Recommender systems have become a popular and effective means to manage the ever-
increasing amount of multimedia content available today and to help users discover …

Recommender systems have become pervasive on the web, shaping the way users see
information and thus the decisions they make. As these systems get more complex, there is …

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 …

This book discusses different aspects of group recommender systems which are systems
that help to identify recommendations for groups instead of single users. In this context, the …

User profiling in social media has gained a lot of attention due to its varied set of
applications in advertising, marketing, recruiting, and law enforcement. Among the various …

Personality recognition in text is a critical problem in classifying personality traits from the
input content of users. Recent studies address this issue by fine-tuning pre-trained language …

This chapter discusses main opportunities and challenges of assessing and utilizing
personality traits in personalized interactive systems and services. This unique perspective …

The idea of utilizing computers for question answering tasks has been around from the early
beginning of these systems. First algorithms with the aim to accomplish this were already …

Recommender systems, like other tools that make use of machine learning, are known to
create or increase certain biases. Earlier work has already unveiled different performance of …

How much trust a user places in a recommender is crucial to the uptake of the
recommendations. Although prior work established various factors that build and sustain …