OpenRaiser/Intern-Atlas huggingface dataset:
A Joint Model for Who-to-Follow and What-to-View Recommendations on Behance
Authors: Maja R. Rudolph, Matthew D. Hoffman, Aaron Hertzmann
Venue: WWW
Year: 2016

Good recommendations are a key tool to increase user engagement and user satisfaction on many social networks. Here we focus on Behance, a social network for artist from various fields such as typography, street art, industrial design, and fashion. On Behance, the artists can connect by following each other, display their work in online portfolios, and brows each other's work. Each user has a personalized dashboard which is an integral part of the Behance experience.
Maps for HTML: A New Media Type and Prototype Client for Web Mapping
Authors: Peter Rushforth
Venue: WWW
Year: 2016

The Web has a long history of Web mapping, being originally described at the first WWW Conference in 1994. Web maps have evolved significantly since then, and patterns have developed. The patterns of modern Web mapping underlie mapping programs across economic sectors. Key issues which remain are that Web standards do not directly address the needs of mapping, and Web mapping standards do not rely on Web architecture. As a result of this lack of collaboration, Web mapping and Web standards continue to evolve independently, with little coordination based on a broader interest. This has led to a situation where newcomers to Web mapping are faced with the problem as to what technologies to use for creating and publishing Web maps, of which the unintended consequence is increasing centralization of Web mapping. This paper documents the results of design and development done by Natural Resources Canada within the scope of the Maps For HTML Community Group. A declarative Web map extension to the HTML standard is proposed, together with a new supporting hypermedia type. Taken together, the proposed standards will progressively support simple to advanced Web map applications, including considerations of layers, projections and feature styling. If widely implemented, the proposed Web standards could help realize the value of the substantial investments in spatial data across all sectors of society.
Mining Big Time-series Data on the Web
Authors: Yasushi Sakurai, Yasuko Matsubara, Christos Faloutsos
Venue: WWW
Year: 2016

Online news, blogs, SNS and many other Web-based services has been attracting considerable interest for business and marketing purposes. Given a large collection of time series, such as web-click logs, online search queries, blog and review entries, how can we efficiently and effectively find typical time-series patterns? What are the major tools for mining, forecasting and outlier detection? Time-series data analysis is becoming of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-line processing capability.
Building Decentralized Applications for the Social Web
Authors: Andrei Vlad Sambra, Amy Guy, Sarven Capadisli, Nicola Greco
Venue: WWW
Year: 2016

Recent advancements in technologies and protocols mean that it is easier than ever to integrate social features into diverse web applications, and increased awareness of privacy concerns means that it is pertinent to consider empowerment of application users when doing so. Many developers are already familiar with the notion of personal data stores; this tutorial will demonstrate how to access or provide such stores for users, and build simple web applications which read and write to the storage whilst remaining completely decoupled from it. This advantages developers in two ways: by removing the burden of storing and maintaining a canonical copy of user data; and by enabling access to and ease of integration with data created through other applications, creating richer, seamless experiences. From the application users' perspective, they need no longer commit and become bound to particular services, but can mix, match and move between those that best meet their needs.
Identifying User Issues and Request Types in Forum Question Posts Based on Discourse Analysis
Authors: Ágnes Sándor, Nikolaos Lagos, Ngoc-Phuoc-An Vo, Caroline Brun
Venue: WWW
Year: 2016

In this paper we propose the detection of user issues and request types in technical forum question posts with a twofold purpose: supporting up-to-date knowledge generation in organizations that provide (semi-) automated customer-care services, and enriching forum metadata in order to enhance the effectiveness of search. We present a categorization system for detecting the proposed question post types based on discourse analysis, and show the advantage of using discourse patterns compared to a baseline relying on standard linguistic features. Besides the detailed description of our method, we also release our annotated corpus to the community.
A Quantitative Analysis of the Most Relevant Gamification Elements in an Online Learning Environment
Authors: Sivaldo J. de Santana, Hugo A. Souza, Victor A. F. Florentin, Ranilson Paiva, Ig Ibert Bittencourt, Seiji Isotani
Venue: WWW
Year: 2016

