OpenRaiser/Intern-Atlas huggingface dataset:
Modeling User Activities on the Web using Paragraph Vector
Authors: Yukihiro Tagami, Hayato Kobayashi, Shingo Ono, Akira Tajima
Venue: WWW
Year: 2015

Modeling user activities on the Web is a key problem for various Web services, such as news article recommendation and ad click prediction. In this paper, we propose an approach that summarizes each sequence of user activities using the Paragraph Vector, considering users and activities as paragraphs and words, respectively. The learned user representations are used among the user-related prediction tasks in common. We evaluate this approach on two data sets based on logs from Web services of Yahoo! JAPAN. Experimental results demonstrate the effectiveness of our proposed methods.
Towards Analysing the Scope and Coverage of Educational Linked Data on the Web
Authors: Davide Taibi, Giovanni Fulantelli, Stefan Dietze, Besnik Fetahu
Venue: WWW
Year: 2015

The diversity of datasets published according to Linked Data (LD) principles has increased in the last few years and also led to the emergence of a wide range of data suitable in educational settings. However, sufficient insights into the state, coverage and scope of available educational Linked Data seem to be missing, for instance, about represented resource types or domains and topics. In this work, we analyse the scope and coverage of educational linked data on the Web, identifying the most popular resource types and topics, apparent gaps and underlining the strong correlation of resource types and topics. Our results indicate a prevalent bias to-wards data in areas such as the life sciences as well as computing-related topics.
Incorporating Social Context and Domain Knowledge for Entity Recognition
Authors: Jie Tang, Zhanpeng Fang, Jimeng Sun
Venue: WWW
Year: 2015

Recognizing entity instances in documents according to a knowledge base is a fundamental problem in many data mining applications. The problem is extremely challenging for short documents in complex domains such as social media and biomedical domains. Large concept spaces and instance ambiguity are key issues that need to be addressed. Most of the documents are created in a social context by common authors via social interactions, such as reply and citations. Such social contexts are largely ignored in the instance-recognition literature. How can users' interactions help entity instance recognition? How can the social context be modeled so as to resolve the ambiguity of different instances? In this paper, we propose the SOCINST model to formalize the problem into a probabilistic model. Given a set of short documents (e.g., tweets or paper abstracts) posted by users who may connect with each other, SOCINST can automatically construct a context of subtopics for each instance, with each subtopic representing one possible meaning of the instance. The model is also able to incorporate social relationships between users to help build social context. We further incorporate domain knowledge into the model using a Dirichlet tree distribution. We evaluate the proposed model on three different genres of datasets: ICDM'12 Contest, Weibo, and I2B2. In ICDM'12 Contest, the proposed model clearly outperforms (+21.4%; $p l 1e-5 with t-test) all the top contestants. In Weibo and I2B2, our results also show that the recognition accuracy of SOCINST is up to 5.3-26.6% better than those of several alternative methods.
Supervised Prediction of Social Network Links Using Implicit Sources of Information
Authors: Ervin Tasnádi, Gábor Berend
Venue: WWW
Year: 2015

In this paper, we introduce a supervised machine learning framework for the link prediction problem. The social network we conducted our empirical evaluation on originates from the restaurant review portal, yelp.com. The proposed framework not only uses the structure of the social network to predict non-existing edges in it, but also makes use of further graphs that were constructed based on implicit information provided in the dataset. The implicit information we relied on includes the language use of the members of the social network and their ratings with respect the businesses they reviewed. Here, we also investigate the possibility of building supervised learning models to predict social links without relying on features derived from the structure of the social network itself, but based on such implicit information alone. Our empirical results not only revealed that the features derived from different sources of implicit information can be useful on their own, but also that incorporating them in a unified framework has the potential to improve classification results, as the different sources of implicit information can provide independent and useful views about the connectedness of users.
The Case for Readability of Crisis Communications in Social Media
Authors: Irina P. Temnikova, Sarah Vieweg, Carlos Castillo
Venue: WWW
Year: 2015

The readability of text documents has been studied from a linguistic perspective long before people began to regularly communicate via Internet technologies. Typically, such studies look at books or articles containing many paragraphs and pages. However, the readability of short messages comprising a few sentences, common on today's social networking sites and microblogging services, has received less attention from researchers working on "readability". Emergency management specialists, crisis response practitioners, and scholars have long recognized that clear communication is essential during crises. To the best of our knowledge, the work we present here is the first to study the readability of crisis communications posted on Twitter-by governments, non-governmental organizations, and mainstream media. The data we analyze is comprised of hundreds of tweets posted during 15 different crises in English-speaking countries, which happened between 2012 and 2013. We describe factors which negatively affect comprehension, and consider how understanding can be improved.
Ad Recommendation Systems for Life-Time Value Optimization
Authors: Georgios Theocharous, Philip S. Thomas, Mohammad Ghavamzadeh
Venue: WWW
Year: 2015

