Query Recommendation by Modelling the Query-Flow Graph

Query recommendation has been widely applied in modern search engines to help users in their information seeking activities. Recently, the query-flow graph has shown its utility in query recommendation. However, there are two major problems in directly using query-flow graph for recommendation. On one hand, due to the sparsity of the graph, one may not well handle the recommendation for many dangling queries in the graph. On the other hand, without addressing the ambiguous intents in such an aggregated graph, one may generate recommendations either with multiple intents mixed together or dominated by certain intent. In this paper, we propose a novel mixture model that describes the generation of the query-flow graph. With this model, we can identify the hidden intents of queries from the graph. We then apply an intent-biased random walk over the graph for query recommendation. Empirical experiments are conducted based on real world query logs, and both the qualitative and quantitative results demonstrate the effectiveness of our approach.