In the last decade, many researchers have studied the use of game elements in education. The term "gamification" refers to the application of elements used in the development of video games, such as mechanics and dynamics in other contexts unrelated to games, in order to generate more enjoyable and positive attitudes from the students. The gamication process involves using several elements present in video games, like: points, levels, rankings, rewards (badges/achievements) and missions. In this study, we assess whether or not, gamification elements can help and motivate students enrolled in a gamified ontology-based adaptive online learning environment called MeuTutor. In this context, we followed the Pedagogical Recommendation Process to discover which gamification elements were relevant to promote learning, in order to recommend improvements to the environment. To do that, this study shows a quantitative analysis(correlation analysis) of the gamification elements from MeuTutor.
Scalable Monitoring of Student Interaction Indicators in Exploratory Learning Environments
Authors: Sergio Gutiérrez Santos, Stefano Capuzzi, Ken Kahn, Sokratis Karkalas, Alexandra Poulovassilis
Venue: WWW
Year: 2016

We present and evaluate a web-based architecture for monitoring student-system interaction indicators in Exploratory Learning Environments (ELEs),using as our case study a microworld for secondary school algebra. We discuss the challenging role of teachers in exploratory learning settings and motivate the need for visualisation and notification tools that can assist teachers in focusing their attention across the class and inform teachers' interventions. We present an architecture that can support such Teacher Assistance tools and demonstrate its scalability to allow concurrent usage by thousands of users (students and teachers).
Technological Transformations of News: A Long Term Perspective
Authors: Stephen Schifferes
Venue: WWW
Year: 2016

The recent transformation of news is one of a series of major revolutions in news delivery. By studying previous episodes we may find some clues as to the future of social news on the web.
Perceived Task Similarities for Task Recommendation in Crowdsourcing Systems
Authors: Steffen Schnitzer, Svenja Neitzel, Sebastian Schmidt, Christoph Rensing
Venue: WWW
Year: 2016

Crowdsourcing platforms support the assignment of jobs while relying on the workers' search capabilities. Recommenders can support the workers' decisions to improve quality and outcome for both worker and requester. A precedent study showed, that many workers expect to get tasks recommended, which are similar to previously finished ones. In order to create genuine task recommendation, similarities between tasks have to be identified and analyzed. Therefore, this work provides an empirical study about how workers perceive task similarities. The perceived task similarities may vary between workers with different cultural background and may depend e.g. on the complexity, required action or the requester of the task.
A Payment Protocol of the Web, for the Web: Or, Finally Enabling Web Micropayments with the Interledger Protocol
Authors: Evan Schwartz
Venue: WWW
Year: 2016

The history of the Web is full of attempts to enable micropayments for content and services. All have failed to achieve widespread adoption. This has fueled recurring debates about the merits or fundamental flaws of the concept of asking users to pay small amounts for what they use online. As a result, however, of the Web's lack of native payment infrastructure, the only viable business models concentrate earnings and power in a small group of content and advertising aggregators and increase demand for privacy-infringing technologies. We need to learn from the failures of previous micropayment schemes and we need to create a payment protocol that is of the Web, for the Web.
Effect of Spam on Hashtag Recommendation for Tweets
Authors: Surendra Sedhai, Aixin Sun
Venue: WWW
Year: 2016

Presence of spam tweets in a dataset may affect the choices of feature selection, algorithm formulation, and system evaluation for many applications. However, most existing studies have not considered the impact of spam tweets. In this paper, we study the impact of spam tweets on hashtag recommendation for hyperlinked tweets (i.e., tweets containing URLs) in HSpam14 dataset. HSpam14 is a collection of 14 million tweets with annotations of being spam and ham (i.e., non-spam). In our experiments, we observe that it is much easier to recommend "correct" hashtags for spam tweets than ham tweets, because of the near duplicates in spam tweets. Simple approaches like recommending most popular hashtags achieves very good accuracy on spam tweets. On the other hand, features that are highly effective on ham tweets may not be effective on spam tweets. Our findings suggest that without removing spam tweets from the data collection (as in most studies), the results obtained could be misleading for hashtag recommendation tasks.
Instant Espresso: Interactive Analysis of Relationships in Knowledge Graphs
Authors: Stephan Seufert, Patrick Ernst, Srikanta J. Bedathur, Sarath Kumar Kondreddi, Klaus Berberich, Gerhard Weikum
Venue: WWW
Year: 2016

We demonstrate InstantEspresso, a system to explain the relationship between two sets of entities in knowledge graphs. Instant-Espresso answers questions of the form. Which European politicians are related to politicians in the United States, and how? or How can one summarize the relationship between China and countries from the Middle East? Each question is specified by two sets of query entities. These sets (e.g. European politicians or United States politicians) can be determined by an initial graph query over a knowledge graph capturing relationships between real-world entities. Instant-Espresso analyzes the (indirect) relationships that connect entities from both sets and provides a user-friendly explanation of the answer in the form of concise subgraphs. These so-called relatedness cores correspond to important event complexes involving entities from the two sets. Our system provides a user interface for the specification of entity sets and displays a visually appealing visualization of the extracted subgraph to the user. The demonstrated system can be used to provide background information on the current state-of-affairs between real-world entities such as politicians, organizations, and the like, e.g. to a journalist preparing an article involving the entities of interest. InstantEspresso is available for an online demonstration at the URL http://espresso.mpi-inf.mpg.de/.
Predictive Analysis of Temporal and Overlapping Community Structures in Social Media
Authors: Mohsen Shahriari, Stephen Gunashekar, Marven von Domarus, Ralf Klamma
Venue: WWW
Year: 2016

