OpenRaiser/Intern-Atlas huggingface dataset:
Incorporating Side Information in Tensor Completion
Authors: Hemank Lamba, Vaishnavh Nagarajan, Kijung Shin, Naji Shajarisales
Venue: WWW
Year: 2016

Matrix and tensor completion techniques have proven useful in many applications such as recommender systems, image/video restoration, and web search. We explore the idea of using external information in completing missing values in tensors. In this work, we present a framework that employs side information as kernel matrices for tensor factorization. We apply our framework to problems of recommender systems and video restoration and show that our framework effectively deals with the cold-start problem.
Maximizing the Spread of Positive Influence by Deadline
Authors: Hemank Lamba, Jürgen Pfeffer
Venue: WWW
Year: 2016

Influence maximization has found applications in various fields such as sensor placement, viral marketing, controlling rumor outbreak, etc. In this paper, we propose a targeted approach to influence maximization in polarized networks i.e. networks where we already know or can predict node's opinion about a product or topic. The goal is to find a set of individuals to target, such that positive opinion about a specific topic or the product to be launched is maximized. Another key aspect that is present in most of the existing viral marketing algorithms is that they do not take into account the timeliness of the product adoption. In this paper, we present a framework where we infer the polarity, activity levels of the users, and then select seeds to launch viral marketing campaigns such that positive influence about the product is maximized by the given deadline.
SEMO: Searching Majority Opinions on Movies using SNS QA Threads
Authors: Jukyoung Lee, Yonghwa Choi, Suhkyung Kim, Seongsoon Kim, Jaewoo Kang
Venue: WWW
Year: 2016

Many people seek majority opinions by searching for question-answers that are uploaded by others or uploading their own questions on social media sites. However, people have to read through a large number of documents returned by search services to find the majority opinions. Moreover, even when users upload questions on social media sites, they cannot immediately obtain answers. To address these problems, we present Searching Majority Opinions System (SEMO), a novel majority opinion-based search system that uses QA threads uploaded on SNS and cQA websites. SEMO returns entities based on majority opinions for opinion-finding queries in real time. We also tackled a data sparsity problem using a novel query component expansion approach. To prove SEMO's usefulness in finding majority opinions, we implemented a prototype of SEMO for the movie domain. We believe that our method can cause a paradigm shift in opinion-finding query search and help people make decisions. SEMO is available at http://semo.korea.ac.kr/
When do Recommender Systems Work the Best?: The Moderating Effects of Product Attributes and Consumer Reviews on Recommender Performance
Authors: Dokyun Lee, Kartik Hosanagar
Venue: WWW
Year: 2016

We investigate the moderating effect of product attributes and consumer reviews on the efficacy of a collaborative filtering recommender system on an e-commerce site. We run a randomized field experiment on a top North American retailer's website with 184,375 users split into a recommender-treated group and a control group with 37,215 unique products in the dataset. By augmenting the dataset with Amazon Mechanical Turk tagged product attributes and consumer review data from the website, we study their moderating influence on recommenders in generating conversion. We first confirm that the use of recommenders increases the baseline conversion rate by 5.9%. We find that the recommenders act as substitutes for high average review ratings with the effect of using recommenders increasing the conversion rate as much as about 1.4 additional average star ratings. Additionally, we find that the positive impacts on conversion from recommenders are greater for hedonic products compared to utilitarian products while search-experience quality did not have any impact. We also find that the higher the price, the lower the positive impact of recommenders, while having lengthier product descriptions and higher review volumes increased the recommender's effectiveness. More findings are discussed in the Results.
Combining Word Embedding and Lexical Database for Semantic Relatedness Measurement
Authors: Yang-Yin Lee, Hao Ke, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: WWW
Year: 2016

