCTS. Using this framework, MCTS explores candidate words (and symbols) under the guidance of a pre-trained Sequence-to-Sequence (Seq2Seq) predictor and generates abundant exploration samples during training. We apply the exploration samples to update the reward simulator and the predictor, based on which we implement another MCTS to search the optimal predictions during inference. Empirical evaluation demonstrates that the MCTS inference substantially improves the accuracy of prediction (more than 10%) and achieves a leading performance over other state-of-the-art comparison models.
Study on Price Consistency regarding Pack Size via Product Variant Retrieval and Pack Size Extraction
Authors: Yang Liu, Chu Wang, Zuohua Zhang, Yongning Wu
Venue: WWW
Year: 2020

Price perception is extremely important for retailers. Customers assess the price of a product not only from the product’s own price history, but also from the prices of the product’s close variants. One particular kind of variant considered is the same product sold in different sizes, where a reduced unit price is generally expected for the ones sold in large quantities. Such price consistency between product variants could be important for customer experience, yet very challenging for retailers which carry millions of products with possibly missing and noisy catalog information. We propose a framework to measure pricing consistency between product size variants by retrieving product variants via search and extracting product size information with natural language processing methods. We evaluate three monotonic regression models that regularize the unit price instead of simple heuristics. To quantify the extent of price inconsistency, we define new metrics and demonstrate that one method can lower the inconsistency measure by up to 45% on the experiment sample set.
Matching Ukrainian Wikipedia Red Links with English Wikipedia's Articles
Authors: Kateryna Liubonko, Diego Sáez-Trumper
Venue: WWW
Year: 2020

This work tackles the problem of matching Wikipedia red links with existing articles. Links in Wikipedia pages are considered red when lead to nonexistent articles. In other Wikipedia editions could exist articles that correspond to such red links. In our work, we propose a way to match red links in one Wikipedia edition to existent pages in another edition. We define the task as a Named Entity Linking problem because red link titles are mostly named entities. We solve it in a context of Ukrainian red links and English existing pages. We created a dataset of 3171 most frequent Ukrainian red links and a dataset of almost 3 million pairs of red links and the most probable candidates for the correspondent pages in English Wikipedia. This dataset is publicly released1. In this work we define conceptual characteristics of the data — word and graph properties — based on its analysis and exploit these properties in entity resolution. BabelNet knowledge base was applied to this task and was regarded as a baseline for our approach (F1 score = 32 %). To improve the result we introduced several similarity metrics based on mentioned red links characteristics. Combined in a linear model they resulted in F1 score = 85 %. To the best of our knowledge, we are the first to state the problem and propose a solution for red links in Ukrainian Wikipedia edition.
Edge formation in Social Networks to Nurture Content Creators
Authors: Chun Lo, Emilie de Longueau, Ankan Saha, Shaunak Chatterjee
Venue: WWW
Year: 2020

Social networks act as major content marketplaces where creators and consumers come together to share and consume various kinds of content. Content ranking applications (e.g., newsfeed, moments, notifications) and edge recommendation products (e.g., connect to members, follow celebrities or groups or hashtags) on such platforms aim at improving the consumer experience. In this work, we focus on the creator experience and specifically on improving edge recommendations to better serve creators in such ecosystems. The audience and reach of creators – individuals, celebrities, publishers and companies – are critically shaped by these edge recommendation products. Hence, incorporating creator utility in such recommendations can have a material impact on their success, and in turn, on the marketplace. In this paper, we (i) propose a general framework to incorporate creator utility in edge recommendations, (ii) devise a specific method to estimate edge-level creator utilities for currently unformed edges, (iii) outline the challenges of measurement and propose a practical experiment design, and finally (iv) discuss the implementation of our proposal at scale on LinkedIn, a professional network with 645M+ members, and report our findings.
On the Robustness of Cascade Diffusion under Node Attacks
Authors: Alvis Logins, Yuchen Li, Panagiotis Karras
Venue: WWW
Year: 2020

How can we assess a network’s ability to maintain its functionality under attacks? Network robustness has been studied extensively in the case of deterministic networks. However, applications such as online information diffusion and the behavior of networked public raise a question of robustness in probabilistic networks. We propose three novel robustness measures for networks hosting a diffusion under the Independent Cascade (IC) model, susceptible to node attacks. The outcome of such a process depends on the selection of its initiators, or seeds, by the seeder, as well as on two factors outside the seeder’s discretion: the attack strategy and the probabilistic diffusion outcome. We consider three levels of seeder awareness regarding these two uncontrolled factors, and evaluate the network’s viability aggregated over all possible extents of node attacks. We introduce novel algorithms from building blocks found in previous works to evaluate the proposed measures. A thorough experimental study with synthetic and real, scale-free and homogeneous networks establishes that these algorithms are effective and efficient, while the proposed measures highlight differences among networks in terms of robustness and the surprise they furnish when attacked. Last, we devise a new measure of diffusion entropy that can inform the design of probabilistically robust networks.
Latent Linear Critiquing for Conversational Recommender Systems
Authors: Kai Luo, Scott Sanner, Ga Wu, Hanze Li, Hojin Yang
Venue: WWW
Year: 2020

