# A Survey of Affective Recommender Systems: Modeling Attitudes, Emotions, and Moods for Personalization

## TL;DR

This survey comprehensively reviews affective recommender systems, classifying them based on psychological theories, and discusses current techniques, challenges, and future directions for emotion and mood-based personalization.

## Contribution

It introduces a taxonomy grounded in psychology, categorizes existing systems, and highlights key trends, limitations, and open challenges in affective recommender systems.

## Key findings

- Classification scheme based on Scherer's typology
- Identification of key affective signal extraction techniques
- Highlighting open challenges and future research directions

## Abstract

Affective Recommender Systems are an emerging class of intelligent systems that aim to enhance personalization by aligning recommendations with users' affective states. Reflecting a growing interest, a number of surveys have been published in this area, however they lack an organizing taxonomy grounded in psychology and they often study only specific types of affective states or application domains. This survey addresses these limitations by providing a comprehensive, systematic review of affective recommender systems across diverse domains. Drawing from Scherer's typology of affective states, we introduce a classification scheme that organizes systems into four main categories: attitude aware, emotion aware, mood aware, and hybrid. We further document affective signal extraction techniques, system architectures, and application areas, highlighting key trends, limitations, and open challenges. As future research directions, we emphasize hybrid models that leverage multiple types of affective states across different modalities, the development of large-scale affect-aware datasets, and the need to replace the folk vocabulary of affective states with a more precise terminology grounded in cognitive and social psychology. Through its systematic review of existing research and challenges, this survey aims to serve as a comprehensive reference and a useful guide for advancing academic research and industry applications in affect-driven personalization.