Digital media has some observable traces named communities. Several events such as split, merge, dissolve and survive happen to communities in social media. But what are significant features to predict these events? And to which extent a feature is relevant in a social media? To answer these questions, we perform a study on community evolution analysis and prediction. We employ three overlapping community detection (OCD) algorithms from literature to the case of time-evolving networks including social, email communication and co-authorship networks. Group evolution discovery (GED) technique is applied to track the identified communities. We compare structural properties of OCD algorithms and investigate most persistent communities over time. Furthermore, static and temporal features of a community are applied to build a logistic classifier for community evolution prediction (CEP). Results reveal important features to predict events happening to a community.
Beyond Collaborative Filtering: The List Recommendation Problem
Authors: Oren Sar Shalom, Noam Koenigstein, Ulrich Paquet, Hastagiri P. Vanchinathan
Venue: WWW
Year: 2016

Most Collaborative Filtering (CF) algorithms are optimized using a dataset of isolated user-item tuples. However, in commercial applications recommended items are usually served as an ordered list of several items and not as isolated items. In this setting, inter-item interactions have an effect on the list's Click-Through Rate (CTR) that is unaccounted for using traditional CF approaches. Most CF approaches also ignore additional important factors like click propensity variation, item fatigue, etc. In this work, we introduce the list recommendation problem. We present useful insights gleaned from user behavior and consumption patterns from a large scale real world recommender system. We then propose a novel two-layered framework that builds upon existing CF algorithms to optimize a list's click probability. Our approach accounts for inter-item interactions as well as additional information such as item fatigue, trendiness patterns, contextual information etc. Finally, we evaluate our approach using a novel adaptation of Inverse Propensity Scoring (IPS) which facilitates off-policy estimation of our method's CTR and showcases its effectiveness in real-world settings.
Question Answering on Linked Data: Challenges and Future Directions
Authors: Saeedeh Shekarpour, Kemele M. Endris, Ashwini Jaya Kumar, Denis Lukovnikov, Kuldeep Singh, Harsh Thakkar, Christoph Lange
Venue: WWW
Year: 2016

Question Answering (QA) systems are becoming the inspiring model for the future of search engines. While, recently, datasets underlying QA systems have been promoted from unstructured datasets to structured datasets with semantically highly enriched metadata, question answering systems are still facing serious challenges and are therefore not meeting users' expectations. This paper provides an exhaustive insight of challenges known so far for building QA systems, with a special focus on employing structured data (i.e. knowledge graphs).It thus helps researchers to easily spot gaps to fill with their future research agendas.
Backward Chaining Ontology Reasoning Systems with Custom Rules
Authors: Hui Shi, Kurt Maly, Dazhi Chong, Gongjun Yan, Wu He
Venue: WWW
Year: 2016

In the semantic web, content is tagged with "meaning" or "semantics" to facilitate machine processing and web searching. In general, question answering systems that are built on top of reasoning and inference face a number of difficult issues. In this paper, we analyze scalability issues faced by a question answering system used by a knowledge base with science information that has been harvested from the web. Using this system, we will be able to answer questions that contain qualitative descriptors such as "groundbreaking", "top researcher", and "tenurable at university x". This question answering system has been built using ontologies, reasoning systems and custom based rules for the reasoning system. Furthermore, we evaluated the performance of our optimized backward chaining engine on supporting custom rules and designed the experimental environment including scalable datasets, rule sets, query sets and metrics and compared the experimental results with other in-memory ontology reasoning systems. The results show that our developed backward chaining ontology reasoning system has better scalability than in-memory reasoning systems.
Selective Exploration of Commercial Documents in Web Search
Authors: Alexander Shishkin, Ekaterina Gladkikh, Aleksandr Vorobev
Venue: WWW
Year: 2016