The main objective in the ad recommendation problem is to find a strategy that, for each visitor of the website, selects the ad that has the highest probability of being clicked. This strategy could be computed using supervised learning or contextual bandit algorithms, which treat two visits of the same user as two separate independent visitors, and thus, optimize greedily for a single step into the future. Another approach would be to use reinforcement learning (RL) methods, which differentiate between two visits of the same user and two different visitors, and thus, optimizes for multiple steps into the future or the life-time value (LTV) of a customer. While greedy methods have been well-studied, the LTV approach is still in its infancy, mainly due to two fundamental challenges: how to compute a good LTV strategy and how to evaluate a solution using historical data to ensure its "safety" before deployment. In this paper, we tackle both of these challenges by proposing to use a family of off-policy evaluation techniques with statistical guarantees about the performance of a new strategy. We apply these methods to a real ad recommendation problem, both for evaluating the final performance and for optimizing the parameters of the RL algorithm. Our results show that our LTV optimization algorithm equipped with these off-policy evaluation techniques outperforms the greedy approaches. They also give fundamental insights on the difference between the click through rate (CTR) and LTV metrics for performance evaluation in the ad recommendation problem.
Assessing the Reliability of Facebook User Profiling
Authors: Thomas Theodoridis, Symeon Papadopoulos, Yiannis Kompatsiaris
Venue: WWW
Year: 2015

User profiling is an essential component of most modern online services offered upon user registration. Profiling typically involves the tracking and processing of users' online traces (e.g., page views/clicks) with the goal of inferring attributes of interest for them. The primary motivation behind profiling is to improve the effectiveness of advertising by targeting users with appropriately selected ads based on their profile attributes, e.g., interests, demographics, etc. Yet, there has been an increasing number of cases, where the advertising content users are exposed to is either irrelevant or not possible to explain based on their online activities. More disturbingly, automatically inferred user attributes are often used to make real-world decisions (e.g., job candidate selection) without the knowledge of users. We argue that many of these errors are inherent in the underlying user profiling process. To this end, we attempt to quantify the extent of such errors, focusing on a dataset of Facebook users and their likes, and conclude that profiling-based targeting is highly unreliable for a sizeable subset of users.
Using Linked Data Traversal to Label Academic Communities
Authors: Ilaria Tiddi, Mathieu d&apos, Aquin, Enrico Motta
Venue: WWW
Year: 2015

In this paper we exploit knowledge from Linked Data to ease the process of analysing scholarly data. In the last years, many techniques have been presented with the aim of analysing such data and revealing new, unrevealed knowledge, generally presented in the form of ``patterns". However, the discovered patterns often still require human interpretation to be further exploited, which might be a time and energy consuming process. Our idea is that the knowledge shared within Linked Data can actuality help and ease the process of interpreting these patterns. In practice, we show how research communities obtained through standard network analytics techniques can be made more understandable through exploiting the knowledge contained in Linked Data. To this end, we apply our system Dedalo that, by performing a simple Linked Data traversal, is able to automatically label clusters of words, corresponding to topics of the different communities.
Using WikiProjects to Measure the Health of Wikipedia
Authors: Ramine Tinati, Markus Luczak-Rösch, Nigel Shadbolt, Wendy Hall
Venue: WWW
Year: 2015

In this paper we examine WikiProjects, an emergent, community-driven feature of Wikipedia. We analysed 3.2 million Wikipedia articles associated with 618 active Wikipedia projects. The dataset contained the logs of over 115 million article revisions and 15 million talk entries both representing the activity of 15 million unique Wikipedians altogether. Our analysis revealed that per WikiProject, the number of article and talk contributions are increasing, as are the number of new Wikipedians contributing to individual WikiProjects. Based on these findings we consider how studying Wikipedia from a sub-community level may provide a means to measure Wikipedia activity.
Study on the Relationship between Profile Images and User Behaviors on Twitter
Authors: Tomu Tominaga, Yoshinori Hijikata
Venue: WWW
Year: 2015

