How to recognize interesting topics to provide cooperative answering
Information Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Preference structures and their numerical representations
Theoretical Computer Science
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A framework for expressing and combining preferences
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
PREFER: a system for the efficient execution of multi-parametric ranked queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Minimal probing: supporting expensive predicates for top-k queries
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Communications of the ACM
Top-k selection queries over relational databases: Mapping strategies and performance evaluation
ACM Transactions on Database Systems (TODS)
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Conjunctive Query Containment Revisited
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Proceedings of the 17th International Conference on Data Engineering
Preferences; Putting More Knowledge into Queries
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An optimal and progressive algorithm for skyline queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
SIAM Journal on Discrete Mathematics
Personalized Web Search For Improving Retrieval Effectiveness
IEEE Transactions on Knowledge and Data Engineering
Preference formulas in relational queries
ACM Transactions on Database Systems (TODS)
Personalization of Queries in Database Systems
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Personalized Queries under a Generalized Preference Model
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Constrained optimalities in query personalization
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Catching the best views of skyline: a semantic approach based on decisive subspaces
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Branch-and-bound processing of ranked queries
Information Systems
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Preference SQL: design, implementation, experiences
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Fast contextual preference scoring of database tuples
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Categorical skylines for streaming data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mining preferences from superior and inferior examples
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Standing Out in a Crowd: Selecting Attributes for Maximum Visibility
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
P-Cube: Answering Preference Queries in Multi-Dimensional Space
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
FlexRecs: expressing and combining flexible recommendations
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
A preference-based recommender system
EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
A context-aware preference model for database querying in an ambient intelligent environment
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
A survey on representation, composition and application of preferences in database systems
ACM Transactions on Database Systems (TODS)
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People's preferences are expressed at varying levels of granularity and detail as a result of partial or imperfect knowledge. One may have some preference for a general class of entities, for example, liking comedies, and another one for a fine-grained, specific class, such as disliking recent thrillers with Al Pacino. In this article, we are interested in capturing such complex, multi-granular preferences for personalizing database queries and in studying their impact on query results. We organize the collection of one's preferences in a preference network (a directed acyclic graph), where each node refers to a subclass of the entities that its parent refers to, and whenever they both apply, more specific preferences override more generic ones. We study query personalization based on networks of preferences and provide efficient algorithms for identifying relevant preferences, modifying queries accordingly, and processing personalized queries. Finally, we present results of both synthetic and real-user experiments, which: (a) demonstrate the efficiency of our algorithms, (b) provide insight as to the appropriateness of the proposed preference model, and (c) show the benefits of query personalization based on composite preferences compared to simpler preference representations.