Implicit user feedback is known to be a strong signal of user preferences in web search. Hence, solving the exploration-exploitation dilemma [5] became an important direction of improvement of ranking algorithms in the last years. In this poster, in the case of commercial queries, we consider a new negative effect of exploration on the user utility -- distracting and confusing users by shifting well-known documents from their common positions -- and propose an approach to take it into account within Multi-Armed Bandit algorithms, usually applied to solve the dilemma.
Did You Say U2 or YouTube?: Inferring Implicit Transcripts from Voice Search Logs
Authors: Milad Shokouhi, Umut Ozertem, Nick Craswell
Venue: WWW
Year: 2016

Web search via voice is becoming increasingly popular, taking advantage of recent advances in automatic speech recognition. Speech recognition systems are trained using audio transcripts, which can be generated by a paid annotator listening to some audio and manually transcribing it. This paper considers an alternative source of training data for speech recognition, called implicit transcription. This is based on Web search clicks and reformulations, which can be interpreted as validating or correcting the recognition done during a real Web search. This can give a large amount of free training data that matches the exact characteristics of real incoming voice searches and the implicit transcriptions can better reflect the needs of real users because they come from the user who generated the audio. On an overall basis we demonstrate that the new training data has value in improving speech recognition. We further show that the in-context feedback from real users can allow the speech recognizer to exploit contextual signals, and reduce the recognition error rate further by up to 23%.
Mining Online Social Data for Detecting Social Network Mental Disorders
Authors: Hong-Han Shuai, Chih-Ya Shen, De-Nian Yang, Yi-Feng Lan, Wang-Chien Lee, Philip S. Yu, Ming-Syan Chen
Venue: WWW
Year: 2016

An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental factors considered in standard diagnostic criteria (questionnaire) cannot be observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMDbased Tensor Model (STM) to improve the performance. Our framework is evaluated via a user study with 3126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results show that SNMDD is promising for identifying online social network users with potential SNMDs.
Analyzing Sequential User Behavior on the Web
Authors: Philipp Singer, Florian Lemmerich
Venue: WWW
Year: 2016

This tutorial aims at outlining fundamental methods for studying categorical sequences on the Web. Categorical sequences can refer to any kind of transitional data between a set of states, for example human navigation (transitions) between Web sites (states). Presented methods focus on sequential pattern mining, modeling and inference aiming at better understanding the production of sequences. A core model utilized in this tutorial is the Markov chain model. We hope that this tutorial raises interest and awareness of the field at hand and provides participants with basic tools for analyzing sequential user behavior on the Web.
Complex Patterns in Dynamic Attributed Graphs
Authors: Rina Singh, Jeffrey A. Graves, Douglas A. Talbert
Venue: WWW
Year: 2016

In recent years, there has been huge growth in the amount of graph data generated from various sources. These types of data are often represented by vertices and edges in a graph with real-valued attributes, topological properties, and temporal information associated with the vertices. Until recently, most pattern mining techniques focus solely on vertex attributes, topological properties, or a combination of these in a static sense; mining attribute and topological changes simultaneously over time has largely been overlooked. In this work-in-progress paper, we propose to extend an existing state-of-the-art technique to mine for patterns in dynamic attributed graphs which appear to trigger changes in attribute values.
Using Semantics to Search Answers for Unanswered Questions in Q&A Forums
Authors: Priyanka Singh, Elena Simperl
Venue: WWW
Year: 2016

The expert based question and answering forums are crowdsourced and rely on people to provide answers for questions. This paper focuses on technology based Q&A systems like StackOverflow and Reddit. These websites are popular and yet many questions remain unanswered. The Suman system uses semantic keyword search in combination with traditional text search techniques to find similar questions with answers for unanswered questions. Furthermore, the Suman system also recommends experts who can answer those questions. This helps to narrow down the long tail of unanswered questions. The Suman system utilises Semantic Web and Linked Data technologies to integrate the datasets from two websites, structure them and link them to Linked Data Cloud. It uses available tools to solve name entity disambiguation problem and expands the query term with added semantics. The Suman system was evaluated and results were analysed to show its viability.
Fusing Social Media Cues: Personality Prediction from Twitter and Instagram
Authors: Marcin Skowron, Marko Tkalcic, Bruce Ferwerda, Markus Schedl
Venue: WWW
Year: 2016

Incorporating users' personality traits has shown to be instrumental in many personalized retrieval and recommender systems. Analysis of users' digital traces has become an important resource for inferring personality traits. To date, the analysis of users' explicit and latent characteristics is typically restricted to a single social networking site (SNS). In this work, we propose a novel method that integrates text, image, and users' meta features from two different SNSs: Twitter and Instagram. Our preliminary results indicate that the joint analysis of users' simultaneous activities in two popular SNSs seems to lead to a consistent decrease of the prediction errors for each personality trait.