Abstract: Entry page (home page) retrieval has the goal to retrieve just one right document, and the queries are usually short Web page names. As a result, finding precisely an entry page with a high initial is quite difficult. According to unigram language model, the authors extract the field of Web page contents for baseline retrieval, which are useful for finding Chinese entry page, and then we build a new model combined contentfield and noncontents features of Web pages (e.g. URLtype prior ,proved to have the strongest predictive power). According to the prior probabilities of URLtype, the relationship between entry page and its subpages is discovered. Based on the relationship, we propose a new algorithm that entry page is extracted from relevant subpages (PERS). At last, we get the result from rerank, and achieve a great advance on performance of entry page retrieval by using PERS.

Key words: information retrieval , URLtype priority, Entry page retrieval