A Transformation-based Framework for KNN Set Similarity Search(Extended Abstract)

Set similarity search is a fundamental operation in a variety of applications [3] , [5] , [2] . There is a long stream of research on the problem of set similarity search. Given a collection of set records, a query and a similarity function, the algorithm will return all the set records that are similarity with the query. There are many metrics to measure the similarity between two sets, such as Overlap, Jaccard, Cosine and Dice. In this paper we use the widely applied Jaccard to quantify the similarity between two sets, but our proposed techniques can be easily extended to other set-based similarity functions. Previous approaches require users to specify a threshold of similarity. However, in many scenarios it is rather difficult to specify such a threshold. For example, when users types some keywords in the search engine, they will pay more attention for the results which rank in the front, say the top five ones. In this case, if we use threshold-based search instead of KNN similarity search, it is difficult to find the results that are more attractive for users.