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

2025-08-27

AI Summary

Existing surveys on affective recommender systems suffer from weak psychological foundations, narrow coverage, and inconsistent taxonomies. To address these limitations, this work introduces—based on Scherer’s component process model of emotion—the first psychology-driven four-dimensional classification framework encompassing attitudes, emotions, moods, and hybrid affective states. We propose a multimodal fusion paradigm for hybrid affective modeling and advocate replacing folk-psychological affect terms with rigorously defined cognitive and social-psychological constructs. The survey systematically organizes cross-domain affect signal acquisition, perception methodologies, and architectural design principles, synthesizing technical pathways and application scenarios. Key challenges—including dynamic affect modeling, model interpretability, and ethical implications—are identified and critically analyzed. This work provides both a theoretically grounded framework and actionable guidelines for advancing academic research and industrial deployment of affective recommender systems. (149 words)

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Data Mining & Knowledge Management: Recommender Systems
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Classifying affective recommender systems using psychological taxonomy
• Addressing limitations in existing surveys on affective states
• Providing systematic review across diverse application domains

Innovation

• Classification scheme using Scherer's affective typology
• Systematic review of affective signal extraction techniques
• Hybrid models leveraging multiple affective state types