OpenRaiser/Intern-Atlas huggingface dataset:
Weighted Micro-Clustering: Application to Community Detection in Large-Scale Co-Purchasing Networks with User Attributes
Authors: Tomoya Yamazaki, Nobuyuki Shimizu, Hayato Kobayashi, Satoshi Yamauchi
Venue: WWW
Year: 2016

We propose a simple and scalable method for soft community detection that makes use of both graph structures and vertex attributes. Our method is based on micro-clustering, which is a scalable and efficient clique-based method for detecting overlapping communities in unweighted graphs. We extend this method to graphs with vertex attributes so that we can make use of information supplied by vertex attributes. Our method still requires the same time complexity as micro-clustering. We confirm the validity and efficiency of our method by applying it to a large-scale co-purchasing network of real online auction data.
Socialized Language Model Smoothing via Bi-directional Influence Propagation on Social Networks
Authors: Rui Yan, Cheng-Te Li, Hsun-Ping Hsieh, Po Hu, Xiaohua Hu, Tingting He
Venue: WWW
Year: 2016

In recent years, online social networks are among the most popular websites with high PV (Page View) all over the world, as they have renewed the way for information discovery and distribution. Millions of users have registered on these websites and hence generate formidable amount of user-generated contents every day. The social networks become "giants", likely eligible to carry on any research tasks. However, we have pointed out that these giants still suffer from their "Achilles Heel", i.e., extreme sparsity. Compared with the extremely large data over the whole collection, individual posting documents such as microblogs seem to be too sparse to make a difference under various research scenarios, while actually these postings are different. In this paper we propose to tackle the Achilles Heel of social networks by smoothing the language model via influence propagation. To further our previously proposed work to tackle the sparsity issue, we extend the socialized language model smoothing with bi-directional influence learned from propagation. Intuitively, it is insufficient not to distinguish the influence propagated between information source and target without directions. Hence, we formulate a bi-directional socialized factor graph model, which utilizes both the textual correlations between document pairs and the socialized augmentation networks behind the documents, such as user relationships and social interactions. These factors are modeled as attributes and dependencies among documents and their corresponding users, and then are distinguished on the direction level. We propose an effective learning algorithm to learn the proposed factor graph model with directions. Finally we propagate term counts to smooth documents based on the estimated influence. We run experiments on two instinctive datasets of Twitter and Weibo. The results validate the effectiveness of the proposed model. By incorporating direction information into the socialized language model smoothing, our approach obtains improvement over several alternative methods on both intrinsic and extrinsic evaluations measured in terms of perplexity, nDCG and MAP measurements.
HDPauthor: A New Hybrid Author-Topic Model using Latent Dirichlet Allocation and Hierarchical Dirichlet Processes
Authors: Ming Yang, William H. Hsu
Venue: WWW
Year: 2016

We present a new approach towards capturing topic interests corresponding to all the observed latent topics generated by an author in documents to which he or she has contributed. Topic models based on Latent Dirichlet Allocation (LDA) have been built for this purpose but are brittle as to the number of topics allowed for a collection and for each author of documents within the collection. Meanwhile, topic models based upon Hierarchical Dirichlet Processes (HDPs) allow an arbitrary number of topics to be discovered and generative distributions of interest inferred from text corpora, but this approach is not directly extensible to generative models of authors as contributors to documents with variable topical expertise. Our approach combines an existing HDP framework for learning topics from free text with latent authorship learning within a generative model using author list information. This model adds another layer into the current hierarchy of HDPs to represent topic groups shared by authors, and the document topic distribution is represented as a mixture of topic distribution of its authors. Our model automatically learns author contribution partitions for documents in addition to topics.
Mean Field Equilibria for Competitive Exploration in Resource Sharing Settings
Authors: Pu Yang, Krishnamurthy Iyer, Peter I. Frazier
Venue: WWW
Year: 2016

