Bipartite graph-based keyword query results recommendation

Relational database is widely used in people´s daily life and productions. Keyword search in relational database makes people search structured information from database as using search engine. So it has become a research hotspot. In recent years, there are a lot of research works about how to use historical query information to improve query efficiency. The existing works can be divided into two main aspects: query expansion and query recommendation. These two aspects have common feature, that is, they both use the historical words to improve query efficiency. If the results of historical query can be directly recommended to the current query, the query efficiency will be improved. But there are not any works using recommended model of recommended system to help keyword search. We have found that recommended model can be used to improve efficiency. We are the first one to use bipartite graph model, and propose query result recommendation algorithm, namely, Query_base algorithm and Distance_base algorithm. Finally this paper analyzes the factors which affect the efficiency of the algorithms, and verified the performance and higher accuracy of the recommendation algorithms by experiments.