Authority flow is an effective ranking mechanism for answering queries on a broad class of data. Systems have been developed to apply this principle on the Web (PageRank and topic sensitive PageRank), bibliographic databases (ObjectRank), and biological databases (Hubs of Knowledge project). However, these systems have the following drawbacks: (a) There is no way to explain to the user why a particular result received its current score; (b) The authority flow rates, which have been shown to dramatically affect the results' quality in ObjectRank, have to be set manually by a domain expert; (c) There is no query reformulation methodology to refine the query results according to the user's preferences. In this work, we address these shortcomings by introducing a framework and algorithms to explain query results and reformulate authority flow queries based on the user's feedback. The query reformulation process can be used to learn the user's preferences and automatically adjust the authority flow rates to facilitate personalized authority flow searching. We experimentally evaluate our algorithms in terms of performance and quality.
展开▼
