Fab: content-based, collaborative recommendation
Communications of the ACM
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
What am I gonna wear?: scenario-oriented recommendation
Proceedings of the 12th international conference on Intelligent user interfaces
Proposing an ESL recommender teaching and learning system
Expert Systems with Applications: An International Journal
Selecting keywords for content based recommendation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Proposing a charting recommender system for second-language nurses
Expert Systems with Applications: An International Journal
Recommendation of text tags using linked data
Proceedings of the 3rd International Workshop on Semantic Search Over the Web
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We examine the problems with automated recommendation systems when information about user preferences is limited. We equate the problem to one of content similarity measurement and apply techniques from Natural Language Processing to the domain of movie recommendation. We describe two algorithms, a naïve word-space approach and a more sophisticated approach using topic signatures, and evaluate their performance compared to baseline, gold standard, and commercial systems.