Query Recommendation System
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Date
2016-07-13T05:35:14Z
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Abstract
Interactive database exploration is a key task in information mining. However,
users who lack SQL expertise or familiarity with the database schema face great
difficulties in performing this task. To aid these users, we developed the QueRIE system
for personalized query recommendations. QueRIE continuously monitors the user’s
querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find
interesting. In this work we describe an instantiation of the QueRIE framework, where the
active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through
experimentation that the proposed method generates meaningful recommendations on
real-life traces from the Sky Server database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach.
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Query Recommendation System, Divya Chaudhary, K.R Anushree