Divided Pretreatment to Targets and Intentions for Query Recommendation
We propose a query recommendation method called “Divided Pretreatment to Targets and Intentions for Query Recommendation”, which concentrates on the structure, elements and composition of a query. Based on the recognition of query targets and query intentions by a classifying method, the clusters of query intentions are built following the clue of consistent and similar query targets. After that, query recommendations are generated by simple substitution of peer intentions. This method aims to explore a simple and efficient mechanism, which only analyzes and processes query itself and its internal attributes. The experiment demonstrates that, accuracy of “query targets” and “query intentions” recognition is 73.11%, while that of intention clustering reaches 55.67%. The p@1 value of query recommendation gets 57.83%.