While many traditional studies on semantic relatedness utilize the lexical databases, such as WordNet or Wikitionary, the recent word embedding learning approaches demonstrate their abilities to capture syntactic and semantic information, and outperform the lexicon-based methods. However, word senses are not disambiguated in the training phase of both Word2Vec and GloVe, two famous word embedding algorithms, and the path length between any two senses of words in lexical databases cannot reflect their true semantic relatedness. In this paper, a novel approach that linearly combines Word2Vec and GloVe with the lexical database WordNet is proposed for measuring semantic relatedness. The experiments show that the simple method outperforms the state-of-the-art model SensEmbed.
VizioMetrix: A Platform for Analyzing the Visual Information in Big Scholarly Data
Authors: Po-Shen Lee, Jevin D. West, Bill Howe
Venue: WWW
Year: 2016

We present VizioMetrix, a platform that extracts visual information from the scientific literature and makes it available for use in new information retrieval applications and for studies that look at patterns of visual information across millions of papers. New ideas are conveyed visually in the scientific literature through figures --- diagrams, photos, visualizations, tables --- but these visual elements remain ensconced in the surrounding paper and difficult to use directly to facilitate information discovery tasks or longitudinal analytics. Very few applications in information retrieval, academic search, or bibliometrics make direct use of the figures, and none attempt to recognize and exploit the type of figure, which can be used to augment interactions with a large corpus of scholarly literature.
Behavior Driven Topic Transition for Search Task Identification
Authors: Liangda Li, Hongbo Deng, Yunlong He, Anlei Dong, Yi Chang, Hongyuan Zha
Venue: WWW
Year: 2016

Search tasks in users' query sequences are dynamic and interconnected. The formulation of search tasks can be influenced by multiple latent factors such as user characteristics, product features and search interactions, which makes search task identification a challenging problem. In this paper, we propose an unsupervised approach to identify search tasks via topic membership along with topic transition probabilities, thus it becomes possible to interpret how user's search intent emerges and evolves over time. Moreover, a novel hidden semi-Markov model is introduced to model topic transitions by considering not only the semantic information of queries but also the latent search factors originated from user search behaviors. A variational inference algorithm is developed to identify remarkable search behavior patterns, typical topic transition tracks, and the topic membership of each query from query logs. The learned topic transition tracks and the inferred topic memberships enable us to identify both small search tasks, where a user searches the same topic, and big search tasks, where a user searches a series of related topics. We extensively evaluate the proposed approach and compare with several state-of-the-art search task identification methods on both synthetic and real-world query log data, and experimental results illustrate the effectiveness of our proposed model.
Remedying Web Hijacking: Notification Effectiveness and Webmaster Comprehension
Authors: Frank Li, Grant Ho, Eric Kuan, Yuan Niu, Lucas Ballard, Kurt Thomas, Elie Bursztein, Vern Paxson
Venue: WWW
Year: 2016

As miscreants routinely hijack thousands of vulnerable web servers weekly for cheap hosting and traffic acquisition, security services have turned to notifications both to alert webmasters of ongoing incidents as well as to expedite recovery. In this work we present the first large-scale measurement study on the effectiveness of combinations of browser, search, and direct webmaster notifications at reducing the duration a site remains compromised. Our study captures the life cycle of 760,935 hijacking incidents from July, 2014--June, 2015, as identified by Google Safe Browsing and Search Quality. We observe that direct communication with webmasters increases the likelihood of cleanup by over 50% and reduces infection lengths by at least 62%. Absent this open channel for communication, we find browser interstitials---while intended to alert visitors to potentially harmful content---correlate with faster remediation. As part of our study, we also explore whether webmasters exhibit the necessary technical expertise to address hijacking incidents. Based on appeal logs where webmasters alert Google that their site is no longer compromised, we find 80% of operators successfully clean up symptoms on their first appeal. However, a sizeable fraction of site owners do not address the root cause of compromise, with over 12% of sites falling victim to a new attack within 30 days. We distill these findings into a set of recommendations for improving web security and best practices for webmasters.
Hierarchy-Based Link Prediction in Knowledge Graphs
Authors: Manling Li, Yantao Jia, Yuanzhuo Wang, Jingyuan Li, Xueqi Cheng
Venue: WWW
Year: 2016

