Ranking in Information Retrieval (IR) has been traditionally evaluated from the perspective
of the relevance of search engine results to people searching for information, ie, the extent to …

Purpose The purpose of this paper is to develop a conceptual framework for assessing what
are the possibilities and pitfalls of using algorithmic systems of news personalization–ie the …

News consumption has shifted over time from traditional media to online platforms, which
use recommendation algorithms to help users navigate through the large incoming streams …

The decision-making process of many state-of-the-art machine learning models is inherently
inscrutable to the extent that it is impossible for a human to interpret the model directly: they …

Knowledge graphs (KG) have become increasingly important to endow modern
recommender systems with the ability to generate traceable reasoning paths to explain the …

A key problem in information retrieval is understanding the latent intention of a user's under-
specified query. Retrieval models that are able to correctly uncover the query intent often …

State-of-the-art industrial-level recommender system applications mostly adopt complicated
model structures such as deep neural networks. While this helps with the model …

In the recent political climate, the topic of news quality has drawn attention both from the
public and the academic communities. The growing distrust of traditional news media makes …

Explaining complex algorithms and models has recently received growing attention in
various domains to support informed decisions. Ranking functions are widely used for …

Algorithmic recommender systems are on the rise in various societal domains, including
journalism. While they offer great promise by making useful selections of large content …