A Query Suggestion Method Based on Random Walk and Topic Concepts

Related query suggestion is very important for search engines. Users could find required information more quickly and accurately with the help of query suggestions, which could greatly improve users' search experience. Thus, query suggestion technology has become a research hotspot in the field of the search engine. Most of existing methods focused on the query log data to mine related queries. However, some of the query log data exist relatively sparse characteristics and have some interferential noise data. Besides, the method that only focus on query log trend to fail to consider the user's initial query intention. These shortages would reduce the accuracy of the recommendation. Thus, this paper proposes a query suggestion method based on random walk and topic concepts (QuS-RWTC). The method is based on the query log data and suggestions from other mature search engines, which could make the suggestions more comprehensive and obtain a higher coverage. In addition, the paper further executes procedures of topic concepts to reorder the candidate queries, which make the suggestions more accurate, since they are more satisfied to the user's initial intention. The results prove the excellent performance of QuS-RWTC method compared with traditional methods and validate the importance of topic concepts.