recommenders

At the moment, the CORE recommender provides non-personalised content-based article recommendations from across the global open access repositories and journals network. In the future, we want to transform our recommender from a pure CBF recommendation engine to a solution that can also deliver personalised recommendations using CF based on past... See more

“System 2” Recommenders – Imagine a feed powered by your long-term goals, not just your immediate engagement. Perhaps AI-powered recommendation systems that help audit your existing feed, and suggest content that you will view as valuable upon reflection while reducing your propensity to be stuck in a filter bubble could be produced as a public... See more

However, researchers are often immersed in more complex search activities, the so-called exploratory search (Marchionini, 2006; Herrmannova & Knoth, 2012). This type of search is characteristic by the difficulty of formulating effective queries and involves, for example, analysis, comparison, exclusion/negation and synthesis. Recommender systems... See more

While there is always the alternative of CBF, CF recommenders are currently known to produce better results in most scenarios. An important issue is that CF recommender systems for research can already be developed by companies that have many users on their platform and consequently collect and own such rich interaction data. However, these... See more