The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Modern Information Retrieval
Exploring Versus Exploiting when Learning User Models for Text Recommendation
User Modeling and User-Adapted Interaction
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
I tag, you tag: translating tags for advanced user models
Proceedings of the third ACM international conference on Web search and data mining
A Scalable, Accurate Hybrid Recommender System
WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
Music recommendations with temporal context awareness
Proceedings of the fourth ACM conference on Recommender systems
Personalized topic-based tag recommendation
Neurocomputing
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
| Hi-index | 0.00 |
This work deals with the problem of automatically creating semantic queries for knowledge bases from preference feedback. Semantic knowledge bases are a good source for retrieving entities for item recommendation. We show that preference decisions are not only based on entities, but also on their corresponding predicate-object relations. By extracting the weights from trained preference models, the weighted predicate-object relations can be stored to a user model. The objective is to use such prototype entities in a general user model to formulate semantic queries for recommendation retrieval.