Increasing new knowledge or learning about new skills is often the main factor that triggers search users to conduct exploratory searches. Due to the lack of knowledge of their problem domain, it is difficult to form a learning path about new knowledge or new functions in the minds of searchers in the early stage of performing exploratory search, often searching for unnecessary information, resulting in wasting search time, the search is inefficient. However, the current search system does not provide enough support to solve this problem. For this reason, this paper designs a query path recommendation method supporting exploratory search based on a search goal shift graph. In the initial stage of exploratory search, directly recommends a set of query paths for searchers based on the searcher’s initial query, helping the searcher to find appropriate learning objects and build efficient learning paths, avoiding the search for irrelevant information, thereby shortening the search time and improving the search efficiency.

Similarity computation among queries is a central step of query recommendation based on click information in search log. In this step, weights of clicked URLs or clicked document terms, which may have a large influence on similarity computation results, are mostly counted based on co-occurrence. However, counting weights based on co-occurrence are unusually disturbed by irrelevant feedbacks in search log, which may decrease the precision of query similarity computation. This paper proposes a method that computes similarity among queries based on "Query - Clicked Sequence" model, which counts weight of clicked document term by density of documents containing this term on clicked sequence, and filters content of irrelevant documents during similarity computation. A series of experiment results show that this method can precisely count the weights of terms, and increase the precision of query similarity computation, accordingly increase the precision of query recommendation.