Critiquing is a method for conversational recommendation that iteratively adapts recommendations in response to user preference feedback. In this setting, a user is iteratively provided with an item recommendation and attribute description for that item; a user may either accept the recommendation, or critique the attributes in the item description to generate a new recommendation. Historical critiquing methods were largely based on explicit constraint- and utility-based methods for modifying recommendations w.r.t. critiqued item attributes. In this paper, we revisit the critiquing approach in the era of recommendation methods based on latent embeddings with subjective item descriptions (i.e., keyphrases from user reviews). Two critical research problems arise: (1) how to co-embed keyphrase critiques with user preference embeddings to update recommendations, and (2) how to modulate the strength of multi-step critiquing feedback, where critiques are not necessarily independent, nor of equal importance. To address (1), we build on an existing state-of-the-art linear embedding recommendation algorithm to align review-based keyphrase attributes with user preference embeddings. To address (2), we exploit the linear structure of the embeddings and recommendation prediction to formulate a linear program (LP) based optimization problem to determine optimal weights for incorporating critique feedback. We evaluate the proposed framework on two recommendation datasets containing user reviews with simulated users. Empirical results compared to a standard approach of averaging critique feedback show that our approach reduces the number of interactions required to find a satisfactory item and increases the overall success rate.
HQADeepHelper: A Deep Learning System for Healthcare Question Answering
Authors: Feng Luo, Xiaoli Wang, Qingfeng Wu, Jiayin Liang, Xueliang Qiu, Zhifeng Bao
Venue: WWW
Year: 2020

It is challenging to generate high quality answers for healthcare queries in online platforms. Recent studies proposed deep models for healthcare question answering (HQA) tasks. However, these models have not been thoroughly compared, and they were only tested on self-created datasets. This paper demonstrates a novel system, denoted by HQADeepHelper, to facilitate the learning and practicing of deep models for HQA. We have implemented a wide spectrum of state-of-the-art deep models for HQA retrieval. Users can upload self-collected HQA datasets and knowledge graphs, and do simple configurations by selecting datasets, knowledge graphs, neural network models, and evaluation metrics. Based on user’s configuration specified, the system can automatically train and test the model, conduct extensive experimental evaluation of the models selected, and report comprehensive findings. The reports provide new insights about the strengths and weaknesses of deep models that can guide practitioners to select appropriate models for various scenarios. Moreover, users can download the datasets, knowledge graphs, experimental reports and source codes of neural network models for their own practice and evaluations further.
Democratizing Content Creation and Dissemination through AI Technology
Authors: Wei-Ying Ma
Venue: WWW
Year: 2020

With the rise of mobile video, user-generated content, and social networks, there is a massive opportunity for disruptive innovations in the media and content industry. It is now a fast-changing landscape with rapid advances in AI-powered content creation, dissemination and interaction technologies. I believe the current trends are leading us towards a world where everyone is equally empowered to produce high-quality content in video, music, augmented reality or more – and to share their information, knowledge, and stories with a large global audience. This new AI- powered content platform can further lead to innovations in advertising, e-commerce, online education, and productivity. I will share the current research efforts at ByteDance connected to this emerging new platform through products such as Douyin and TikTok, and discuss the challenges and the direction of our future research.
AutoMAP: Diagnose Your Microservice-based Web Applications Automatically
Authors: Meng Ma, Jingmin Xu, Yuan Wang, Pengfei Chen, Zonghua Zhang, Ping Wang
Venue: WWW
Year: 2020

