Affective computing
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Getting to know you: learning new user preferences in recommender systems
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Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
TiVo: making show recommendations using a distributed collaborative filtering architecture
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Neural Networks - Special issue: Emotion and brain
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
User Modeling and User-Adapted Interaction
Entertainment capture through heart rate activity in physical interactive playgrounds
User Modeling and User-Adapted Interaction
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
Diagnosing and acting on student affect: the tutor's perspective
User Modeling and User-Adapted Interaction
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
User Modeling and User-Adapted Interaction
Introduction to special Issue on `Affective modeling and adaptation'
User Modeling and User-Adapted Interaction
Comparing Two Emotion Models for Deriving Affective States from Physiological Data
Affect and Emotion in Human-Computer Interaction
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Emotion-based music recommendation by affinity discovery from film music
Expert Systems with Applications: An International Journal
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
Social signal processing: Survey of an emerging domain
Image and Vision Computing
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Exploiting facial expressions for affective video summarisation
Proceedings of the ACM International Conference on Image and Video Retrieval
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Expert Systems with Applications: An International Journal
Interfaces for eliciting new user preferences in recommender systems
UM'03 Proceedings of the 9th international conference on User modeling
Content-based recommendation systems
The adaptive web
Photo-based user profiling for tourism recommender systems
EC-Web'07 Proceedings of the 8th international conference on E-commerce and web technologies
Foundation of a new digital ecosystem for u-content: needs, definition, and design
Proceedings of the 2011 international conference on Virtual and mixed reality: systems and applications - Volume Part II
Impact of implicit and explicit affective labeling on a recommender system's performance
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Beyond lists: studying the effect of different recommendation visualizations
Proceedings of the sixth ACM conference on Recommender systems
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There is an increasing amount of multimedia content available to end users. Recommender systems help these end users by selecting a small but relevant subset of items for each user based on her/his preferences. This paper investigates the influence of affective metadata (metadata that describe the user's emotions) on the performance of a content-based recommender (CBR) system for images. The underlying assumption is that affective parameters are more closely related to the user's experience than generic metadata (e.g. genre) and are thus more suitable for separating the relevant items from the non-relevant. We propose a novel affective modeling approach based on users' emotive responses. We performed a user-interaction session and compared the performance of the recommender system with affective versus generic metadata. The results of the statistical analysis showed that the proposed affective parameters yield a significant improvement in the performance of the recommender system.