The Analysis of Web Search Snippets Displaying User's Knowledge

In recent years, due to the widespread use of the Internet, the number of opportunities to search the Web using search engines has been increasing. In conventional search engines, information retrieval is achieved by repeatedly entering a query and selecting and browsing each page in the search engine result pages (SERPs). The search engines present titles, snippets, and other information to help users select suitable Web pages. However, there are cases in which people view Web pages one by one due to lack of prior knowledge or failure of search strategies. To solve this problem, we present keywords from unvisited results in the SERPs, so that users can predict the content of the Web pages. We propose two kinds of feature words as extended snippets to be presented in each search result: a content word to indicate the central content of a Web page and known-topic and unknown-topic words to indicate the degree of knowledge that one would gain by browsing the Web page. The extraction of those is based on the clustering of words in snippet sentences using the distributed representation of the words and the clustering of words in the visited pages, respectively. We investigated the impact of the proposed extended snippet on user search behavior. The experimental findings indicate that our method was useful in certain types of search, as it decreased the time necessary to complete the search. Furthermore, the participants' ratings of the extended snippets were favorable, especially those of the unknown-topic words.