A framework for expressing and combining preferences
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Preference formulas in relational queries
ACM Transactions on Database Systems (TODS)
Personalized Queries under a Generalized Preference Model
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Spectral regression: a unified subspace learning framework for content-based image retrieval
Proceedings of the 15th international conference on Multimedia
Fast contextual preference scoring of database tuples
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Ranking Query Results using Context-Aware Preferences
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Adapting ranking functions to user preference
ICDEW '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop
Modeling the propagation of user preferences
ER'11 Proceedings of the 30th international conference on Conceptual modeling
| Hi-index | 0.00 |
There has been an increasing interest in context-awareness and preferences for database querying. Ranking of database query results under different contexts is an effective approach to provide the most relevant information to the right users. By applying the regression models developed in the statistics field, we present a quantitative way to measure the impact of context upon database query results by means of contextual ranking functions with context attributes and their influential database attributes as parameters. To make the approach computationally efficient, we furthermore propose to reduce the dimensionality of context space, which can not only increase computational efficiency but also help ones identify informative association patterns among context attributes and database attributes. Our experimental study on both synthetic and real data verifies the efficiency and effectiveness of our methods.