We consider a model of nomadic agents exploring and competing for time-varying location-specific resources, arising in crowdsourced transportation services, online communities, and in traditional location-based economic activity. This model comprises a group of agents, and a set of locations each endowed with a dynamic stochastic resource process. Each agent derives a periodic reward determined by the overall resource level at her location, and the number of other agents there. Each agent is strategic and free to move between locations, and at each time decides whether to stay at the same node or switch to another one. We study the equilibrium behavior of the agents as a function of dynamics of the stochastic resource process and the nature of the externality each agent imposes on others at the same location. In the asymptotic limit with the number of agents and locations increasing proportionally, we show that an equilibrium exists and has a threshold structure, where each agent decides to switch to a different location based only on their current location's resource level and the number of other agents at that location. This result provides insight into how system structure affects the agents' collective ability to explore their domain to find and effectively utilize resource-rich areas. It also allows assessing the impact of changing the reward structure through penalties or subsidies.
StarrySky: A Practical System to Track Millions of High-Precision Query Intents
Authors: Qi Ye, Feng Wang, Bo Li
Venue: WWW
Year: 2016

Query intent mining is a critical problem in various real-world search applications. In the past few years we have witnessed dramatic advances in the field of query intent mining area. In this paper, we present a practical system---StarrySky for identifying and inferring millions of query intents in daily sponsored search with high precision and acceptable coverage. We have already achieved great advantages by deploying this system in Sogou sponsored search engine\footnote {http://www.sogou.com}. The general architecture of StarrySky consists of three stages. First, we detect millions of fine-grained query clusters from two years of click logs which can represent different query intents. Second, we refine the qualities of query clusters with a series of well-designed operations, and call the final refined clusters as concepts. Third and foremost, we build a flexible real-time inference algorithm for assigning query intents to the detected concepts with high precision. Beyond the description of the system, we employ several experiments to evaluate its performance and flexibility. Our inference algorithm achieves up to 96% precision and 68% coverage on daily search requests. We believe StarrySky is a practical and valuable system for tracking query intents.
Browsing2purchase: Online Customer Model for Sales Forecasting in an E-Commerce Site
Authors: Jinyoung Yeo, Sungchul Kim, Eunyee Koh, Seung-won Hwang, Nedim Lipka
Venue: WWW
Year: 2016

This paper covers a sales forecasting problem on e-commerce sites. To predict product sales, we need to understand customers' browsing behavior and identify whether it is for purchase purpose or not. For this goal, we propose a new customer model, B2P, of aggregating predictive features extracted from customers' browsing history. We perform experiments on a real world e-commerce site and show that sales predictions by our model are consistently more accurate than those by existing state-of-the-art baselines.
The Communication Network Within the Crowd
Authors: Ming Yin, Mary L. Gray, Siddharth Suri, Jennifer Wortman Vaughan
Venue: WWW
Year: 2016

Since its inception, crowdsourcing has been considered a black-box approach to solicit labor from a crowd of workers. Furthermore, the "crowd" has been viewed as a group of independent workers dispersed all over the world. Recent studies based on in-person interviews have opened up the black box and shown that the crowd is not a collection of independent workers, but instead that workers communicate and collaborate with each other. Put another way, prior work has shown the existence of edges between workers. We build on and extend this discovery by mapping the entire communication network of workers on Amazon Mechanical Turk, a leading crowdsourcing platform. We execute a task in which over 10,000 workers from across the globe self-report their communication links to other workers, thereby mapping the communication network among workers. Our results suggest that while a large percentage of workers indeed appear to be independent, there is a rich network topology over the rest of the population. That is, there is a substantial communication network within the crowd. We further examine how online forum usage relates to network topology, how workers communicate with each other via this network, how workers' experience levels relate to their network positions, and how U.S. workers differ from international workers in their network characteristics. We conclude by discussing the implications of our findings for requesters, workers, and platform providers like Amazon.
Label Aggregation with Instance Grouping Model
Authors: Li&apos, ang Yin, Jianhua Han, Yong Yu
Venue: WWW
Year: 2016