The high complexity and dynamics of the microservice architecture make its application diagnosis extremely challenging. Static troubleshooting approaches may fail to obtain reliable model applies for frequently changing situations. Even if we know the calling dependency of services, we lack a more dynamic diagnosis mechanism due to the existence of indirect fault propagation. Besides, algorithm based on single metric usually fail to identify the root cause of anomaly, as single type of metric is not enough to characterize the anomalies occur in diverse services. In view of this, we design a novel tool, named AutoMAP, which enables dynamic generation of service correlations and automated diagnosis leveraging multiple types of metrics. In AutoMAP, we propose the concept of anomaly behavior graph to describe the correlations between services associated with different types of metrics. Two binary operations, as well as a similarity function on behavior graph are defined to help AutoMAP choose appropriate diagnosis metric in any particular scenario. Following the behavior graph, we design a heuristic investigation algorithm by using forward, self, and backward random walk, with an objective to identify the root cause services. To demonstrate the strengths of AutoMAP, we develop a prototype and evaluate it in both simulated environment and real-work enterprise cloud system. Experimental results clearly indicate that AutoMAP achieves over 90% precision, which significantly outperforms other selected baseline methods. AutoMAP can be quickly deployed in a variety of microservice-based systems without any system knowledge. It also supports introduction of various expert knowledge to improve accuracy.
CrEOS: Identifying Critical Events in Online Sessions
Authors: Meghanath Macha, Shankar Venkitachalam, Deepak Pai
Venue: WWW
Year: 2020

Analyzing a consumer’s experience in an online session is a fundamental task of a marketer to enrich, personalize and enhance their future interactions with a firm. Click (web) or tap (mobile) event streams efficiently capture a consumer’s experience in an online session. In this work, we propose CrEOS contributing to the extant literature in the deployment of deep learning techniques to model online consumer behaviour. CrEOS models consumer conversion behaviour using a multi-variable LSTM, enriching the consumer event stream with a series of contextual features. In addition to the predictions, CrEOS identifies events that either hamper or help a conversion at three levels - consumer, consumer segment and transitions in marketing funnel. We validate CrEOS on click-stream data of a large US based e-commerce firm, compare it with single variable LSTM and discuss the insights derived from the critical event identification considering a series of marketing tasks.
A Web Tool to Map Research Impacts Via Altmetrics
Authors: Luís Fernando Monsores Passos Maia, Marcia Lenzi, Elaine Rabello, Jonice Oliveira
Venue: WWW
Year: 2020

Currently, there is a big concern on governments and research institutes on assessing the population awareness about the scientific innovations, such as new food production technologies and the development of drugs against emerging and neglected diseases. There is an unmet demand for new ways to show the impact of scientific research on social media (population’s main communication vehicles) and assess the social outreach of the scientific output. This article presents a novel web tool to map research impacts via Altmetrics, wich are alternative metrics based on the exchange of scientific knowledge on social media and online environments.
Herding a Deluge of Good Samaritans: How GitHub Projects Respond to Increased Attention
Authors: Danaja Maldeniya, Ceren Budak, Lionel P. Robert Jr., Daniel M. Romero
Venue: WWW
Year: 2020

Collaborative crowdsourcing is a well-established model of work, especially in the case of open source software development. The structure and operation of these virtual and loosely-knit teams differ from traditional organizations. As such, little is known about how their behavior may change in response to an increase in external attention. To understand these dynamics, we analyze millions of actions of thousands of contributors in over 1100 open source software projects that topped the GitHub Trending Projects page and thus experienced a large increase in attention, in comparison to a control group of projects identified through propensity score matching. In carrying out our research, we use the lens of organizational change, which considers the challenges teams face during rapid growth and how they adapt their work routines, organizational structure, and management style. We show that trending results in an explosive growth in the effective team size. However, most newcomers make only shallow and transient contributions. In response, the original team transitions towards administrative roles, responding to requests and reviewing work done by newcomers. Projects evolve towards a more distributed coordination model with newcomers becoming more central, albeit in limited ways. Additionally, teams become more modular with subgroups specializing in different aspects of the project. We discuss broader implications for collaborative crowdsourcing teams that face attention shocks.
PARS: Peers-aware Recommender System
Authors: Huiqiang Mao, Yanzhi Li, Chenliang Li, Di Chen, Xiaoqing Wang, Yuming Deng
Venue: WWW
Year: 2020

The presence or absence of one item in a recommendation list will affect the demand for other items because customers are often willing to switch to other items if their most preferred items are not available. The cross-item influence, called “peers effect”, has been largely ignored in the literature. In this paper, we develop a peers-aware recommender system, named PARS. We apply a ranking-based choice model to capture the cross-item influence and solve the resultant MaxMin problem with a decomposition algorithm. The MaxMin model solves for the recommendation decision in the meanwhile of estimating users’ preferences towards the items, which yields high-quality recommendations robust to input data variation. Experimental results illustrate that PARS outperforms a few frequently used methods in practice. An online evaluation with a flash sales scenario at Taobao also shows that PARS delivers significant improvements in terms of both conversion rates and user value.
Analyzing the Use of Audio Messages in WhatsApp Groups
Authors: Alexandre Maros, Jussara M. Almeida, Fabrício Benevenuto, Marisa Vasconcelos
Venue: WWW
Year: 2020

