An effective query recommendation approach using semantic strategies for intelligent information retrieval

With the explosive growth of web information, search engines have become the mainstream tools of information retrieval (IR). However, a notable problem emerged in the current IR systems is that the input queries are usually too short and too ambiguous to express their actual idea which largely affects the performance of IR systems. In this study, a novel query recommendation technology which suggests a list of related queries is proposed to resolve these problems. The query concepts can be firstly extracted from the web-snippets of the search result returned by the input query. A bipartite graph is subsequently built to identify the related queries, and the query similarity can be calculated by such bipartite graph. Moreover, by analyzing the URLs clicked by users, we find that some tokens appeared in URLs are very meaningful, especial for some typical topic-based pages. Therefore, these potential tokens which can provide a brief description from the subject of the URL are also considered. In order to reveal the real semantics between queries, the approach TF-IQF model is further discussed, and three features of a query, i.e. clicked documents, associated query and reversed query, are utilized in our approach in depth. Such a method could hopefully acquire the comprehensive idea of a query. To investigate how these three features could be used effectively for query recommendation in search engine, we adopt the benchmark evaluation criterions in our experiments, and the experimental results show its promising results in comparison with state of the art methods.