Automating the assignment of submitted manuscripts to reviewers
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Improving text classification accuracy using topic modeling over an additional corpus
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Personalized recommendation on dynamic content using predictive bilinear models
Proceedings of the 18th international conference on World wide web
Technical paper recommendation: a study in combining multiple information sources
Journal of Artificial Intelligence Research
Recommender systems for the conference paper assignment problem
Proceedings of the third ACM conference on Recommender systems
Growing an organic indoor location system
Proceedings of the 8th international conference on Mobile systems, applications, and services
Effective event discovery: using location and social information for scoping event recommendations
Proceedings of the fifth ACM conference on Recommender systems
Ads and the city: considering geographic distance goes a long way
Proceedings of the sixth ACM conference on Recommender systems
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Hybrid event recommendation using linked data and user diversity
Proceedings of the 7th ACM conference on Recommender systems
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We demonstrate a method for collaborative ranking of future events. Previous work on recommender systems typically relies on feedback on a particular item, such as a movie, and generalizes this to other items or other people. In contrast, we examine a setting where no feedback exists on the particular item. Because direct feedback does not exist for events that have not taken place, we recommend them based on individuals' preferences for past events, combined collaboratively with other peoples' likes and dislikes. We examine the topic of unseen item recommendation through a user study of academic (scientific) talk recommendation, where we aim to correctly estimate a ranking function for each user, predicting which talks would be of most interest to them. Then by decomposing user parameters into shared and individual dimensions, we induce a similarity metric between users based on the degree to which they share these dimensions. We show that the collaborative ranking predictions of future events are more effective than pure content-based recommendation. Finally, to further reduce the need for explicit user feedback, we suggest an active learning approach for eliciting feedback and a method for incorporating available implicit user cues.