Learning from users' querying experience on intranets

Query recommendation is becoming a common feature of web search engines especially those for Intranets where the context is more restrictive. This is because of its utility for supporting users to find relevant information in less time by using the most suitable query terms. Selection of queries for recommendation is typically done by mining web documents or search logs of previous users. We propose the integration of these approaches by combining two models namely the concept hierarchy, typically built from an Intranet's documents, and the query flow graph, typically built from search logs. However, we build our concept hierarchy model from terms extracted from a subset (training set) of search logs since these are more representative of the user view of the domain than any concepts extracted from the collection. We then continually adapt the model by incorporating query refinements from another subset (test set) of the user search logs. This process implies learning from or reusing previous users' querying experience to recommend queries for a new but similar user query. The adaptation weights are extracted from a query flow graph built with the same logs. We evaluated our hybrid model using documents crawled from the Intranet of an academic institution and its search logs. The hybrid model was then compared to a concept hierarchy model and query flow graph built from the same collection and search logs respectively. We also tested various strategies for combining information in the search logs with respect to the frequency of clicked documents after query refinement. Our hybrid model significantly outperformed the concept hierarchy model and query flow graph when tested over two different periods of the academic year. We intend to further validate our experiments with documents and search logs from another institution and devise better strategies for selecting queries for recommendation from the hybrid model.