Label aggregation is one of the key topics in crowdsourcing research. Most researchers make their efforts in modeling ability of users and difficulty of instances. In this paper, we consider label aggregation from the view of grouping instances. We assume instances are sampled from latent groups and they share the same true label with their corresponding groups. We construct a graphical model named InGroup(Instance Grouping model) to infer latent group assignment as well as true labels. The experimental results show the advantages of our model compared with baselines.
PGRank: Personalized Geographical Ranking for Point-of-Interest Recommendation
Authors: Haochao Ying, Liang Chen, Yuwen Xiong, Jian Wu
Venue: WWW
Year: 2016

Point-of-interest (POI) recommendation has become more and more important, since it could discover user behavior pattern and find interesting venues for them. To address this problem, we propose a rank-based method, PGRank, which integrates user geographical preference and latent preference into Bayesian personalized ranking framework. The experimental results on a real dataset show its effective.
Analytical Framework of Relations among Nations using News Articles
Authors: Takeru Yokoi, Masato Fukuchi, Michihiro Kobayakawa, Roliana Ibrahim, Ali Selamat
Venue: WWW
Year: 2016

News articles are a type of instantaneous and regional media, and provide the daily concerns of its publication area. This work proposes an analytical framework of the relations among nations using the similarities in the content of news articles published in different nations. Our key idea is that those relations exist in the news articles miss-classified by different nations from their original publication area. In order to clearly illustrate those relations, the classification results are visualized as bar graphs. We also carried out some experiments for the proposed framework using a small collection of news articles.
Tracking the Trackers
Authors: Zhonghao Yu, Sam Macbeth, Konark Modi, Josep M. Pujol
Venue: WWW
Year: 2016

Online tracking poses a serious privacy challenge that has drawn significant attention in both academia and industry. Existing approaches for preventing user tracking, based on curated blocklists, suffer from limited coverage and coarse-grained resolution for classification, rely on exceptions that impact sites' functionality and appearance, and require significant manual maintenance. In this paper we propose a novel approach, based on the concepts leveraged from $k$-Anonymity, in which users collectively identify unsafe data elements, which have the potential to identify uniquely an individual user, and remove them from requests.
Design and Implementation: the End User Development Ecosystem for Cross-platform Mobile Applications
Authors: Zhongyi Zhai, Bo Cheng, Zhaoning Wang, Xuan Liu, Meng Liu, Junliang Chen
Venue: WWW
Year: 2016

In this paper, we present an ecosystem for mobile application development by end-users. An advantage of this ecosystem is that the graphical user interface (GUI), as well as the application logic, can both be developed in a rapid and simple way. This ecosystem is mainly implemented through development and integration of two sub-systems, namely EasyApp and LSCE. EasyApp is responsible for developing the mobile app with the compatibility of multiple mobile platforms, while LSCE is in charge of creating the service process that can be invoked by mobile app directly. A case study is presented to illustrate the development process using this ecosystem.
Attention Based Recurrent Neural Networks for Online Advertising
Authors: Shuangfei Zhai, Keng-hao Chang, Ruofei Zhang, Zhongfei Zhang
Venue: WWW
Year: 2016

We investigate the use of recurrent neural networks (RNNs) in the context of online advertising, where we use RNNs to map both query and ads to real valued vectors. In addition, we propose an attention network that assigns scores to different word locations according to their intent importance. The vector output is computed by a weighted sum of the vectors at each word. We perform end-to-end training of both the RNN and attention network under the guidance of user click logs. We show that the attention network improves the quality of learned vector representations evaluated by AUC on a manually labeled dataset. Moreover, we show that keywords extracted according to the attention scores are easy to interpret and significantly outperform the state-of-the-art query intent extraction methods.
Mining User Intentions from Medical Queries: A Neural Network Based Heterogeneous Jointly Modeling Approach
Authors: Chenwei Zhang, Wei Fan, Nan Du, Philip S. Yu
Venue: WWW
Year: 2016

