Recommender systems have significantly developed in recent years in parallel with the
witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) …

Learner modeling is a basis of personalized, adaptive learning. The research literature
provides a wide range of modeling approaches, but it does not provide guidance for …

Deep learning techniques have become the method of choice for researchers working on
algorithmic aspects of recommender systems. With the strongly increased interest in …

With tremendous amount of recommendation algorithms proposed every year, one critical
issue has attracted a considerable amount of attention: there are no effective benchmarks for …

Research on understanding, develo** and assessing personalisation systems is spread
over multiple disciplines and builds on methodologies and findings from several different …

Citation recommendation describes the task of recommending citations for a given text. Due
to the overload of published scientific works in recent years on the one hand, and the need …

Many of today's online services provide personalized recommendations to their users. Such
recommendations are typically designed to serve certain user needs, eg, to quickly find …

The purpose of this study is to present an exhaustive analysis on research paper
recommender systems which have become very popular and gained a lot of research …

Scholars in algorithmic recommender systems research have developed a largely
standardized scientific method, where progress is claimed by showing that a new algorithm …

Research is facing a reproducibility crisis, in which the results and findings of many studies
are difficult or even impossible to reproduce. This is also the case in machine learning (ML) …