Perk: personalized keyword search in relational databases through preferences

Keyword-based search in relational databases allows users to dis-cover relevant information without knowing the database schema or using complicated queries. However, such searches may return an overwhelming number of results, often loosely related to the user intent. In this paper, we propose personalizing keyword database search by utilizing user preferences. Query results are ranked based on both their relevance to the query and their preference degree for the user. To further increase the quality of results, we consider two new metrics that evaluate the goodness of the result as a set, namely coverage of many user interests and content diversity. We present an algorithm for processing preference queries that uses the pref-erential order between keywords to direct the joining of relevant tuples from multiple relations. We then show how to reduce the complexity of this algorithm by sharing computational steps. Fi-nally, we report evaluation results of the efficiency and effective-ness of our approach. 1