Link prediction over a knowledge graph aims to predict the missing entity h or t for a triple (h,r,t). Existing knowledge graph embedding based predictive methods represent entities and relations in knowledge graphs as elements of a vector space, and employ the structural information for link prediction. However, knowledge graphs contain many hierarchical relations, which existing methods have pay little attention to. In this paper, we propose a hierarchy-constrained locally adaptive knowledge graph embedding based link prediction method, called hTransA, by integrating hierarchical structures into the predictive work. Experiments over two benchmark data sets demonstrate the superiority of hTransA.
In a World That Counts: Clustering and Detecting Fake Social Engagement at Scale
Authors: Yixuan Li, Oscar Martinez, Xing Chen, Yi Li, John E. Hopcroft
Venue: WWW
Year: 2016

How can web services that depend on user generated content discern fake social engagement activities by spammers from legitimate ones? In this paper, we focus on the social site of YouTube and the problem of identifying bad actors posting inorganic contents and inflating the count of social engagement metrics. We propose an effective method, Leas (Local Expansion at Scale), and show how the fake engagement activities on YouTube can be tracked over time by analyzing the temporal graph based on the engagement behavior pattern between users and YouTube videos. With the domain knowledge of spammer seeds, we formulate and tackle the problem in a semi-supervised manner --- with the objective of searching for individuals that have similar pattern of behavior as the known seeds --- based on a graph diffusion process via local spectral subspace. We offer a fast, scalable MapReduce deployment adapted from the localized spectral clustering algorithm. We demonstrate the effectiveness of our deployment at Google by achieving a manual review accuracy of 98% on YouTube Comments graph in practice. Comparing with the state-of-the-art algorithm CopyCatch, Leas achieves 10 times faster running time on average. Leas is now actively in use at Google, searching for daily deceptive practices on YouTube's engagement graph spanning over a billion users.
What Links Alice and Bob?: Matching and Ranking Semantic Patterns in Heterogeneous Networks
Authors: Jiongqian Liang, Deepak Ajwani, Patrick K. Nicholson, Alessandra Sala, Srinivasan Parthasarathy
Venue: WWW
Year: 2016

An increasing number of applications are modeled and analyzed in network form, where nodes represent entities of interest and edges represent interactions or relationships between entities. Commonly, such relationship analysis tools assume homogeneity in both node type and edge type. Recent research has sought to redress the assumption of homogeneity and focused on mining heterogeneous information networks (HINs) where both nodes and edges can be of different types. Building on such efforts, in this work we articulate a novel approach for mining relationships across entities in such networks while accounting for user preference (prioritization) over relationship type and interestingness metric. We formalize the problem as a top-$k$ lightest paths problem, contextualized in a real-world communication network, and seek to find the $k$ most interesting path instances matching the preferred relationship type. Our solution, PROphetic HEuristic Algorithm for Path Searching (PRO-HEAPS), leverages a combination of novel graph preprocessing techniques, well designed heuristics and the venerable A* search algorithm. We run our algorithm on real-world large-scale graphs and show that our algorithm significantly outperforms a wide variety of baseline approaches with speedups as large as 100X. We also conduct a case study and demonstrate valuable applications of our algorithm.
Cracking Classifiers for Evasion: A Case Study on the Google's Phishing Pages Filter
Authors: Bin Liang, Miaoqiang Su, Wei You, Wenchang Shi, Gang Yang
Venue: WWW
Year: 2016

