Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
A personalization framework for OLAP queries
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
A framework for recommending OLAP queries
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
Enhanced Business Intelligence using EROCS
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Preference-Based Recommendations for OLAP Analysis
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Discovering functional dependencies for multidimensional design
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
Query recommendations for OLAP discovery driven analysis
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
myOLAP: An Approach to Express and Evaluate OLAP Preferences
IEEE Transactions on Knowledge and Data Engineering
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As the amount and complexity of data keep increasing in data warehouses, their exploration for analytical purposes may be hindered. Recommender systems have grown very popular on the Web with sites like Amazon, Netflix, etc. These systems proved successful to help users explore available content related to what they are currently looking at. Recent systems consider the use of recommendation techniques to suggest data warehouse queries and help an analyst pursue its exploration. In this paper, we present a personalized query expansion component which suggests measures and dimensions to iteratively build consistent queries over a data warehouse. Our approach leverages (a) semantics defined in multi-dimensional domain models, (b) collaborative usage statistics derived from existing repositories of Business Intelligence documents like dashboards and reports and (c) preferences defined in a user profile. We finally present results obtained with a prototype implementation of an interactive query designer.