Chinese Query Recommendation by Weighted SimRank
Bin Wang · Zhongwen xinxi xuebao · 2010
Query recommendation as an important technology used in search engines suggests relevant queries to help users to reformulate more accurate queries.Existing approaches of query suggestion compute query similarity based on direct matching of query properties.However,it is hard to find the semantic relevant queries that are related indirectly.In this paper,queries are modeled by a query relation graph where query similarity is computed using WSimRank,a revised algorithm based on SimRank.WSimRank takes the edge information and global structure of query relation graph into account so that it can find the latent semantic relations between queries.To reduce the high complexity of basic WSimRank w.r.t real large query relation graph,this paper changes the WSimRank into a state graph and optimized with dynamic programming and pruning.Experiments on large real search engine query logs show that WSimRank outperforms SimRank and other conventional approaches on query suggestion.The MAP of query suggestions generated by WSimRank achieves nearly 0.9.