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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-05022011-233235


Tipo di tesi
Tesi di dottorato di ricerca
Autore
NARDINI, FRANCO MARIA
URN
etd-05022011-233235
Titolo
Query Log Mining to Enhance User Experience in Search Engines
Settore scientifico disciplinare
ING-INF/05
Corso di studi
INGEGNERIA DELL'INFORMAZIONE
Relatori
tutor Prof. Simoncini, Luca
tutor Dott. Silvestri, Fabrizio
Parole chiave
  • effectiveness in Web search engines
  • query log mining
  • query recommendation
  • web information retrieval
  • web search results diversification
Data inizio appello
11/05/2011
Consultabilità
Completa
Riassunto
The Web is the biggest repository of documents humans have ever built. Even more, it is increasingly growing in size every day. Users rely on Web search engines (WSEs) for finding information on the Web. By submitting a textual query expressing their information need, WSE users obtain a list of documents that are highly relevant to the query. Moreover, WSEs tend to store such huge amount of users activities in "query logs". Query log mining is the set of techniques aiming at extracting valuable knowledge from query logs. This knowledge represents one of the most used ways of enhancing the users’ search experience. According to this vision, in this thesis we firstly prove that the knowledge extracted from query logs suffer aging effects and we thus propose a solution to this phenomenon. Secondly, we propose new algorithms for query recommendation that overcome the aging problem. Moreover, we study new query recommendation techniques for efficiently producing recommendations for rare queries. Finally, we study the problem of diversifying Web search engine results. We define a methodology based on the knowledge derived from query logs for detecting when and how query results need to be diversified and we develop an efficient algorithm for diversifying search results.
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