Knowledge graphs have been pivotal in supporting downstream applications like search, recommendation, and question answering, among others. Therefore, knowledge graphs have naturally become key enabling technologies in e-Commerce platforms. Developing a ...

As ML systems have become more broadly adopted in high-stakes settings, our scrutiny of them should reflect their greater impact on real lives. The field of fairness in data mining and machine learning has blossomed in the last decade, but most of the ...

Online advertising has historically been approached as user targeting and ad-to-user matching problems within sophisticated optimization algorithms. As the research area and ad tech industry have progressed over the last couple of decades, advertisers ...

Data is one of the most critical resources in the AI Era. While substantial research has been dedicated to training machine learning models using various types of data, much less efforts have been invested in the exploration of assessing and governing ...

Similarity query (a.k.a. nearest neighbor query) processing has been an active research topic for several decades. It is an essential procedure in a wide range of applications (e.g., classification & regression, deduplication, image retrieval, and ...

Recent years have witnessed the enormous success of text representation learning in a wide range of text mining tasks. Earlier word embedding learning approaches represent words as fixed low-dimensional vectors to capture their semantics. The word ...

A wealth of human knowledge is expressed in structured tables, across web pages, scientific articles, spreadsheets, and databases. This wealth of knowledge is mirrored by diversity in the vast number of layout structures, content types, formats, and ...

Recommender systems (RS) have been successfully explored in a vast number of domains, e.g. movies and tv shows, music, or e-commerce. In these domains we have a large number of datasets freely available for testing and evaluating new recommender ...

Designers of online search and recommendation services often need to develop metrics to assess system performance. This tutorial focuses on mixed methods approaches to developing user-focused evaluation metrics. This starts with choosing how data is ...

Networks (i.e., graphs) are often collected from multiple sources and platforms, such as social networks extracted from multiple online platforms, team-specific collaboration networks within an organization, and inter-dependent infrastructure networks, ...

Blockchain technology garners an ever-increasing interest of researchers in various domains that benefit from scalable cooperation among trust-less parties. As blockchains and their applications proliferate, so do the complexity and volume of data ...

Drug discovery is a long and costly process, taking on average 10 years and 2.5 billion dollars to develop a new drug. Artificial intelligence has the potential to significantly accelerate the process of drug discovery by analyzing a large amount of ...

Language scaling aims to deploy Natural Language Processing (NLP) applications economically across many countries/regions with different languages. Language scaling has been heavily invested by industry since many parties want to deploy their ...

The rise of social media has democratized content creation and has made it easy for anybody to share and to spread information online. On the positive side, this has given rise to citizen journalism, thus enabling much faster dissemination of ...

Thanks to the explosion of heterogeneous healthcare data and advanced machine learning and data mining techniques, specifically deep learning methods, we now have an opportunity to make difference in healthcare. In this tutorial, we will present state-...

The development of recommender systems usually deal with single-objective optimizations, such as minimizing prediction errors or maximizing the ranking quality. There is an emerging demand in multi-objective recommendations in which the recommendation ...

The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds and more recently compounded with the complex user's path to purchase across devices, ...

We propose to organize the 6th ODD workshop at KDD 2021, following the successful series of the past five ODD Workshops that have been organized at KDD 2013, KDD 2014, KDD 2015, KDD 2016, and KDD 2018.

Automatic processing of bibliographic data becomes very important in digital libraries, data science and machine learning due to its importance in keeping pace with the significant increase of published papers every year from one side and to the inherent ...