Various classifiers based on the machine learning techniques have been widely used in security applications. Meanwhile, they also became an attack target of adversaries. Many existing studies have paid much attention to the evasion attacks on the online classifiers and discussed defensive methods. However, the security of the classifiers deployed in the client environment has not got the attention it deserves. Besides, earlier studies only concentrated on the experimental classifiers developed for research purposes only. The security of widely-used commercial classifiers still remains unclear. In this paper, we use the Google's phishing pages filter (GPPF), a classifier deployed in the Chrome browser which owns over one billion users, as a case to investigate the security challenges for the client-side classifiers. We present a new attack methodology targeting on client-side classifiers, called classifiers cracking. With the methodology, we successfully cracked the classification model of GPPF and extracted sufficient knowledge can be exploited for evasion attacks, including the classification algorithm, scoring rules and features, etc. Most importantly, we completely reverse engineered 84.8% scoring rules, covering most of high-weighted rules. Based on the cracked information, we performed two kinds of evasion attacks to GPPF, using 100 real phishing pages for the evaluation purpose. The experiments show that all the phishing pages (100%) can be easily manipulated to bypass the detection of GPPF. Our study demonstrates that the existing client-side classifiers are very vulnerable to classifiers cracking attacks.
Characterizing Long-tail SEO Spam on Cloud Web Hosting Services
Authors: Xiaojing Liao, Chang Liu, Damon McCoy, Elaine Shi, Shuang Hao, Raheem A. Beyah
Venue: WWW
Year: 2016

The popularity of long-tail search engine optimization (SEO) brings with new security challenges: incidents of long-tail keyword poisoning to lower competition and increase revenue have been reported. The emergence of cloud web hosting services provides a new and effective platform for long-tail SEO spam attacks. There is growing evidence that large-scale long-tail SEO campaigns are being carried out on cloud hosting platforms because they offer low-cost, high-speed hosting services. In this paper, we take the first step toward understanding how long-tail SEO spam is implemented on cloud hosting platforms. After identifying 3,186 cloud directories and 318,470 doorway pages on the leading cloud platforms for long-tail SEO spam, we characterize their abusive behavior. One highlight of our findings is the effectiveness of the cloud-based long-tail SEO spam, with 6% of the doorway pages successfully appearing in the top 10 search results of the poisoned long-tail keywords.
Effective Construction of Relative Lempel-Ziv Dictionaries
Authors: Kewen Liao, Matthias Petri, Alistair Moffat, Anthony Wirth
Venue: WWW
Year: 2016

Web crawls generate vast quantities of text, retained and archived by the search services that initiate them. To store such data and to allow storage costs to be minimized, while still providing some level of random access to the compressed data, efficient and effective compression techniques are critical. The Relative Lempel Ziv (RLZ) scheme provides fast decompression and retrieval of documents from within large compressed collections, and even with a relatively small RAM-resident dictionary, is competitive relative to adaptive compression schemes. To date, the dictionaries required by RLZ compression have been formed from concatenations of substrings regularly sampled from the underlying document collection, then pruned in a manner that seeks to retain only the high-use sections. In this work, we develop new dictionary design heuristics, based on effective construction, rather than on pruning; we identify dictionary construction as a (string) covering problem. To avoid the complications of string covering algorithms on large collections, we focus on k-mers and their frequencies. First, with a reservoir sampler, we efficiently identify the most common k-mers. Then, since a collection typically comprises regions of local similarity, we select in each "epoch" a segment whose k-mers together achieve, locally, the highest coverage score. The dictionary is formed from the concatenation of these epoch-derived segments. Our selection process is inspired by the greedy approach to the Set Cover problem.
Power of Human Curation in Recommendation System
Authors: Yuchen Liu, Dmitry Chechik, Junghoo Cho
Venue: WWW
Year: 2016

This paper introduces human curation signals and demonstrates incorporating human curation signals improves the relevance of state-of-art recommendation system models by up to 30% by experiments on a large-scale Pinterest dataset.
Workload Study of a Media-Rich Educational Web Site
Authors: Yang Liu, Carey Williamson
Venue: WWW
Year: 2016

Modern educational Web sites often feature a rich assortment of linked media content. In this paper, we present a workload study of such an educational Web site hosted at the University of Calgary. Three main insights emerge from our study. First, educational Web sites can generate large volumes of Internet traffic, even when the number of users is limited. Second, network usage is highly influenced by course-related events, such as midterms and finals. Third, the approach used by the site for displaying videos can have adverse impacts on user experience and network traffic. We demonstrate these effects with active measurement of different Web browser and video player implementations.
Open Data Business Licence Usage to Improve Local Search Engine Content Ranking
Authors: Robert F. Lytle
Venue: WWW
Year: 2016

