Visual information seeking: tight coupling of dynamic query filters with starfield displays
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CHI '94 Conference Companion on Human Factors in Computing Systems
Readings in information visualization
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Min-wise independent permutations
Journal of Computer and System Sciences - 30th annual ACM symposium on theory of computing
Placing search in context: the concept revisited
Proceedings of the 10th international conference on World Wide Web
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Enhanced word clustering for hierarchical text classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
Automatic categorization of query results
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Ranking objects based on relationships
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Ordering the attributes of query results
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Probabilistic ranking of database query results
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Making database systems usable
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Supporting OLAP operations over imperfectly integrated taxonomies
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Management as a Service for IT Service Management
ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
It takes variety to make a world: diversification in recommender systems
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Answering approximate queries over autonomous web databases
Proceedings of the 18th international conference on World wide web
Hierarchical result views for keyword queries over relational databases
Proceedings of the First International Workshop on Keyword Search on Structured Data
Exploring biomedical databases with BioNav
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
DataLens: making a good first impression
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
DivQ: diversification for keyword search over structured databases
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Making interval-based clustering rank-aware
Proceedings of the 14th International Conference on Extending Database Technology
Diversification and refinement in collaborative filtering recommender
Proceedings of the 20th ACM international conference on Information and knowledge management
Search result diversification for enterprise data
Proceedings of the 20th ACM international conference on Information and knowledge management
Differentiating search results on structured data
ACM Transactions on Database Systems (TODS)
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Skimmer: rapid scrolling of relational query results
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Parameter-free and domain-independent similarity search with diversity
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Diversity maximization under matroid constraints
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Profile diversity in search and recommendation
Proceedings of the 22nd international conference on World Wide Web companion
On the complexity of query result diversification
Proceedings of the VLDB Endowment
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Database queries are often exploratory and users often find their queries return too many answers, many of them irrelevant. Existing work either categorizes or ranks the results to help users locate interesting results. The success of both approaches depends on the utilization of user preferences. However, most existing work assumes that all users have the same user preferences, but in real life different users often have different preferences. This paper proposes a two-step solution to address the diversity issue of user preferences for the categorization approach. The proposed solution does not require explicit user involvement. The first step analyzes query history of all users in the system offline and generates a set of clusters over the data, each corresponding to one type of user preferences. When user asks a query, the second step presents to the user a navigational tree over clusters generated in the first step such that the user can easily select the subset of clusters matching his needs. The user then can browse, rank, or categorize the results in selected clusters. The navigational tree is automatically constructed using a cost-based algorithm which considers the cost of visiting both intermediate nodes and leaf nodes in the tree. An empirical study demonstrates the benefits of our approach.