Recommender systems have been researched extensively over the past decades. Whereas several algorithms have been developed and deployed in various application domains …
Deep learning models for natural language processing (NLP) are increasingly adopted and deployed by analysts without formal training in NLP or machine learning (ML). However, the …
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making …
Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a …
Recommender systems have been increasingly used in online services that we consume daily, such as Facebook, Netflix, YouTube, and Spotify. However, these systems are often …
E Lex, D Kowald, P Seitlinger, TNT Tran… - … and trends® in …, 2021 - nowpublishers.com
Personalized recommender systems have become indispensable in today's online world. Most of today's recommendation algorithms are data-driven and based on behavioral data …
M Jugovac, D Jannach - ACM Transactions on Interactive Intelligent …, 2017 - dl.acm.org
Automated recommendations have become a ubiquitous part of today's online user experience. These systems point us to additional items to purchase in online shops, they …
Recommender systems usually face the issue of filter bubbles: over-recommending homogeneous items based on user features and historical interactions. Filter bubbles will …
Traditionally, the field of recommender systems has evaluated the fruits of its labor using metrics of algorithmic accuracy and precision (see Chap. 8 for an overview of recommender …