THEWEBCONF

In this keynote we describe progress in work that our research teams have been doing over the past years, including advances in difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, ...

The Internet moves in phases, and we are entering the third in 20 years. In this keynote, using a framework drawn from the Law of the Horse [1], I describe the phase we are entering - the surveillance phase - and the threat it presents to society ...

Over the past three years, platforms, governments and a plethora of nonprofit initiatives have prioritized fighting online misinformation through a variety of different means. Yet the current framework is too fragmented to deliver global results. The ...

Existing unbiased learning-to-rank models use counterfactual inference, notably Inverse Propensity Scoring (IPS), to learn a ranking function from biased click data. They handle the click incompleteness bias, but usually assume that the clicks are noise-...

Graph edges, along with their labels, can represent information of fundamental importance, such as links between web pages, friendship between users, the rating given by users to other users or items, and much more. We introduce LEAP, a trainable, ...

Email remains a critical channel for communicating information in both personal and work accounts. The number of emails people receive every day can be overwhelming, which in turn creates challenges for efficient information management and consumption. ...

Can neural networks learn to compare graphs without feature engineering? In this paper, we show that it is possible to learn representations for graph similarity with neither domain knowledge nor supervision (i.e. feature engineering or labeled graphs). ...

With the ever-increasing cases of hate spread on social media platforms, it is critical to design abuse detection mechanisms to pro-actively avoid and control such incidents. While there exist methods for hate speech detection, they stereotype words and ...

Product bundling, offering a combination of items to customers, is one of the marketing strategies commonly used in online e-commerce and offline retailers. A high-quality bundle generalizes frequent items of interest, and diversity across bundles ...

Clinical notes contain detailed information about health status of patients for each of their encounters with a health system. Developing effective models to automatically assign medical codes to clinical notes has been a long-standing active research ...

Many approaches focus on detecting dense blocks in the tensor of multimodal data to prevent fraudulent entities (e.g., accounts, links) from retweet boosting, hashtag hijacking, link advertising, etc. However, no existing method is effective to find the ...

Data collection often involves the partial measurement of a larger system. A common example arises in collecting network data: we often obtain network datasets by recording all of the interactions among a small set of core nodes, so that we end up with ...

Tor hidden services allow offering and accessing various Internet resources while guaranteeing a high degree of provider and user anonymity. So far, most research work on the Tor network aimed at discovering protocol vulnerabilities to de-anonymize ...

Despite being vast repositories of factual information, cross-domain knowledge graphs, such as Wikidata and the Google Knowledge Graph, only sparsely provide short synoptic descriptions for entities. Such descriptions that briefly identify the most ...

This paper provides an in-depth and diversified analysis of the Wikidata query logs, recently made publicly available. Although the usage of Wikidata queries has been the object of recent studies, our analysis of the query traffic reveals interesting ...

The spread of content produced by fake news publishers was one of the most discussed characteristics of the 2016 U.S. Presidential Election. Yet, little is known about the prevalence and focus of such content, how its prevalence changed over time, and ...

Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the ”knowledge” in KG at the shallow ...

Enriching the content of news articles with auxiliary resources is a technique often employed by online news services to keep articles up-to-date and thereby increase users' engagement. We address the task of enriching news articles with related search ...

We revisit the opinion susceptibility problem that was proposed by Abebe et al. [1], in which agents influence one another's opinions through an iterative process. Each agent has some fixed innate opinion. In each step, the opinion of an agent is updated ...

Community norm violations can impair constructive communication and collaboration online. As a defense mechanism, community moderators often address such transgressions by temporarily blocking the perpetrator. Such actions, however, come with the cost ...

We consider the problem of regulating products with negative externalities to a third party that is neither the buyer nor the seller, but where both the buyer and seller can take steps to mitigate the externality. The motivating example to have in mind ...

Online advertising is one of the primary funding sources for various of content, services, and applications on both web and mobile platforms. Mobile in-app advertising reuses many existing web technologies under the same ad-serving model (i.e., users - ...

Modeling the behaviors of drug-target-disease interactions is crucial in the early stage of drug discovery and holds great promise for precision medicine and personalized treatments. The growing availability of new types of data on the internet brings ...

Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data. Only positive feedback are observed and the unobserved feedback can be attributed to two reasons: unknow or dislike. Existing methods ...

Recommendation serendipity is being increasingly recognized as being equally important as the other beyond-accuracy objectives (such as novelty and diversity), in eliminating the “filter bubble” phenomenon of the traditional recommender systems. However,...

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages, e.g., more English texts are labeled than texts in any other languages, which creates a ...

Graph smoothing methods are an extremely popular family of approaches for semi-supervised learning. The choice of graph used to represent relationships in these learning problems is often a more important decision than the particular algorithm or loss ...

Recently, data mining through analyzing the complex structure and diverse relationships on multi-network has attracted much attention in both academia and industry. One crucial prerequisite for this kind of multi-network mining is to map the nodes ...

We present a method for implementing shrinkage of treatment effect estimators, and hence improving their precision, via experiment splitting. Experiment splitting reduces shrinkage to a standard prediction problem. The method makes minimal ...

This paper introduces an active-learning-based truth estimator for social networks, such as Twitter, that enhances estimation accuracy significantly by requesting a well-selected (small) fraction of data to be labeled. Data assessment and truth ...