Highlights•Big Data offer a cheap, extensive source of consumer information for
marketers.•Prior research focused on predicting rather than understanding consumer …

Recommendation systems have overcome the overload of irrelevant information by
considering users' preferences and emotional states in the fields of tourism, health, e …

We present a multimodal data set for the analysis of human affective states. The
electroencephalogram (EEG) and peripheral physiological signals of 32 participants were …

This paper presents a user-independent emotion recognition method with the goal of
recovering affective tags for videos using electroencephalogram (EEG), pupillary response …

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 …

Decision making is the cognitive process of identifying and choosing alternatives based on
preferences, beliefs, and degree of importance given by the decision maker to objects or …

As of today, most movie recommendation services base their recommendations on
collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata …

A large amount of research in recommender systems focuses on algorithmic accuracy and
optimization of ranking metrics. However, recent work has unveiled the importance of other …

Personality, as defined in psychology, accounts for the individual differences in users'
preferences and behaviour. It has been found that there are significant correlations between …

Images can convey rich semantics and induce various emotions to viewers. Most existing
works on affective image analysis focused on predicting the dominant emotions for the …