WhatsApp is a free messaging app with more than one billion active monthly users which has become one of the main communication platforms in many countries, including Saudi Arabia, Germany, and Brazil. In addition to allowing the direct exchange of messages among pairs of users, the app also enables group conversations, where multiple people can interact with one another. A number of recent studies have shown that WhatsApp groups play an important role as an information dissemination platform, especially during important social mobilization events. In this paper, we build upon those prior efforts by taking a first look into the use of audio messages in WhatsApp groups, a type of content that is becoming increasingly important in the platform. We present a methodology to analyze audio messages shared in WhatsApp groups, characterizing content properties (e.g, topics and language characteristics), their propagation dynamics and the impact of different types of audios (e.g., speech versus music) on such dynamics.
Improved Advertisement Targeting via Fine-grained Location Prediction using Twitter
Authors: Shogo Matsuno, Sakae Mizuki, Takeshi Sakaki
Venue: WWW
Year: 2020

With the growing demand for social network service advertisements, more accurate targeting methods are required. Therefore, the authors of this study try to predict the location of Twitter users in a more fine-grained manner with regard to area marketing. Specifically, the visit probability to each segment (e.g., prefecture and city) is predicted by a label propagation algorithm, and the user’s location information is obtained from a geo-tagged tweet and an user profile as a label. The proposed method predicts which Twitter users may visit the corresponding area with improved granularity, to the level of an oaza or a block. As a verification experiment, we construct a system that outputs a list of users who are likely to visit the location when an oaza name is entered based on the proposed method, and we also try to predict the possibility of users visiting each segment. The results show that the average accuracy is 73% at the prefecture level, 42% at the city level, and 25% at the oaza level. In addition, the prediction accuracy in Tokyo alone is 31% at the oaza level. These results indicate the effectiveness of the proposed method.
Predicting Drug Demand with Wikipedia Views: Evidence from Darknet Markets
Authors: Sam Miller, Abeer ElBahrawy, Martin Dittus, Mark Graham, Joss Wright
Venue: WWW
Year: 2020

Rapid changes in illicit drug demand, such as the Fentanyl epidemic, are a major public health issue. Policymakers currently rely on annual surveys to monitor public consumption, which are arguably too infrequent to detect rapid shifts in drug use. We present a novel method to predict drug use based on high-frequency sales data from darknet markets. We show that models based on historic trades alone cannot accurately predict drug demand. However, augmenting these models with data on Wikipedia page views for each drug greatly improves predictive accuracy, particularly for less popular drugs, suggesting such models may be particularly useful for detecting newly emerging substances. These results hold out-of-sample at high time frequency, across a range of drugs and countries. Therefore Wikipedia data may enable us to build a high-frequency measure of drug demand, which could help policymakers respond more quickly to future drug crises.
CityDNA Dynamics: A Model for Smart City Maturity and Performance Benchmarking
Authors: Vaia Moustaka, Antonis Maitis, Athena Vakali, Leonidas G. Anthopoulos
Venue: WWW
Year: 2020

Smart cities have emerged significantly since their initial appearance in 1990s, more and more cities around the world are striving to gain intelligence and in this regard the need for standardization and performance measurement grows. Given the current challenges in the field of smart cities, this work revisits the proposed “cityDNA” framework which has been designed to detect the interrelations between smart city dimensions and form city profile. This work contribute to further enable benchmarking and measuring the maturity of smart cities through the exploitation of international standards and civil engagement. The proposed framework along with the design of a web application will handle urban data effectively. “cityDNA” that will be advanced to receive and utilize urban data and visualize cities’ health and maturity metrics is presented, while potential constraints on its implementation are highlighted.
Visual Concept Naming: Discovering Well-Recognized Textual Expressions of Visual Concepts
Authors: Masayasu Muraoka, Tetsuya Nasukawa, Rudy Raymond, Bishwaranjan Bhattacharjee
Venue: WWW
Year: 2020

We propose a task called Visual Concept Naming to associate visual concepts with the corresponding textual expressions, i.e., names of visual concepts found in real-world multimodal data. To tackle the task, we create a dataset consisting of 3.4 million tweets in total in three languages. We also propose a method for extracting candidate names of visual concepts and validating them by exploiting Web-based knowledge obtained through image search. To demonstrate the capability of our method, we conduct an experiment with the dataset we create and evaluate names obtained by our method through crowdsourcing, where we establish an evaluation method to verify the names. The experimental results indicate that the proposed method can identify a wide variety of names of visual concepts. The names we obtained also show interesting insights regarding languages and countries where the languages are used.1