Deriving Context Specific Information on the Web.
Christo Dichev, Darina Dicheva · 2001
Abstract : The Web is huge, unstructured and diverse in quality, which makes searching for information difficult. In practice, few of the documents returned by a search engine are valuable to a user. Which documents are valuable depends on the context of the query. Some adequate context information provided in addition to keywords can improve significantly search precision. In this paper we propose a framework for dynamic conceptual clustering of web documents based on clusters of users that share common interests. The basic assumption is that the search results would be more relevant to a user when provided within the context of semantically related documents marked as ‘interesting’ by a sufficiently large group of users with similar interests. This framework can support personalization of a search based on a search engine that ‘knows’ the context of the user information needs and uses it to tailor the search results. 1 Introduction The Web is huge and ubiquitous, unstructured, diverse in quality, dynamic and distributed, which makes searching for information principally difficult. General-purpose search engines that use keyword matching are notorious for returning too many matches of little relevance or quality in response to user queries. For example, if you submit the keyword “centroid” to Google almost 60,000 documents will be found. Which documents will be valuable to the user depends on the of the query. The context depends on a number