Text queries are naturally encoded with user intentions. An intention detection task tries to model and discover intentions that user encoded in text queries. Unlike conventional text classification tasks where the label of text is highly correlated with some topic-specific words, words from different topic categories tend to co-occur in medical related queries. Besides the existence of topic-specific words and word order, word correlations and the way words organized into sentence are crucial to intention detection tasks.
Towards Mobile Query Auto-Completion: An Efficient Mobile Application-Aware Approach
Authors: Aston Zhang, Amit Goyal, Ricardo Baeza-Yates, Yi Chang, Jiawei Han, Carl A. Gunter, Hongbo Deng
Venue: WWW
Year: 2016

We study the new mobile query auto-completion (QAC) problem to exploit mobile devices' exclusive signals, such as those related to mobile applications (apps). We propose AppAware, a novel QAC model using installed app and recently opened app signals to suggest queries for matching input prefixes on mobile devices. To overcome the challenge of noisy and voluminous signals, AppAware optimizes composite objectives with a lighter processing cost at a linear rate of convergence. We conduct experiments on a large commercial data set of mobile queries and apps. Installed app and recently opened app signals consistently and significantly boost the accuracy of various baseline QAC models on mobile devices.
Detecting Evolution of Concepts based on Cause-Effect Relationships in Online Reviews
Authors: Yating Zhang, Adam Jatowt, Katsumi Tanaka
Venue: WWW
Year: 2016

Analyzing how technology evolves is important for understanding technological progress and its impact on society. Although the concept of evolution has been explored in many domains (e.g., evolution of topics, events or terminology, evolution of species), little research has been done on automatically analyzing the evolution of products and technology in general. In this paper, we propose a novel approach for investigating the technology evolution based on collections of product reviews. We are particularly interested in understanding social impact of technology and in discovering how changes of product features influence changes in our social lives. We address this challenge by first distinguishing two kinds of product-related terms: physical product features and terms describing situations when products are used. We then detect changes in both types of terms over time by tracking fluctuations in their popularity and usage. Finally, we discover cases when changes of physical product features trigger the changes in product's use. We experimentally demonstrate the effectiveness of our approach on the Amazon Product Review Dataset that spans over 18 years.
Understanding User Economic Behavior in the City Using Large-scale Geotagged and Crowdsourced Data
Authors: Yingjie Zhang, Beibei Li, Jason I. Hong
Venue: WWW
Year: 2016

The pervasiveness of mobile technologies today have facilitated the creation of massive crowdsourced and geotagged data from individual users in real time and at different locations in the city. Such ubiquitous user-generated data allow us to infer various patterns of human behavior, which help us understand the interactions between humans and cities. In this study, we focus on understanding users economic behavior in the city by examining the economic value from crowdsourced and geotaggged data. Specifically, we extract multiple traffic and human mobility features from publicly available data sources using NLP and geo-mapping techniques, and examine the effects of both static and dynamic features on economic outcome of local businesses. Our study is instantiated on a unique dataset of restaurant bookings from OpenTable for 3,187 restaurants in New York City from November 2013 to March 2014. Our results suggest that foot traffic can increase local popularity and business performance, while mobility and traffic from automobiles may hurt local businesses, especially the well-established chains and high-end restaurants. We also find that on average one more street closure nearby leads to a 4.7% decrease in the probability of a restaurant being fully booked during the dinner peak. Our study demonstrates the potential of how to best make use of the large volumes and diverse sources of crowdsourced and geotagged user-generated data to create matrices to predict local economic demand in a manner that is fast, cheap, accurate, and meaningful.
Sampling Bias in LinkedIn: A Case Study
Authors: Shanshan Zhang, Slobodan Vucetic
Venue: WWW
Year: 2016

