PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Baraglia R., Castillo C., Donato D., Nardini F. M., Perego R., Silvestri F. The effects of time on query flow graph-based models for query suggestion. In: RIAO 2010 - International Conference on Adaptivity, Personalization, and Fusion of Heterogeneous Information (Parigi, 28-30 Aprile 2010). Proceedings, article n. 10. CID, 2010.
 
 
Abstract
(English)
A recent query-log mining approach for query recommendation is based on Query Flow Graphs, a markov-chain representation of the query reformulation process followed by users of Web Search Engines trying to satisfy their information needs. In this paper we aim at extending this model by providing methods for dealing with evolving data. In fact, users' interests change over time, and the knowledge extracted from query logs may suffer an aging effect as new interesting topics appear. Starting from this observation validated experimentally, we introduce a novel algorithm for updating an existing query flow graph. The proposed solution allows the recommendation model to be kept always updated without reconstructing it from scratch every time, by incrementally merging efficiently the past and present data.
Subject Query Flow Graph
Query Suggestions
Topic Drift
Aging Effects
Effectiveness in Query Recommendation
H.2.8 Database Management. Database Applications
H.4.3 Communications Applications


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