Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Emotion in human-computer interaction
The human-computer interaction handbook
Developing multimodal intelligent affective interfaces for tele-home health care
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
eMoto: emotionally engaging interaction
Personal and Ubiquitous Computing
Compound Critiques for Conversational Recommender Systems
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Introduction to the special section on recommender systems
ACM Transactions on Computer-Human Interaction (TOCHI)
Towards mood-oriented interfaces for synchronous interaction
CLIHC '05 Proceedings of the 2005 Latin American conference on Human-computer interaction
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Making recommendations better: an analytic model for human-recommender interaction
CHI '06 Extended Abstracts on Human Factors in Computing Systems
International Journal of Human-Computer Studies
Editorial: Evaluating affective interactions
International Journal of Human-Computer Studies
How emotion is made and measured
International Journal of Human-Computer Studies
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
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Watching a comedy can help a user escape from the negative mood, which in turn affect the user's feedback over the movie. In other words, a non-cognitive mood inducer (the movie) can affect a user's post-consumption evaluation over the inducer (the rating the user give) which is directly associated with users' assessment over consumed goods. If these goods are generated from a recommender system, they will then directly affect the performance of the system. As such, our study attempts to enrich our understanding of the inducers and their effects in the recommendation performance. In addition, this paper provides a preliminary exploration of a mood-based filter to enhance the interaction between human and the system.