Group recommendation: semantics and efficiency
Proceedings of the VLDB Endowment
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Improved recommendations via (more) collaboration
Procceedings of the 13th International Workshop on the Web and Databases
Adaptive bootstrapping of recommender systems using decision trees
Proceedings of the fourth ACM international conference on Web search and data mining
Diversification and refinement in collaborative filtering recommender
Proceedings of the 20th ACM international conference on Information and knowledge management
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Traditional recommender systems generate personalized recommendations based on a profile that they create for each user. We argue here that such profiles are often too coarse to capture the current user's state of mind and desire. For example, a serious user that usually prefers documentary features may, at the end of a long and tiring conference, be in the mood for a lighter entertaining movie, not captured by her usual profile. As communicating one's state of mind to a system in (key)words may be difficult, we present in this demo Mood4 - a novel plug-in for recommender systems, which allows users to describe their current desire/mood through examples. Mood4 utilizes the user's examples to refine the recommendations generated by a given recommender system, considering several, possibly competing, desired properties of the recommended items set (rating, diversity, coverage). The system uses a novel algorithm, based on a simple geometric representation of the items, which allows for efficient processing and the generation of suitable recommendations even in the absence of semantic information.