Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Verification of communicating data-driven web services
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
FlexRecs: expressing and combining flexible recommendations
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
On models and query languages for probabilistic processes
ACM SIGMOD Record
Navigating in complex mashed-up applications
Proceedings of the VLDB Endowment
Top-k queries over web applications
The VLDB Journal — The International Journal on Very Large Data Bases
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Web-sites for on-line shopping typically offer a vast number of product options and combinations thereof. While this is very useful, it often makes the navigation in the site and the identification of the "ideal" purchase (where the notion of ideal differs among users) a confusing, non-trivial experience. This demonstration presents ShopIT (ShoppIng assitanT), a system that assists on-line shoppers by suggesting the most effective navigation paths for their specified criteria and preferences. The suggestions are continually adapted to choices/decisions taken by the users while navigating. ShopITis based on a set of novel, adaptive, provably optimal algorithms for TOP-K query evaluation.