In recent years, many researchers have studied the characteristics of Twitter, which is a microblogging service used by a large number of people worldwide. However, to the best of our knowledge, no study has yet been conducted to study the relationship between profile images and user behaviors on Twitter. We assume that the profile images and behaviors of users are influenced by their internal properties, because users consider their profile images as symbolic representations of themselves on Twitter. We empirically categorized profile images into 13 types, and investigated the relationships between each category of profile images and users' behaviors on Twitter.
A Model for Copyright and Licensing: Elsevier's Copyright Model
Authors: Anna Tordai
Venue: WWW
Year: 2015

With the rise of digital publishing and open access it has become increasingly important for publishers to store information about ownership and licensing of published works in a robust way. In a fast moving environment Elsevier, a leading science publisher, recognizes the importance of sound models underlying its data. In this paper, we describe a data model for copyright and licensing used by Elsevier for capturing elements of copyright. We explain some of the rationale behind the model and provide examples of frequently occurring cases in terms of the model.
Time-travel Translator: Automatically Contextualizing News Articles
Authors: Nam Khanh Tran, Andrea Ceroni, Nattiya Kanhabua, Claudia Niederée
Venue: WWW
Year: 2015

Fully understanding an older news article requires context knowledge from the time of article creation. Finding information about such context is a tedious and time-consuming task, which distracts the reader. Simple contextualization via Wikification is not sufficient here. The retrieved context information has to be time-aware, concise (not full Wikipages) and focused on the coherence of the article topic. In this paper, we present Contextualizer, a web-based system that acquires additional information for supporting interpretations of a news article of interest that requires a mapping, in this case, a kind of time-travel translation between present context knowledge and context knowledge at time of text creation. For a given article, the system provides a GUI that allows users to highlight their interested keywords which are then used to construct appropriate queries for retrieving contextualization candidates. Contextualizer exploits different kinds of information such as temporal similarity and textual complementarity to re-rank the candidates and presents to users in a friendly and interactive web-based interface.
Provably Fast Inference of Latent Features from Networks: with Applications to Learning Social Circles and Multilabel Classification
Authors: Charalampos E. Tsourakakis
Venue: WWW
Year: 2015

A well known phenomenon in social networks is homophily, the tendency of agents to connect with similar agents. A derivative of this phenomenon is the emergence of communities. Another phenomenon observed in numerous networks is the existence of certain agents that belong simultaneously to multiple communities. An understanding of these phenomena constitutes a central research topic of network science.
Mapping The Evolution of Scientific Community Structures in Time
Authors: Theresa Velden, Shiyan Yan, Kan Yu, Carl Lagoze
Venue: WWW
Year: 2015

The increasing online availability of scholarly corpora promises unprecedented opportunities for visualizing and studying scholarly communities. We seek to leverage this with a mixed-method approach that integrates network analysis of features of the online corpora with ethnographic studies of the communities that produce them. In our development of tools and visualizations we seek to support the going back and forth between views of community structures and the perceptions and research trajectories of individual researchers and research groups. We here present results from tracking the temporal evolution of community structures within a research specialty. We explore how the temporal evolution of these maps can be used to provide insights into the historical evolution of a field as well as extract more accurate snapshots of the community structures at a given point in time. We are currently conducting qualitative interviews with experts in this research specialty to assess the validity of the maps.
A First Look at Tribal Web Traffic
Authors: Morgan Vigil, Matthew Rantanen, Elizabeth M. Belding
Venue: WWW
Year: 2015

With broadband penetration rates of less than 10% per capita, Tribal areas in the U.S. represent some of the most underserved communities in terms of Internet access. Although numerous sources have identified this digital divide, there have been no empirical measurements of the performance and usage of services that do exist in these areas. In this paper, we present the characterization of the Tribal Digital Village (TDV) network, a multi-hop wireless network currently connecting 13 reservations in San Diego county. This work represents the first traffic analysis of broadband usage in Tribal lands. After identifying some of the unique purposes of broadband connectivity in indigenous communities, such as language revitalization and cultural development, we focus on the performance of popular applications that enable such activities, including Youtube and Instagram. Though only a fraction of the bandwidth capacity is actually used, 30% of Youtube uploads and 24% of Instagram uploads fail due to packet loss on the relay and access links that connect the reservations to the TDV backbone. Although failure rates are prohibitive to the contribution of locally generated media (particularly videos), our analysis of Instagram media interactions and engagement in the TDV network reveals a high locality of interest. Residents engage with locally created media 8.2 times more than media created by outside sources. Furthermore, locally created media circulates through the network two days longer than non-local media. The results of our analysis point to new directions for increasing content availability on reservations.
Gathering Additional Feedback on Search Results by Multi-Armed Bandits with Respect to Production Ranking
Authors: Aleksandr Vorobev, Damien Lefortier, Gleb Gusev, Pavel Serdyukov
Venue: WWW
Year: 2015

