Searching the Web: learning based techniques

Searching and retrieving information from the Web poses new issues that can be effectively tackled by applying machine learning techniques. In particular, the fast dynamics of the information available on the Internet requires new approaches for indexing; the huge amount of available data is hardly manageable by humans and, on the other hand, can provide large sets of examples for learning algorithms; finally, there is the need of new services like search tools optimized for a specific Web community or even for a single user. Thus, the main areas for the application of these methodologies are the classification of Web documents, the modeling of users' behavior, the personalization of search engines, the automatic extraction of information from Web pages, and the auto-organization of documents on the base of their contents. Many services used to access information on the Web, like Web directories, crawlers, search engines and recommender systems, can be improved by learning from data. This survey reports the main contributions in the application of learning-based techniques to Web searching.