This paper addresses the use of Open Data business licence records in the course of local web search. It assesses the feasibility of increasing the search ranking of authorised service providers and rank reduction or removal of providers with an invalid licence status. A case study was conducted to identify usable results returned with a local search across multiple providers. Result records were then analysed against an applicable Open Data set to determine the usability of the search results for end-users seeking service. A model for adjusting search result ranking is proposed for further analysis. Finally, findings based on the analysis lead to additional proposed research efforts in this space.
Observlets: Empowering Analytical Observations on Web Observatory
Authors: Aastha Madaan, Thanassis Tiropanis, Srinath Srinivasa, Wendy Hall
Venue: WWW
Year: 2016

The Web observatory is proposed as a global catalogue for sharing data-sets and analytic applications to support researchers from a variety of disciplines for analysing huge amount of research data for Web Science research. However, often these users fail to understand various transformations and consequences of complex data processing involved in a data analytic application. Therefore, there is a need to enable these users develop and re-use analytic applications on web observatory. In this study, we propose formal design patterns called "Observlets" for analytic applications to "observe" various web phenomena. The observlets provide abstract definitions for intermediate analysis required for a data analytic application. The users can share observlets across distributed web observatory nodes. The observlets are aimed to enhance end-users' awareness and engagement on web observatory and support programmers for innovating various data analytic applications.
A Demonstration of the Solid Platform for Social Web Applications
Authors: Essam Mansour, Andrei Vlad Sambra, Sandro Hawke, Maged Zereba, Sarven Capadisli, Abdurrahman Ghanem, Ashraf Aboulnaga, Tim Berners-Lee
Venue: WWW
Year: 2016

Solid is a decentralized platform for social Web applications. In the Solid platform, users' data is managed independently of the applications that create and consume this data. Each user stores their data in a Web-accessible personal online datastore (or pod). Each user can have one or more pods from different pod providers, and can easily switch between providers. Applications access data in users' pods using well defined protocols, and a decentralized authentication and access control mechanism guarantees the privacy of the data. In this decentralized architecture, applications can operate on users' data wherever it is stored. Users control access to their data, and have the option to switch between applications at any time. We will demonstrate the utility of Solid and how it is experienced from the point of view of end users and application developers. For this, we will use a set of Solid servers and multiple Web applications that use these servers. We believe that experience with a concrete platform such as Solid is highly valuable in truly appreciating the power of a decentralized social Web.
Understanding the Detection of View Fraud in Video Content Portals
Authors: Miriam Marciel, Rubén Cuevas, Albert Banchs, Roberto Gonzalez, Stefano Traverso, Mohamed Ahmed, Arturo Azcorra
Venue: WWW
Year: 2016

While substantial effort has been devoted to understand fraudulent activity in traditional online advertising (search and banner), more recent forms such as video ads have received little attention. The understanding and identification of fraudulent activity (i.e., fake views) in video ads for advertisers, is complicated as they rely exclusively on the detection mechanisms deployed by video hosting portals. In this context, the development of independent tools able to monitor and audit the fidelity of these systems are missing today and needed by both industry and regulators.
Web Communities in Big Data Era. Editorial
Authors: Pierre Maret, Rajendra Akerkar, Laurent Vercouter
Venue: WWW
Year: 2016

Web-based community is a self-defined web-based network of interactive communication organized around a shared interest or purpose. It provides the means of interactions among people in which they create, share, and exchange information and ideas in virtual space and networks. Working with big data often requires querying and reasoning that data to isolate information of interest and manipulate it in various ways. This editorial paper explores recent big data research topics -- stream querying and reasoning -- over data from web based communities. It combines aspects from some well-studied research domains, such as, social network analysis, graph databases, and data streams. We provide a brief synopsis of some research issues in supporting reasoning and querying tasks. This editorial also presents the WI&C-16 workshop's goal and programme.