Given a repeatedly issued query and a document with a not-yet-confirmed potential to satisfy the users' needs, a search system should place this document on a high position in order to gather user feedback and obtain a more confident estimate of the document utility. On the other hand, the main objective of the search system is to maximize expected user satisfaction over a rather long period, what requires showing more relevant documents on average. The state-of-the-art approaches to solving this exploration-exploitation dilemma rely on strongly simplified settings making these approaches infeasible in practice. We improve the most flexible and pragmatic of them to handle some actual practical issues. The first one is utilizing prior information about queries and documents, the second is combining bandit-based learning approaches with a default production ranking algorithm. We show experimentally that our framework enables to significantly improve the ranking of a leading commercial search engine.
Modelling Time-aware Search Tasks for Search Personalisation
Authors: Thanh Tien Vu, Alistair Willis, Dawei Song
Venue: WWW
Year: 2015

Recent research has shown that mining and modelling search tasks helps improve the performance of search personalisation. Some approaches have been proposed to model a search task using topics discussed in relevant documents, where the topics are usually obtained from human-generated online ontology such as Open Directory Project. A limitation of these approaches is that many documents may not contain the topics covered in the ontology. Moreover, the previous studies largely ignored the dynamic nature of the search task; with the change of time, the search intent and user interests may also change.
The E-Commerce Market for "Lemons": Identification and Analysis of Websites Selling Counterfeit Goods
Authors: John Wadleigh, Jake Drew, Tyler Moore
Venue: WWW
Year: 2015

We investigate the practice of websites selling counterfeit goods. We inspect web search results for 225 queries across 25 brands. We devise a binary classifier that predicts whether a given website is selling counterfeits by examining automatically extracted features such as WHOIS information, pricing and website content. We then apply the classifier to results collected between January and August 2014. We find that, overall, 32% of search results point to websites selling fakes. For 'complicit' search terms, such as "replica rolex", 39% of the search results point to fakes, compared to 20% for 'innocent' terms, such as "hermes buy online". Using a linear regression, we find that brands with a higher street price for fakes have higher incidence of counterfeits in search results, but that brands who take active countermeasures such as filing DMCA requests experience lower incidence of counterfeits in search results. Finally, we study how the incidence of counterfeits evolves over time, finding that the fraction of search results pointing to fakes remains remarkably stable.
The Knowledge Web Meets Big Scholars
Authors: Kuansan Wang
Venue: WWW
Year: 2015

Human is the only species on earth that has mastered the technologies in writing and printing to capture ephemeral thoughts and scientific discoveries. The capabilities to pass along knowledge, not only geographically but also generationally, have formed the bedrock of our civilizations. We are in the midst of a silent revolution driven by the technological advancements: no longer are computers just a fixture of our physical world but have they been so deeply woven into our daily routines that they are now occupying the center of our lives. No where are the phenomena more prominent than our reliance on the World Wide Web. More and more often, the web has become the primary source of fresh information and knowledge. In addition to general consumption, the availability of large amount of contents and behavioral data has also instigated new interdisciplinary research activities in the areas of information retrieval, natural language processing, machine learning, behavioral studies, social computing and data mining. This talk will use web search as an example to demonstrate how these new research activities and technologies have help the web evolve from a collection of documents to becoming the largest knowledge base in our history. During this evolution, the web is transformed from merely reacting to our needs to a living entity that can anticipate and push timely information to wherever and whenever we need it. How the scholarly activities and communications can be impacted will also be illustrated and elaborated, and some observations derived from a web scale data set, newly release to the public, will also be shared.
User Latent Preference Model for Better Downside Management in Recommender Systems
Authors: Jian Wang, David Hardtke
Venue: WWW
Year: 2015

Downside management is an important topic in the field of recommender systems. User satisfaction increases when good items are recommended, but satisfaction drops significantly when bad recommendations are pushed to them. For example, a parent would be disappointed if violent movies are recommended to their kids and may stop using the recommendation system entirely. A vegetarian would feel steak-house recommendations useless. A CEO in a mid-sized company would feel offended by receiving intern-level job recommendations. Under circumstances where there is penalty for a bad recommendation, a bad recommendation is worse than no recommendation at all. While most existing work focuses on upside management (recommending the best items to users), this paper emphasizes on achieving better downside management (reducing the recommendation of irrelevant or offensive items to users). The approach we propose is general and can be applied to any scenario or domain where downside management is key to the system.