An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors
Interacting with Computers
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
ACM Transactions on Information Systems (TOIS)
Robust collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Emotion-based music recommendation by affinity discovery from film music
Expert Systems with Applications: An International Journal
A novel method for personalized music recommendation
Expert Systems with Applications: An International Journal
A Multidimensional Paper Recommender: Experiments and Evaluations
IEEE Internet Computing
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
New approaches to mood-based hybrid collaborative filtering
Proceedings of the Workshop on Context-Aware Movie Recommendation
Handling data sparsity in collaborative filtering using emotion and semantic based features
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Knowledge-Based Systems
Hybrid recommendation approaches for multi-criteria collaborative filtering
Expert Systems with Applications: An International Journal
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Studies in consumer research indicate that mood states have effects on user behaviors and evaluation. In movie recommendation, a user in a bad mood might decide to rate some movies more harshly. In this paper, we examine how users' mood can have an impact on their appraisal of movies in different genres, which in turn can help inform recommender system of picking up movies that are appropriate for users in different mood. Specifically, we carried out two studies. The first consists of a series of user studies to examine user mood and movie ratings to answer questions like: will a user in a more positive mood tend to rate a romantic comedy higher? Will a user in a more nervous mood tend to rate an action movie higher? Then, drawn upon the results from the first study, we modify the traditional collaborative-filtering based recommendation approach by injecting user mood and proposed a mood-aware collaborative-filtering approach. Empirical studies demonstrate that the mood-aware recommendation approach performs better than traditional one that does not consider mood.