Finding Pertinent Page-Pairs from Web Search Results

Abstract

Conventional Web search engines evaluate each single page as a ranking unit. When the information a user wishes to have is distributed on multiple Web pages, it is difficult to find pertinent search results with these conventional engines. Furthermore, search result lists are hard to check and they do not tell us anything about the relationships between the searched Web pages. We often have to collect Web pages that reflect different viewpoints. Here, a collection of pages may be more pertinent as a search result item than a single Web page. In this paper, we propose the idea to realize the notion of “multiple viewpoint retrieval” in Web searches. Multiple viewpoint retrieval means searching Web pages that have been described from different viewpoints for a specific topic, gathering multiple collections of Web pages, ranking each collection as a search result and returning them as results. In this paper, we consider the case of page-pairs. We describe a feature-vector based approach to finding pertinent page-pairs. We also analyze the characteristics of page-pairs.