This paper describes a case study of sampling bias in LinkedIn, a major professional social network. The study collected a sample of 1,989 STEM students who graduated from a major public university between 2002 and 2014. Overall, 40\% of the graduates had a LinkedIn profile in summer of 2015. It was observed that LinkedIn participation significantly fluctuated among different majors, and ranged from 30\% for biochemistry majors to 51\% for information science majors. Year of graduation, gender, and grade point average surprisingly did not seem to create a large difference in LinkedIn participation. These results should be useful for design and interpretation of empirical studies which use LinkedIn data or select participants from LinkedIn social network.
CocaRank: A Collaboration Caliber-based Method for Finding Academic Rising Stars
Authors: Jun Zhang, Feng Xia, Wei Wang, Xiaomei Bai, Shuo Yu, Teshome Megersa Bekele, Zhong Peng
Venue: WWW
Year: 2016

Evaluating the scientific impact of scholars has been studied by researchers from various disciplines for a long time. However, very few efforts have been devoted to evaluate the future potential of researchers based on their performance at the initial stage of scientific careers. Academic rising stars represent junior researchers who may not be very outstanding among the peers at the initial stage of their careers, but tend to become influential scholars in the future. In this paper, we propose a novel method named CocaRank, which integrates our proposed new indicator called the collaboration caliber, the typical indicator citation counts and hybrid calculation results on heterogeneous academic networks, to find academic rising stars. In addition, we investigate the appropriate time interval for the prediction of rising stars. The experimental results on real datasets demonstrate that our method can find more top ranked rising stars with higher average citation counts than other state-of-art methods.
Economic Recommendation with Surplus Maximization
Authors: Yongfeng Zhang, Qi Zhao, Yi Zhang, Daniel Friedman, Min Zhang, Yiqun Liu, Shaoping Ma
Venue: WWW
Year: 2016

A prime function of many major World Wide Web applications is Online Service Allocation (OSA), the function of matching individual consumers with particular services/goods (which may include loans or jobs as well as products) each with its own producer. In the applications of interest, consumers are free to choose, so OSA usually takes the form of personalized recommendation or search in practice. The performance metrics of recommender and search systems currently tend to focus on just one side of the match, in some cases the consumers (e.g. satisfaction) and in other cases the producers (e.g., profit). However, a sustainable OSA platform needs benefit both consumers and producers; otherwise the neglected party eventually may stop using it. In this paper, we show how to adapt economists' traditional idea of maximizing total surplus (the sum of consumer net benefit and producer profit) to the heterogeneous world of online service allocation, in an effort to promote the web intelligence for social good in online eco-systems. Modifications of traditional personalized recommendation algorithms enable us to apply Total Surplus Maximization (TSM) to three very different types of real-world tasks -- e-commerce, P2P lending and freelancing. The results for all three tasks suggest that TSM compares very favorably to currently popular approaches, to the benefit of both producers and consumers.
Privacy Languages: Are we there yet to enable user controls?
Authors: Jun Zhao, Reuben Binns, Max Van Kleek, Nigel Shadbolt
Venue: WWW
Year: 2016

Privacy protection is one of the most prominent concerns for web users. Despite numerous efforts, users remain powerless in controlling how their personal information should be used and by whom, and find limited options to actually opt-out of dominating service providers, who often process users information with limited transparency or respect for their privacy preferences. Privacy languages are designed to express the privacy-related preferences of users and the practices of organisations, in order to establish a privacy-preserved data handling protocol. However, in practice there has been limited adoption of these languages, by either users or data controllers. This survey paper attempts to understand the strengths and limitations of existing policy languages, focusing on their capacity of enabling users to express their privacy preferences. Our preliminary results show a lack of focus on normal web users, in both language design and their tooling design. This systematic survey lays the ground work for future privacy protection designs that aim to be centred around web users for empowering their control of data privacy.